Shadow Concepts Considered Harmful

In this post I will describe “Shadow Concepts“, and why they are problematic. In the next forthcoming post I will describe strategies for mitigating these problems, and for avoiding them altogether.

Ontologists love to tease apart the world in fractal ways, considering the many different aspects of concepts and terms we take for granted. This kind of ontological analysis can be extremely helpful in extracting precise statements from ambiguous natural language or fuzzy thinking. For example, when we make statements about a drug like aspirin, do we mean the chemical entity, as represented in CHEBI, or a drug formulation consisting of both active ingredient and filler, as found in DRON? Is aspirin a singular entity, existing in chemical structure space, or is aspirin a set or class whose instances are the quadrillions of actual molecule instances existing in physical-temporal space located inside leaves of willow trees and drug packets?

Aspirin and aspirin

It can be both illuminating and fun to tease apart both everyday concepts and specialized scientific terms in this way; ‘carving ontology at the joints’ as the ontologists say. Clear thinking and unambiguous communication is undoubtedly important. However, like a lot of ontological analysis, a little moderation goes a long way. This is especially true when it comes to translating this analysis into concrete ontological products intended to be used by non-ontologists. There is a dangerous path that leads to a kind of ontological Balkanization, with concepts fragmented across different disconnected branches of ontologies, or across different ontologies.

Ontological Balkanization: The same family of concepts can find themselves spread over the ontological globe, with poor connections between them

This is prevalent in, and as far as I am aware, restricted to the life science / biomedical ontology space (and related environmental/ecological). I am not sure if this is due to something about our domain or knowledge, or more a matter of practice of the groups operating in this space. If this blog post doesn’t describe your experience of ontologies, then it may be less useful for you, although I hope that it will help inoculate you from any future ontologization epidemics.

In this post I will give examples of problems caused by these shadow concepts. But first, I will start by telling an ancient parable.

The Parable of the Intrepid Climber of Mount Ontolympus

One day an intrepid climber sought to ascend to the peak of the forbidding Mount Ontolympus. However, the climber was ill-prepared, and when the temperature dropped precipitously, he found himself feeling sick, shivering and confused. On performing a temperature assay with a thermometer, he confirmed he was suffering from hypothermia. He cried out for help, comforted in the knowledge that Mount Ontolympus was home to many wandering ontologist-physicians.

On hearing his plaintive cries, an ontologist immediately came to assist. “I have hypothermia, help me” said the climber, knowing it was good to be clear and direct with ontologists. The ontologist smiled and said “you are in luck my friend, for hypothermia is a class in my ontology, where is it clearly a type of measurement. Specifically, a measurement of the temperature of a multicellular organism”. He grabbed the thermometer from the climber and crushed it beneath his feet, exclaiming “without a measurement device, there is no measurement, and thus no hypothermia instance”. The ontologist then triumphantly strode down the mountain, with a shout of “You’re welcome!”.

Seeing the obvious increasing distress of the climber, a second ontologist approached. “I hear you have hypothermia, good sir. You are in luck, for hypothermia is a class in my ontology, where it is clearly a type of information entity, specifically a ‘datum’ that is about a measurement of a temperature of a bodily core. To dispel this instance, you must destroy all hard drives in which the concretizations in which this generically dependent continuant are borne, thus vanquishing the datum”. A troubled look crossed the face of the second ontologists, before he continued: “hmm, neurophysical substrates can also be the bearer of such concretizations,… including the pattern of synaptic wiring in my own brain”. The second ontologist then proceeded to run towards the nearest cliff, which he threw himself from, with the final cry of “your welcome!” echoing throughout the valley below.

The climber was now in peril. A third ontologist approached. She informed the climber: “Sir, you have hypothermia, which is clearly a quality of decreased temperature that inheres in your bodily core. To eliminate this instance, we must increase your body temperature. Here, take this blanket and wrap yourself in it, and sup warm liquids from my flask, …”. But it was too late, the climber’s heart had stopped beating, he had succumbed to his hypothermia, and now lay dead at the third ontologist’s feet.

Upper ontologies lead to shadow concept proliferation

Like all parables, this isn’t intended to be taken literally, and to my knowledge over-ontologization has never led to physical harm beyond moderately increased blood pressure. But there is a core of truth here in how we ontologists are liable to confuse people by insisting on narrowing in on our favorite aspect of a concept, and to waste time chasing different ‘ontological shadows’ rather than focusing on the ‘core’ aspect that matters most to people. This is not to say that it’s not important to distinguish these entities – but there is a time, place, and methodology for doing this.

The Basic Formal Ontology (BFO) is a great tool for analyzing these different aspects of common concepts. It provides a set of upper level categories that subdivide the world roughly similar to Aristotle’s categories. BFO allows us to take something like the class of human hearts and examine the different ontological aspects entailed by the existence of a heart. This includes the site and spatial region at which the heart resides, the disposition of a heart (to pump blood, to make a thumping noise), the realization of those dispositions as processes, the life history of a heart, the representation of a heart such as in a drawing by Vesalius, and so on.

This is illustrated in the following diagram:

The different aspects of a heart, and how those aspects are related. The heart disposition inheres in the heart, which is realized in a heart process. The heart “occupies” the heart site, the heart history “occupies” the heart spatiotemporal region, which itself spatially projects onto the heart spatial region. Other aspects such as heart information are not shown. The concept of a heart could be considered core here, with other aspects shadowing them

BFO is a great tool for analyzing what we mean when we talk about hearts or genes or any other scientific term. Those that are philosophically inclined like to discuss the pros or cons of different upper ontology representations, and there is ongoing vigorous debate about some parts of the above diagram. But that’s not what I am interested in discussing today, and in some ways these debates are irrelevant.

The problem arises when the upper ontology goes beyond an analytic tool, and ontologists start building ontologies that follow this model too literally, creating a vast cross-product of terms that intersect scientific terms such as parts of hearts with shadow ontological aspects. The end result is a ragged lattice, sometimes Balkanized across ontologies, with conceptual drift between the components, confusing users and leading to inconsistency and maintenance tar pits.

Some specific areas where I have seen this problem manifest:

• Geological entities, such as islands (which can be conceived of as material entities in BFO terms), and various shadows such as the area occupied by the island (an immaterial entity).
• Ontologies which follow the OGMS model end up creating many shadow concepts for a disease D, including the disease process, the disease qua disposition, the disease diagnosis (a generically dependent continuant), a concretization of that diagnosis, and many more. For an example of what happens here, try searching CIDO for the different ‘aspects’ of COVID-19
• Biological information macromolecules or macromolecule regions (e.g. DNA regions) can have many aspects, from the material molecular entity itself, through to ‘abstract’ sequences (generically dependent continuants), representation of those sequences in computers (also generically dependent continuants, but concretized by bits and bytes on a hard disk rather than in the mind-independent molecule itself)
• Organismal traits or physical characteristics such as axillary temperature and their information shadows such as axillary temperature measurement datum.

Apologies for the ontological jargon in these descriptions – follow the links to get elucidation of these, but the specific jargon and its meaning isn’t really the point here (although the obfuscated nature of the terminology is a part of the problem).

Shadow concepts lead to conceptual drift and inconsistent hierarchies

Let’s say we have two branches in our ontology, one for material environmental entities such as tropical forests, and another for their immaterial shadows, e.g. tropical forest areas. Ostensibly this is to serve different use cases. An ecologist may be interested in the material entity and its causal influence. A geographer may be interested in the abstracted area as found on a map (I am skeptical about the perceived need for materializing this distinction as separate ontology terms, but I will return to this later).

Problems arise when these branches evolve separately. Let’s say that in response to a request from an ecologist we introduce a subclass ‘tropical broadleaf forest‘ in the material entity hierarchy, but as this didn’t come from a geographer we leave the parallel area hierarchy untouched. Immediately we have a point of confusion – is the omission of a shadow ‘tropical broadleaf forest area‘ class in the area hierarchy intentional, reflecting different use cases between ecologists and geographers, or is it unintentional, reflecting the fact that synchronizing ontology branches is usually poorly handled in ontologies? See The Open World Assumption Considered Harmful for more on this.

The problems worsen if a geographer requests a class ‘broadleaf forest area‘. This seems like it’s broader than our previously introduced ‘tropical broadleaf forest’ class but in fact is broader on a different axis.

Shadow concept hierarchies have a tendency to drift and become inconsistent. Note these two hierarchies are different ontological categories, so they cannot be related by is-a links

This is all very confusing for the typical user who does not care about fine-grained philosophical upper-ontology distinctions and is just looking for terms encompassing ‘tropical forests’. In one hierarchy they get tropical broadleaf forests, in another they don’t get anything relating to broadleaf forests.

This is a small simplified example, the actual confusion caused multiplies when hierarchies grow. Furthermore, other aspects of the hierarchies drift, such as textual definitions, and again it is hard to tell if these differences are intentional reflecting the different ontological nature of the two hierarchies, or if it is just drift.

Needless to say this can cause huge maintenance headaches for ontology developers. This is something we cannot afford, given how few resources and expertise we have for building these ontologies and keeping them up to date.

There are techniques for syncing hierarchies that I will return to later, but these are not cost-free, and have downsides.

Shadow concepts that span ontologies are especially problematic

Syncing shadow concepts within an ontology maintained by a single group is hard enough; problems are vastly exacerbated when the shadows are Balkanized across different ontologies, especially when these ontologies are maintained by different groups.

One example of this is the fragmentation of concepts between OBA (an ontology of biological traits or attributes) and OBI (an ontology biomedical investigations). OBA contains classes that are mostly compositions of some kind of attribute (temperature, height, mass, etc) and some organismal entity or process (e.g. armpit, eye, cell nucleus, apoptosis). OBA is built following the Rector Normalization Pattern using DOSDPs following the EQ pattern.

OBI contains classes representing core entities such as assays and investigations, as well as some datum shadow classes such as axillary temperature measurement datum, defined as A temperature measurement datum that is about an axilla.

Immediately we see here there is a problem from the perspective of ontology modularization. Modularization is already hard, especially when coordinated among many different ontology building groups with different objectives. But ostensibly in OBO we should have ‘orthogonal’ ontologies with clear scope. And on the one hand, we do have modularization and scope here: datums (as in datum classes, not actual data) arising from experimental investigations go in OBI, the core traits which those data are about go in OBA.

But from a maintenance and usability perspective this is really problematic. It is especially hard to synchronize the hierarchies and leverage work done by one group for another due to the fact the terms are in different ontologies with different ontology building processes and design perspectives. Users are frequently confused. Many users take a “pick and mix” approach to finding terms, ignoring ontological fine details and collecting terms that are ‘close enough’ (see How to select and request terms from ontologies). Rather than request a term ‘axillary temperature’ from OBA they may take the OBI term, and end up with a mixture of datum shadows and core concepts. This actually has multiple negative ramifications from multiple perspectives: reasoning, text mining, search, usability. It also goes against plain common sense – things that are alike should go in the same ontology, things that are unalike go in different ones.

Case Study: The Molecular Sequence Ontology

A classic shadow concept use case is distinguishing between the molecular aspect of genomic entities (genes, gene products, regulatory regions, introns, etc) and their sequence/information aspect. This is not idle philosophizing – it lies at the heart of eternal questions of ‘what is a gene‘. It is important for giving clear answers to questions like ‘how many genes in the human genome’.

I won’t go into the nuances here, interesting as they are. The important thing to know here was that the Sequence Ontology (SO) was originally formally ontologically uncommitted with regards to BFO. What this means is that neither the genomics use case driven developers of SO not the users particularly cared about the distinction between molecules and information. One of the main uses for SO is typing features in GFF files, querying genomics databases, and viewing sequences in genome browsers (this probably makes it the most widely instantiated biological ontology, with trillions of SO instances, compared with the billions in GO). Users are typically OK with a gene simultaneously being an abstract piece of information carried by DNA molecules, an actual molecular region, and a representation in a computer. But this led to a few complications when working with an ontology.

To rectify this, in 2011, I proposed the creation of a ‘molecular sequence ontology‘. I am quite proud of the paper and the work my colleagues and I did on this, but I take full responsibility for the part of this paper that proposed the ‘SOM’ (Sequence Ontology of Molecules, later MSO), which I now regard as a mistake. The basic idea is captured in Table 2 of the paper.

Table 2 of the paper introducing SOM.

the basic idea was to ‘split’ SO into two ontologies that would largely shadow each other, one branch encompassing information entities (the SO would be ‘recast’ as this branch), and another branch for the molecular entities (MSO).

A number of good papers came out of this effort:

Intelligently Designed Ontology Alignment: A Case Study from the Sequence Ontology

Michael Sinclair, Michael Bada, and Karen Eilbeck

ISMB Bio-ontologies track conference proceeding 2018

Efforts toward a More Consistent and Interoperable Sequence Ontology

Michael Bada and Karen Eilbeck. Conference Proceeding ICBO 2012

Toward a Richer Representation of Sequence Variation in the Sequence Ontology

Michael Bada and Karen Eilbeck. Annotation, Interpretation and Management of Mutations. A workshop at ECCB10.

These are definitely worth reading for understanding the motivation for MSO, the challenges for developing and synchronizing it, and improvements that were made. However, ultimately the challenges in syncing the ontologies proved greater than expected, and did not stack well against the benefits, and resources ran out before a number of the benefits could be realized.

At the Biocuration workshop in 2017 there was a birds of a feather session featuring the MSO developers plus a large cross-representation of the curators that use SO for annotating genomic data. The outcome was that not everyone saw the need for splitting into two ontologies, and many found the ontological motivation confusing. However, everyone saw the value of the ontology editing effort that had gone into MSO (which included many improvements orthogonal to the ontological recasting), and so the consensus was to fold in any changes made into MSO back into SO that did not pertain to the fine-grained MSO/SO distinction. Unfortunately this was quite challenging as the ontologies had diverged in different ways, and not all the changes have been folded back in.

I think there is a lesson there for us all — we should make sure we have buy-in from actual users and not just ontologists before making decisions about how to shape and recast our ontologies. “Ontological perfection” can seem a lofty goal, especially for newcomers to ontology development, but this has to be balanced against practical use cases. And crucially we should be careful not to underestimate development, maintenance and synchronization costs. It’s easy to ignore these especially when building something from scratch, but just like in software engineering, maintenance eats up the lions share of the cost of developing an ontology.

Strategies for managing and avoiding shadow concepts

In the next post I will provide some strategies for mitigating and avoiding these problems.

Where shadow concepts are unavoidable, templating systems like DOSDPs should be used to sync the hierarchies (ideally all aspects of the shadow, including lexical information, should mirror the core term). This is necessary but not sufficient. There needs to be extra rigor and various workflow and social coordination processes in place to ensure synchrony, especially for inter-ontology shadows. There are a lot of reasoning traps the unwary can fall into.

I will also review various conflation techniques including what I call the Schulzian transform. These attempt to “stitch together” fragmented classes back into intuitive concepts.

I will also review cases where I think materialization of certain classes of shadow are genuinely useful. These include things such as genes and their ‘reference’ proteins, genes and their functions, or infectious diseases like COVID-19 and the taxonomic classification of their agents like SARS-CoV-2. Nevertheless it can still be useful to have strategies to selectively conflate these.

But really these mitigation and conflation techniques should not be necessary most of the time: in most cases there is no need to create shadow hierarchies. I can’t emphasize this strongly enough. If you want to talk about a COVID diagnosis (which is undoubtedly a different ontological entity than the instance of the disease itself) then you don’t need to materialize a shadow of the whole disease hierarchy. Just use the core class! You can represent the aspect separately as ‘post-composition’. The same goes if you feel yourself wanting classes with the string “datum” appended at the end. Instead of making a semi-competing shadow ontology, work on the core ontology, and if you really really need to talk about the “X datum” rather than “X” itself (and I doubt you do), just do this via post-coordination. I will show examples in the next post, but in many ways this should just be common sense.

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OntoTip: Don’t over-specify OWL definitions

This is one post in a series of tips on ontology development, see the parent post for more details.

A common mistake is to over-specify an OWL definition (another post will be on under-specification). While not technically wrong, over-specification loses you reasoning power, limiting your ability to auto-classify your ontology. Formally, what I mean by over-specifying here is: stating more conditions than is required for correct entailments. 

One manifestation of this anti-pattern is the over-specified genus. (this is where I disagree with Seppala et al on S3.1.1, use the genus proximus, see previous post). I will use a contrived example here, although there are many real examples. GO contains a class ‘Schwann cell differentiation’, with an OWL definition referencing ‘Schwann cell’ from the cell ontology (CL).  I consider the logical definition to be neither over- nor under- specified:

‘Schwann cell differentiation’ EquivalentTo ‘cell differentiation’ and results-in-acquisition-of-features-of some ‘Schwann cell’

We also have a corresponding logical definition for the parent:

‘glial cell differentiation’ EquivalentTo ‘cell differentiation’ and results-in-acquisition-of-features-of some ‘glial cell’

The Cell Ontology (CL) contains the knowledge that Schwann cells are subtypes of glial cells, which allows us to infer that ‘Schwann cell differentiation’ is a subtype of ‘glial cell differentiation’. So far, so good (if you read the post on Normalization you should be nodding along). This definition does real work for us in the ontology: we infer the GO hierarchy based on the definition and classification of cells in CL. 

Now, imagine that in fact GO had an alternate OWL definition:

‘Schwann cell differentiation’ EquivalentTo ‘glial cell differentiation’ and results-in-acquisition-of-features-of some ‘Schwann cell’

This is not wrong, but is far less useful. We want to be able to infer the glial cell parentage, rather than assert it. Asserting it violates DRY (the Don’t Repeat Yourself principle) as we implicitly repeat the assertion about Schwann cells being glial cells in GO (when in fact the primary assertion belongs in CL). If one day the community decides that in fact that Schwann cells are not glial but in fact neurons (OK, so this example is not so realistic…), then we have to change this in two places. Having to change things in two places is definitely a bad thing.

I have seen this kind of genus-overspecification in a number of different ontologies; this can be a side-effect of the harmful misapplication of the single-inheritance principle (see ‘Single inheritance considered harmful’, a previous post). This can also arise from tooling limitations: the NCIT neoplasm hierarchy has a number of examples of this due to the tool they originally used for authoring definitions.

Another related over-specification is too many differentiae, which drastically limits the work a reasoner and your logical axioms can do for you. As a hypothetical example, imagine that we have a named cell type ‘hippocampal interneuron’, conventionally defined and used in the (trivial) sense of any interneuron whose soma is located in a hippocampus. Now let’s imagine that single-cell transcriptomics has shown that these cells always express genes A, B and C (OK, there are may nuances with integrating ontologies with single-cell data but let’s make some simplifying assumptions for now)/

It may be tempting to write a logical definition:

‘hippocampal interneuron’ EquivalentTo

• interneuron AND
• has-soma-location SOME hippocampus AND
• expresses some A AND
• expresses some B AND
• expresses some C

This is not wrong per se (at least in our hypothetical world where hippocampal neurons always express these), but the definition does less work for us. In particular, if we later include a cell type ‘hippocampus CA1 interneuron’ defined as any interneuron in the CA1 region of the hippocampus, we would like this to be classified under hippocampal neuron. However, this will not happen unless we redundantly state gene expression criteria for every class, violating DRY.

The correct thing to do here is to use what is sometimes called a ‘hidden General Class Inclusion (GCI) axiom’ which is just a fancy way of saying that SubClassOf (necessary conditions) can be mixed in with an equivalence axiom / logical definition:

‘hippocampal interneuron’ EquivalentTo interneuron AND has-soma-location SOME hippocampus

‘hippocampal interneuron’ SubClassOf expresses some A

‘hippocampal interneuron’ SubClassOf expresses some B

‘hippocampal interneuron’ SubClassOf expresses some C

In a later post, I will return to the concept of an axiom doing ‘work’, and provide a more formal definition that can be used to evaluate logical definitions. However, even without a formal metric, the concept of ‘work’ is intuitive to people who have experience using OWL logical definitions to derive hierarchies. These people usually intuitively test things in the reasoner as they go along, rather than simply writing an OWL definition and hoping it will work.

Another sign that you may be overstating logical definitions is if they are for groups of similar classes, yet they do not fit into any design pattern template.

For example, in the above examples, the cell differentiation branch of GO fits into a standard pattern

cell differentiation and results-in-acquisition-of-features-of some C

where C is any cell type. The over-specified definition does not fit this pattern.

 

 

 

OntoTip: Write simple, concise, clear, operational textual definitions

This is a post in a series of tips on ontology development, see the parent post for more details.

Ontologies contain both textual definitions (aimed primarily at humans) and logical definitions (aimed primarily at machines). There is broad agreement that textual definitions are highly important (they are an OBO principle), and the utility of logical definitions has been shown for both ontology creation/maintenance (see previous post) as well as for analytic applications. However, there has been insufficient attention paid to the crafting of definitions, and to addressing questions such as how textual and logical definitions inter-relate, leading to a lot of inconsistent practice across OBO ontologies. 

text definitions are for consumption by biocurators and domain scientists, logical definitions for machines. Logical definition here shown in OWL Manchester syntax, with units written as human-readable labels in quotes. Note the correspondence between logical and textual definitions.

Two people who have thought deeply about this are Selja Seppälä and Alan Ruttenberg. They organized the  2016 International Workshop on Definitions in Ontologies (IWOOD 2016), and I will lift a quote directly from the website here:

Definitions of terms in ontologies serve a number of purposes. For example, logical definitions allow reasoners to assist in and verify classification, lessening the development burden and enabling expressive queries. Natural language (text) definitions allow humans to understand the meaning of classes, and can help ameliorate low inter-annotator agreement. Good definitions allow for non-experts and experts in adjacent disciplines to understand unfamiliar terms making it possible to confidently use terms from external ontologies, facilitating data integration. 

Despite the importance of definitions in ontologies, developers often have little if any training in writing definitions and axioms, as shown in Selja Seppälä and Alan Ruttenberg, Survey on defining practices in ontologies: Report, July 2013. This leads to varying definition practices and inconsistent definition quality. Worse, textual and logical definitions are often left out of ontologies altogether. 

I would also state that poorly constructed textual definitions can have severe long term ramifications. They can introduce cryptic ambiguities or misunderstandings that may not be uncovered for years, at which point they necessitate expensive ontology repair and re-curation efforts. My intent in this post is not to try and impose my own stylistic quirks on everyone else, but to improve the quality of engineering in ontologies, and to improve the lives of curators using definitions for their daily work.

There is an excellent follow-up paper Guidelines for writing definitions in ontologies by Seppälä, Smith, and Ruttenberg (henceforth referred to as the SRS paper), which should be required reading for anyone who is involved in building ontologies. The authors provide a series of guidelines based on their combined ontology development expertise and empirical work on surveying usage and attitudes.

While there is potentially an aspect of personal preference and stylistic preference in crafting text, I think that their guidelines are eminently sensible and deserve further exposure and adoption. I recommend reading the full paper. Here I will look at a subset of these, and give my own informal take on them. In their paper, SRS use a numbering system for their guidelines. I prefix their numbering system with S, and will go through them in a different order.

I have transcribed the guidelines to a table here, with the guidelines I discuss here in bold:

S1	Conform to conventions
S1.1	Harmonize definitions
S2	Principles of good practice
S3	Use the genus differentia form
S3.1	Include exactly one genus
S3.1.1	Use the genus proximus
S3.1.2	Avoid plurals
S3.1.3	Avoid conjunctions and disjunctions
S3.1.4	Avoid categorizers
S4	Avoid use/mention confusion
S5	Include necessary, and whenever possible, jointly sufficient conditions
S5.1	Avoid encyclopedia information
S5.2	Avoid negative terms
S5.3	Avoid definitions by extension
S6	Adjust the scope
S6.1	Definition should be neither too broad nor too narrow
S6.2	Define only one thing with a single textual definition
S7	Avoid circularity
S8	Include jointly satisfiable features
S9	Use appropriate degree of generality
S9.1	Avoid generalizing exprressions
S9.2	Avoid examples and lists
S9.3	Avoid indexical and dialectic terms
S9.4	Avoid subjective and evaluative statements
S10	Define abbreviations and acronyms
S11	Match text and logical definitions
S11.1	Proofread definitions

Concisely state necessary and sufficient conditions, cut the chit-chat

Listen to The Clash: cut the c**p

Combining S6.1 “A definition should be neither too broad nor too narrow” with S9.4 “avoid subjective and evaluative statements”, I would choose to emphasize that textual definitions should concisely encapsulate necessary and sufficient conditions, avoiding weasel words, irrelevant verbiage, chit-chat and random blethering. This makes it easier for a reader to hone in on the intended meaning of the class. It also encourages a standard style (S1), which can make it easier for others to write definitions when creating new classes. It also makes it easier to be consistent with the logical definition, when provided (S11; see below). 

SRS provide this example under S9.4:

cranberry bean: Also called shell bean or shellout, and known as borlotti bean in Italy, the cranberry bean has a large, knobby beige pod splotched with red. The beans inside are cream- colored with red streaks and have a delicious nutlike flavor. Cranberry beans must be shelled before cooking. Heat diminishes their beautiful red color. They’re available fresh in the summer and dried throughout the year (FOODON_03411186)

While this text contains potentially useful information, this is not a good operational definition, it lacks easy to apply objective criteria to determine what is and what is not a member of this class.

If you need to include discursive text, use either the definition gloss or a separate description field. The ‘gloss’ is the part of the text definition that comes after the first period/full-stop. A common practice in the GO is to recapitulate the definition of the differentia in the gloss. For example, the definition for ‘ectoderm development’ is

“The process whose specific outcome is the progression of the ectoderm over time, from its formation to the mature structure. In animal embryos, the ectoderm is the outer germ layer of the embryo, formed during gastrulation.”.

(embedded ‘ectoderm’ definition underlined)

This suffers some problems as it violates DRY (if the wording of the definition of ectoderm changes, then the wording of the definition of ‘ectoderm development’ changes). However, it provides utility as users do not have to traverse the elements of the OWL definition to achieve the bigger picture. It is marginally easier to semi-automatically update the gloss, compared to the situation where the redundant information permeates the core text definition. 

When the conventions for a particular ontology allow for gloss, it is important to be consistent about how this is used, and to include only necessary and sufficient conditions before the period. Recently in GO we were puzzling over what was included and excluded in the following definition:

“An apical plasma membrane part that forms a narrow enfolded luminal membrane channel, lined with numerous microvilli, that appears to extend into the cytoplasm of the cell. A specialized network of intracellular canaliculi is a characteristic feature of parietal cells of the gastric mucosa in vertebrates”

It is not clear if parietal cells are included as an exemplar, or if this is intended as a necessary condition. S5.1 “avoid encyclopedic information” is excellent advice. This recommends putting examples of usage in a dedicated field. Unfortunately the practice of including examples in definitions is common because many curation tools limit which fields are shown, and examples can help curators immensely. I would therefore compromise on this advice and say that IF examples are to be included in the definition field, THEN this MUST be included in the gloss (after the necessary and sufficient conditions, separated by a period), AND it should be clearly indicated as an example. GO uses the string “An example of this {process,component,…} is found in …” to indicate an example.

Genus-differentia definitions are your friend

(S3)

Genus-differentia definitions are your friend.

In the introduction, SRS define a ‘classic definition’ as one following genus-differentia style i.e. “a G that D”. The precise lexical structure can be modified for readability, but the important part is to state differentiating characteristics from a generic superclass. 

The example in the paper is the Uberon definition of skeletal ligament: “Dense regular connective tissue connecting two or more adjacent skeletal elements”. Here the genus is “dense regular connective tissue” (which should be the name of a superclass in the ontology; not necessarily the direct parent post-reasoning) and the differentiating characteristics are property of “connecting two or more adjacent skeletal elements” (which is also expressed via relationships in the ontology). As it happens, this definition violates one of the other principles as we should say later.

I agree enthusiastically with S3 “Use the genus-differentia form”. (Note that this should not be confused with elevation of single-inheritance as desired property in released ontologies; see this post)

The genus-differentia definition should be both necessary (i.e. the genus and the characteristics hold for all instances of the class) and sufficient (i.e. anything that satisfies the genus and characteristics must be an instance of the class).

Genus-differentia definitions encourage modularity and reuse. We can construct an ontology in a modular fashion, reusing simpler concepts to fashion more complex concepts.

Genus-differentia form is an excellent way to ensure definitions are operational. The set of all genus-differentia definitions form a decision tree, we can work up or down the tree to determine if an observation falls into an ontology class.

I also agree with S3.1 “include exactly one genus”. SRS give the example in OBI of

recombinant vector: “A recombinant vector is created by a recombinant vector cloning process”

which omits a genus (it could be argued that a more serious issue is the practice of defining an object in terms of its creation process rather than vice versa).

In fact, omission of a genus is often observed in logical definitions too, and is usually the result of an error, and will give unintended results in reasoning. I chose the following example from CLO (reported here):

http://purl.obolibrary.org/obo/CLO_0000266 immortal uterine cervix-derived cell line cell

• EquivalentTo: derived from anatomic part some uterine cervix

This is wrong because a reasoner will classify anything that comes from a cervix as being a cell line!

In a rare disagreement with SRS, I have a slight issue with S3.1.1 “use the genus proximus”, i.e. use the closest parent term, but I cover this in a future post. Using the closest parent can lead to redundancy and violations of DRY. 

Avoid indexicals (S9.3)

Quoting SRS’ wording for S9.3:

“Avoid indexical and deictic terms, such as ‘today’, ‘here’, and ‘this’ when they refer to (the context of ) the author of the definition or the resource itself. Such expressions often indicate the presence of a non-defining feature or a case of use/mention confusion. Most of the times, the definition can be edited and rephrased in a more general way”

Here is a bad disease definition for a fictional disease (adapted from a real example): “A recently discovered disease that affects the anterior diplodocus organ…”. Don’t write definitions like this. This is obviously bad as it will become outdated and your ontology will look sad. If the date of discovery is important, include an annotation assertion for date of discovery (or better yet, a field for originating publication, which entails a date). But it’s more likely this is unnecessary verbiage that detracts from the business of precisely communicating the meaning of the class (S9.4).

Conform to conventions (S1)

As well as following natural language conventions and conventions of the domain of the ontology, it’s good to follow conventions, if not across ontologies, at least within the same ontology.

Do not replicate the name of the class in the definition

An example is a hypothetical definition for ‘nucleus’

“A nucleus is a membrane-bounded organelle that … ”

This violates DRY and is not robust to changes in the name. Under S1.1 this is stated as “limiting the definition to the definiens”, alternatively states as “avoid including the definiendum and copula”.  If you really must include the name (definiendum), do this consistently throughout the ontology rather than ad-hoc. But I strongly recommend not to, and to start the text of the definition with the string “A <genus> that …”.

Here is another bad made-up definition for a fictional disease (based on real examples):

“Spattergroit (also known as contagious purple pustulitis) is a highly contagious disease caused by…”.

Including a synonym in the definition violates DRY, and will lead to inconsistency if the synonym becomes a class in its own right. Remember, we are not writing encyclopedic descriptions, but ontology definitions. Information such as synonyms can go in dedicated fields (where they can be used computationally, and presented appropriately to the user).

S11 Match Textual and Logical Definitions

The OWL definition (aka logical definition, aka equivalence axiom), when it exists, should correspond in some broad sense to the text definition. This does not mean that it should be a literal transcription of the OWL. On the contrary, you should always avoid strange computerese conventions in text intended for humans (this includes the use of IDs in text, connecting_words_with_underscoresOrCamelCase, use of odd characters, as well as strange unwieldy grammatical forms; see S1). It does mean that if your OWL definition veers wildly from your text then you have a bad smell you need to get rid of before visitors come around.

If your OWL definition doesn’t match your text definition, it is often a sign you are writing overly clever complex Boolean logic OWL definitions that don’t correspond to how domain scientists think about the class [covered in a future post]. Or maybe you are over-axiomatizing, and you should drop your equivalence axiom since on examination it’s not right (see the over-axiomatizing principle).

SRS provide one positive example, but no negative examples. The positive example is from IDO:

Positive example from IDO: bacteremia: An infection that has as part bacteria located in the blood. Matches the logical def of infection and (has_part some
(infectious agent and Bacteria and (located_in some blood)))

Unfortunately, there are many cases where text and logical definitions deviate. An example reported for OBI is oral administration:

“The administration of a substance into the mouth of an organism”

the text def above is considerably different from the logical one:

EquivalentTo (realizes some material to be added role) and (realizes some (target of material addition role and (role of some mouth)))

Use of DOSDPs can help here, as a standard textual definition form here can be generated for classes with OWL definitions. One thing that would be useful would be a tool that could help spot cases where the text definition and logical definition have veered widely.

Summary

I was able to write this post by cribbing from the SRS paper (Seppala et al) which I strongly recommend reading. Even if you don’t agree with everything in either the paper or my own take, I think it’s important if the ontology community discuss some of these and reach some kind of consensus on which principles to apply when.

Of course, there will always be an element of subjectivity and stylistic preference that will be harder to agree on. When making recommendations here there is the danger of being perceived as the ‘ontology police’. But I think there is a core set of common-sense principles that help with making ontologies more usable, consistent, and maintainable. My own experience strongly suggests that when this advice is not heeded, we end up with costly misannotation due to differing interpretations of terms, and many other issues.

I would like OBO to play more of a role in the process of coming up with these guidelines, and on evaluating their usage in existing ontologies. Stay tuned for more on this, and please provide feedback on what you think!

OntoTip: Learn the Rector Normalization technique

This is a post in a series of tips on ontology development, see the parent post for more details.

(Note there is an excellent introduction to this topic in the ontogenesis blog)

The 2003 paper Modularisation of Domain Ontologies Implemented in Description Logics and related formalisms including OWL by Alan Rector lays out in very clear terms a simple methodology for building and maintaining compositional ontologies in a modular and maintainable fashion. From the introduction, the paper “concentrates specifically on the engineering issues of robust modular implementation in logic based formalisms such as OWL”.

Anyone involved with the authoring of ontologies should read this paper, and should strive to build modular, normalized ontologies from the outset.

The motivation for the paper is the observation that when ontologies grow beyond a certain size become increasingly hard to maintain, because polyhierarchies (i.e hierarchies where classes can have more than one parent) become increasingly “tangled”, leading to errors and high maintenance cost. This observation was based on medical ontologies such as GALEN and SNOMED, but at the time the paper came out, this was already true for many OBO ontologies such as the GO, as well as various phenotype and trait ontologies. One property all these ontologies share is their ‘compositional nature’, where more complex concepts are built up from more basic ones. 

Figure: Example of difficult-to-maintain, tangled polyhierarchy, taken from the Drosophila anatomy ontology. Figure taken from OBO to OWL slides by David Osumi-Sutherland.

The methodology for “untangling” these is to decompose the domain ontology into  simpler (“primitive”) ontologies, which can then be recombined using logical definitions and other axioms, and to infer the polyhierarchy using reasoning. Note that for end-users the is-a structure of the ontology remains the same. However, for the ontology maintainers, maintenance cost is much lower. This is illustrated in the paper which demonstrates the methodology using an example chemical entity hierarchy, see figure below:

Rector 2003, Figure 1. Original tangled polyhierarchy on the left (multiple parents indicated with “^”). Normalized “primitive skeleton” trees in top left, logical axioms on bottom right. The 3 bars means “equivalent to”, these are logical definitions providing necessary and sufficient conditions. The arrows indicate subClassOf, i.e. necessary conditions. The original hierarchy can be entirely recreated using the skeleton taxonomies, domain axioms, through the use of a reasoner.

 

Rector calls this approach implementation normalization. The concept of database normalization should be familiar to anyone who has had to create or maintain relational database schemas (one example of how patterns from software and database engineering translate to construction of ontologies; see previous post). 

 

From the paper:

The fundamental goal of implementation normalisation is to achieve explicitness and modularity in the domain ontology in order to support re-use, maintainability and evolution. These goals are only possible if:

 

• The modules to be re-used can be identified and separated from the whole

• Maintenance can be split amongst authors who can work independently

• Modules can evolve independently and new modules be added with minimal side effects

• The differences between different categories of information are represented explicitly both for human authors’ understanding and for formal machine inference.

 

Rector describes five features of ontology languages that are needed to support normalized design:

 

• Primitive concepts described by necessary conditions

 

• Defined concepts defined by necessary & sufficient conditions

• Properties which relate concepts and can themselves be placed in a subsumption hierarchy.

• Restrictions constructed as quantified role -concept pairs, e.g. (restriction hasLocation someValuesFrom Leg) meaning “located in some leg”.

• Axioms which declare concepts either to be disjoint or to imply other concepts.

 

 

 

Some of the terms may seem unfamiliar due to terminological drift in the ontology world. Here ‘concepts’ are classes or terms, ‘necessary and sufficient conditions’ are sometimes called ‘logical definitions’, or equivalence axioms (represented using ‘intersection_of’ in obo syntax), ‘properties’ are relations (ObjectProperties in OWL), Quantified role-concept pairs are just simple relational class expressions (or simply “relationships” to those coming from OBO-Edit). 

The constructs described here are exactly the ones now used in ontologies such as the Gene Ontology and Phenotype Ontologies for construction of logical definitions (see 1, 2, 3). These ontologies have undergone (or are still undergoing) a process of ‘de-tangling’. Many ontologies now employ a prospective normalized development process, where classes are logically defined at the time of creation, and their placement inferred automatically. Examples include uPheno-compliant ontologies such as XPO, ZP, and PLANP. This has the advantage of requiring no retrospective de-tangling, thus saving on wasted effort.

In practice it’s rarely the case that we perfectly adhere to the normalization pattern. In particular, we rarely ‘bottom out’ at pure tree-based ontologies. Usually there is a chain of dependencies from more compositional to less compositional, with the terminal ontologies in the dependency tree being more tree-like. It should also be noted that the practice of normalization and avoidance of assertion of multiple is-a parents has sometimes been mistaken for a principle that multiple parents is bad. This misconception is addressed in a separate post. 

It is also considerably easier to do this than when Rector wrote this paper. Protege has seen numerous improvements. One game-changer was the advent of fast reasoners such as Elk which reasons over the EL subset of OWL (or a close enough approximation), which is sufficient to cover the constructs described in the Rector paper, and thus sufficient for basic normalization.

We also have a number of systems for creating normalized ontology classes using templates, where the template corresponds to a design pattern. These include Dead Simple OWL Design Patterns, ROBOT templates, TermGenie, and Tawny-OWL. These allow ontology developers to author logical definitions for classes without directly writing any OWL axioms. An ontologist defines the pattern in advance and the ontology developer can simply fill in ‘slots’.

Example template for GO biochemical process classes. From Hill et al (note some relations may have changed). Using a templating system, a curator needs only select the template (e.g. biosynthetic process) and a value for any template values (e.g. X=alanine, a class from ChEBI).

Where are we now?

The Rector 2003 paper ends with “we believe that if the potential of OWL and related DL based formalisms is to be realised, then such criteria for normalisation need to become well defined and their use routine”. Fast forward sixteen years to 2019, and the bio-ontology world is still engaged in a process of untangling ontologies. Although we have made good progress, some ontologies are still tangled, and many continue to assert hierarchies that could be inferred. Why? There may be a number of reasons for this, including:

It’s tempting to hack

It can be tempting to “hack” together an asserted hierarchy as opposed to constructing an ontology in a modular fashion. This is especially true for ontology developers who have not been trained in techniques like modular design.  We see this trade-off in software development all the time: the quick hack that grows to unmaintainable beast.

Retrospectively untangling gets harder the larger the ontology becomes. This is a good example of the  technical debt  concept from software engineering, outlined in the previous post. Even for well normalized ontologies, a tangled remnant remains, leading to ballooning of technical debt. 

We’re still not there with tools

Even where we have the tools, they are not universally used. ChEBI is one of the most widely used bio-ontologies, but it currently lacks logical definitions. This is in part because it is developed as a traditional database resource rather than an ontology. Curators used a specialized database interface that is optimized for things such as chemical structures, but lacks modern ontology engineering features such as authoring OWL axioms or integrating reasoning with curation.

Untangling is hard!

Untangling can be really hard. Sometimes the untangling involves hierarchies that have been conceptually baked in for centuries, along with apparent contradictions. For example, the classification of the second cranial nerve as a nerve, being part of the central nervous system, and the classification of nerves as part of the peripheral nervous system (see the example in this post). Trying to tease apart different nuances into a well-behaved normalized ontologies can be hard.

It should be noted that of course not everything in biology is amenable to this kind of normalization. A common mistake is over-stating logical definitions (there will be a future post on this). Still, there are no shortage of cases of named concepts in the life sciences represented in ontologies that are trivially compositional and amenable to normalization.

Sociotechnological issues confound the problem

In principle the Rector criteria that “maintenance can be split amongst authors who can work independently” is a good one, but can lead to sociotechnological issues. For example, it is often the case that the larger domain ontologies that are the subject of untangling receive more support and funding than the primitive skeleton ontologies. This is not surprising as the kinds of concepts required for curators to annotate biological data will often be more compositional, and this closer to the ‘tip’ of an ontology dependency tree. Domain ontology developers accustomed to moving fast and needing to satisfy term-hungry curators will get frustrated if their requested changes in dependent module ontologies are not acted on quickly, necessitating “patches” in the domain ontology.

Another important point is that the people developing the domain ontology are often different from the people developing the dependent ontology, and may have valid differences in perspective. Even among willing and well-funded groups, this can take considerable effort and expertise to work through.

For me the paradigmatic example of this was the effort required to align perspectives between GO and ChEBI such that ChEBI could be used as a ‘skeleton’ ontology in the Rector approach.  This is described in Dovetailing biology and chemistry: integrating the Gene Ontology with the ChEBI chemical ontology (Hill et al, 2013). 

For example, a nucleotide that contains a nucleobase, a sugar and at least one phosphate group would be described as a carbohydrate by a carbohydrate biochemist, who is primarily concerned with the reactivity of the carbohydrate moiety of the molecule, whereas general organic chemists might classify it as a phosphoric ester.

Both of the above classifications are correct chemically, but they can lead to incorrect inferences when extrapolated to the process hierarchy in the GO. Consider ‘nucleotide metabolic process’ (GO:0009117), ‘carbohydrate metabolic process’ (GO:0005975) and ‘phosphate metabolic process’ (GO:0006796) in the GO. If ‘nucleotide metabolic process’ (GO:0009117) were classified as both is _a‘carbohydrate metabolic process’ (GO:0005975) and is _a ‘phosphate metabolic process’ (GO:0006796) to parallel the structural hierarchy in ChEBI, then the process that results in the addition of a phosphate group to a nucleotide diphosphate would be misleadingly classified as is _a ‘carbohydrate metabolic process’ (GO:0005975). This is misleading because, since the carbohydrate portion of the nucleotide is not being metabolized, biologists would not typically consider this to be a carbohydrate metabolic process.

This situation was resolved by decreasing is-a overloading in ChEBI by the addition of the functional-parent relationship. But the process of understanding these nuanced differences and resolving them to everyone’s satisfaction can take considerable time and resources. Again, it is harder and more expensive to do this retrospectively, it’s always better to prospectively normalize.

Thankfully this was all resolved, and GO is able to leverage CHEBI as a skeleton ontology in the automatic classification of metabolic processes, as illustrated here:

 

This is one of the motivations for the creation and ongoing operation of the Open Bio-Ontologies Foundry (OBO). One of the motivating factors in the creation of OBO was the recognized need for modular ontology construction and the coordination of the different modules. For me, one of the inspirations for OBO was my involvement in BioPerl development (BioPerl was one of a family of bioinformatics libraries, including BioPython and BioJava). At that time, there was a growing recognition that bioinformatics software was facing a crisis due to unmaintainable code (everyone from that time remembers the “quick perl script” that became enshrined as monolithic infrastructure). The ‘Bio*’ projects attempted to follow best software engineering practice and to break complex code into maintainable modules. Often those modules would be developed by distinct people, but the BioPerl maintainers ensured that these worked together as a cohesive whole. 

Summary

• All ontology developers should familiarize themselves with the Rector 2003 paper
• The approach described is particularly useful for larger ontologies that have a high number of compositional classes to manage. Note the vast majority of ontologies in OBO have branches that are to some degree explicitly or implicitly compositional.
• The sooner you adopt this the better – retrospective normalization is harder than prospective.
• Although the approach described is independent of particular technology choice, the adoption of explicit design patterns and a system such as DOSDP-tools, ROBOT templates, or Tawny-OWL is recommended. 
• Sometimes the normalization approach involves identifying modules within your own ontology. Sometimes it requires use of an external ontology. This can pose challenges, but rather than give up without trying, use the OBO approach. File tickets on the external ontology tracker, communicate your requirements publicly (it is likely others have the same requirements).

OntoTip: Clearly document your design decisions

This is one post in a series of tips on ontology development, see the parent post for more details.

When building a bio-ontology, we frequently make design decisions regarding how we choose to model a particular aspect of the domain. Developing ontologies is not simply a matter of collecting terms and textual definitions, nor is it a matter of recording observations. It involves modeling decisions that reflect how we want to slice and dice the various different generalizations of biological phenomena. These modeling decisions frequently involve trade-offs between different use cases and other factors such as complexity of the ontology.  Sometimes these modeling decisions are made by individual ontology editors; sometimes they are made by a larger group, such as a committee or a content meeting combining domain experts and ontologists. Making these design decisions transparent is really important for making your ontology more usable, and more sustainable.

Model of a generalized eukaryotic cell. Bio-ontologists build models of biological entities and phenomena, such as the internal structure and components of a cell. This is guided both by the underlying ground-truth reality, and design decisions about how to carve up different parts, where to draw boundaries, and how best to generalize over variation in nature.

A note for people reading this from outside the OBO community: bio-ontologies are frequently large, involving thousands or tens of thousands of classes, with varying levels of axiomatization. They are commonly by biocurators for annotation of biological data, and entities such as genes or genomic features. Here ‘annotation’ means creating some kind of association between an entity of interest and an ontology class, typically with provenance and evidence information. These ontologies are usually built by biocurators with biology backgrounds, with broad knowledge of a particular domain. Some of the concerns may be different compared to some of the more ‘data model’ oriented ontologies found outside the life sciences and biomedicine, but some of the concerns may be the same.

Some examples of design decisions:

• For a biological process or pathway, deciding on the starts and ends of a process (which constrains what the parts of a process can be). For example, does a signaling pathway start with the binding between a receptor activity and a ligand? Does it end with the activity of a transcription factor?
• For brain regions, how should we draw the boundaries? E.g. does the hippocampus include the dentate gyrus? Do we include different classes to accommodate different groups’ boundary preferences (thus introducing complexity in both nomenclature and ontology structure) or choose to follow a particular standard or preferences of an individual group or researcher (potentially alienating or limiting the applicability of your ontology to these other groups)?
• How do we represent the relationship between the PNS and the CNS? Do we allow overlap, or do we model as spatially disjoint? There are distinct consequences of each choice that may not be clear from the outset.
• How should we represent structures such as a vertebra, which can exist in both cartilage form and bony form? (with variation potentially on an ontogenic/developmental axis, and potentially on a phylogenetic axis, e.g. sharks have cartilaginous skeletons). If we bake in assumptions drawn from fully formed humans (i.e. that vertebra is a subClassOf bone), this limits applicability to either developmental biology use cases, or comparative anatomy. In Uberon, we have an endochondral element design pattern, with a triad of structures: the composition-agnostic superclass, and  bony and cartilaginous subclasses. This ensures maximum applicability of the ontology, with annotators choosing the subclass that is appropriate to their organism/time stage. However it comes at some cost of nomenclature complexity, inflation of classes, and potential for annotators to accidentally select the wrong class
• How should the different subtypes of skeletal tissue be modeled, where divisions can be along a continuum rather than discrete groups? How should the different skeletal elements be related to the tissue that composes them? Should we have distinct classes for ‘bone tissue’ and ‘bone element’?
• How should environmental processes such as deforestation be linked to environmental physical entities such as forests? What relations should connect these, and what should the logical axioms for both look like?
• How do we handle chemical entities such as citric acid and citrate which are formally chemically distinct, yet may be interchangeable from a biological perspective? See Hill et al.
• Which upper ontology classes should be used (if any)? In order to represent the lumen of a subcellular organelle, do we model this as an immaterial entity (thus forcing this class to be in a different subclass hierarchy from the other parts, such as the membrane), or in the same material entity hierarchy? Some ontologies such as OGMS and IDO make use of a lot of different BFO classes, other disease ontologies use fewer (note there will be another post on this topic…)

example of endochondral pattern in uberon. The vertebra exists in up to 3 states: pre-cartilage, cartilage, and bone (with the latter absence in cartilaginous fish). A generic “vertebral element” class captures the vertebra in a composition-agnostic grouping. Subclasses are defined using OWL equivalence axioms, e.g. ‘vertebra cartilage element’ = ‘vertebral element’ and ‘composed primarily of’ some ‘cartilage tissue’

Whatever the ontology and whatever the design decision you and your fellow editors make, I can guarantee that someone will not like that decision; or more frequently, fail to understand it. This often results in confusion and annoyance in trying to use an ontology. Annotators may be presented with two similar-sounding classes, and may not know the background and nuanced reasons you modeled it that way. This can result in frustration, and in inconsistency in how the ontology is applied (with some annotators opting for class A, and some for class B). Sometimes this inconsistency is not noticed for years after, substantial resources have been devoted to annotation. The resulting corpus is far less useful because of this inconsistency in usage. This is something you want to take immediate prospective steps to avoid happening.

Documenting design decisions in an easy to comprehend way is also vital for maintainability of an ontology. Maybe you are lucky to have mad ontology skillz and have thought deeply and very hard about your domain, and have an elaborate internal model of how everything fits together, and you can easily slot terms into the perfect place in the ontology with ease. If this is the case, pause reading this for now and read up about the Bus Factor. This is a concept originally drawn from software engineering — basically, if you get hit by a bus, then no one will be able to carry on the development of your ontology since all the key knowledge is in your head. I should stress this is a metaphorical bus, there is no actual bus driving around mowing down ontologists (although some may find it tempting).

If you document all design decisions it makes it easier for people to come on board and make edits to the ontology in ways that don’t introduce incoherencies. It makes it easier for annotators to understand your intent, reducing frustration, and making it less likely that the ontology is applied inconsistently.

Note that when I am talking about documentation here, I mean documentation in addition to well-formed textual definitions. The topic of writing good text definitions is deserving of its own post, and indeed a future post in this series will be dedicated entirely to definitions. While including good definitions is a necessary condition of a well-documented ontology, it’s a mistake to assume it’s sufficient. This is particularly true for ontologies that incorporate a lot of nuanced fine-grained distinctions. While these can seem like intricate Swiss watches to the designers, they may resemble Rube-Goldberg contraptions to some users.

How an ontology looks to its designer

How an ontology sometimes looks to users

Hopefully this has convinced you (or you were already convinced). So how should you go about documenting these decisions?

There is no one correct way, but I will provide some of my own recommendations here. I should also note that ontologies I work on often fall short of some of these. I will provide examples, from various different ontologies.

Manage your ontology documentation as embedded or external documents as appropriate

Documentation can either be embedded in the ontology, or external.

Example of embedded and external documentation. Box surrounded by dotted lines denotes the OWL file. The OWL file contains (1) annotations on classes with URL values point to external docs (2) annotations on classes with human-readable text as values (3) documentation axiom on a class, where the axiom is annotated with a URL (4) design pattern YAML, lives outside OWL file, but managed as text file in GitHub. DP framework generates axioms which can be auto-annotated with documentation axioms (5) external documentation, e.g. on a wiki. This contains more detailed narrative formatted text, with images, figures, examples, etc.

Embedded documentation is documentation contained in the ontology itself, usually as annotation assertion axioms, such as textual definitions, rdfs:comments, etc. (Note here I am using “annotation” in the OWL sense, rather than the sense of annotating data and biological entities using ontologies).

Embedded documentation “follows the ontology around”, e.g. it is present when people download the OWL, it should be visible in ontology browsers (although not all browsers show all annotation properties).

Embedded documentation is somewhat analogous to inline documentation in software development, but a key difference is that ontologies are not encapsulated in the same way; inline documentation in software is typically only visible to developers, not users. (The analogy fits better when thinking about inline documentation for APIs that gets exposed to API users). It is possible for an ontology to include embedded documentation that is only visible to ontology developers, by including a step in the ontology release workflow for stripping out internal definitions. See the point below about eliminating jargon.

External documentation is documentation that exists outside the ontology. It may be managed alongside the ontology as text documents inside your GitHub repo, and version controlled (you are using version control for your ontology aren’t you? If not, stop now and go back to the first post in this series!). Alternatively, it may be managed outside the repo, as a google doc, or in an external wiki. If you are using google docs to manage your documentation, then standard google doc management practice applies: keep everything well-organized in a folder rather than headless; use meaningful document titles (e.g. don’t call a doc “meeting”). Make all your documentation world-readable, and allow comments from a broad section of your community. If you are using mediawiki then categories are very useful, especially if the ontology documentation forms a part of a larger corpus or project documentation. Another choice is systems like readthedocs or mkdocs. If for some unfathomable reason you want to use Word docs, then obviously you should be storing these in version control or in the cloud somewhere (it’s easier to edit Word docs via google docs now), not on your hard drive or in an email attachments. For external documentation I would recommend something where it is easy to provide a URL that takes you to the right section of the text. A Word doc is less suited to this.

You could also explore various minipublication mechanisms. You could publish design documents using Zenodo, and get a DOI for them. This has some nice features such as easy tracking of different versions of a document, and providing more explicit attribution than something like a google doc. Sometimes actual peer-reviewed manuscripts can help serve as documentation; for example, the Vertebrate Skeleton Anatomy Ontology paper was written after an ontology content meeting involving experts in comparative skeletal anatomy and expert ontologists. However, peer-reviewed manuscripts are hard to write (and often take a long time to get reviewed for ontology papers). Even less non-peer reviewed manuscripts can be more time-intensive to write than less formal documentation. Having a DOI is not essential, it’s more important to focus on the documentation content itself and not get too tied to mechanism.

I personally like using markdown format for narrative text. It is easy to manage under version control, it is easy for people to learn and edit, the natively rendering in GitHub is nice, it can be easily converted to formats like HTML using pandoc, and works in systems like readthedocs, as well as GitHub tickets. Having a standard format allows for easy portability of documentation. Whatever system you are using, avoid ‘vendor lockin’. It should be easy to migrate your documentation to a new system. We learned this the hard way when googlecode shut down – the wiki export capabilities turned out not to capture everything, which we only discovered later on.

One advantage of external docs is that they can be more easily authored in a dedicated documented authoring environment. If you are editing embedded documentation as long chunks of text using Protege, then you have limited support for formatting the text or embedding images, and there is no guarantee about how formatting will be rendered in different systems.

However, the decoupling of external docs from the ontology itself can lead to things getting out of sync and getting stale. Keeping things in sync can be a maintenance burden. There is no ideal solution to this but it is something to be aware of.

An important class of documentation is structured templated design pattern specification files, such as DOSDPs or ROBOT templates. This will be a topic of a future post. The DOSDP YAML file is an excellent place to include narrative text describing a pattern, and the rationale for that pattern (see for example the carcinoma DP documentation in Mondo). These could be considered embedded, with the design pattern being a “metaclass” in the ontology, but it’s probably easier to consider these as external documentation. (in the future we hope to have better tools for compiling a DP down into human-friendly markdown or HTML, stay tuned).

Another concept from software development is literate programming. Here the idea is that the code is embedded in narrative text/documentation, rather than vice versa. This can be applied to ontology development as this paper from Phil Lord and Jennifer Warrender demonstrates. I think this is an interesting idea, but it still remains hard to implement for ontologies that rely on a graphical ontology editing environment like Protege, rather than coding an ontology using a system like Tawny-OWL.

Provide clear links from sections of the ontology to relevant external documentation

When should documentation be inlined/embedded, and when should it be managed externally? There is no right answer, but as a rule of thumb I would keep embedded docs to a few sentences per unit of documentation, with anything larger being managed externally. With external docs it’s easier to use formatting, embed figures, etc. Wherever you choose to draw the line, it’s important to embed links to external documentation in the ontology. It’s all very well having reams of beautiful documentation, but if it’s hard to find, or it’s hard to navigate from the relevant part of the ontology to the appropriate section of the documentation, then it’s less likely to be read by the people who need to read it. Ideally everyone would RTFM in detail, but in practice you should assume that members of your audience are incredibly busy and thus appreciate being directed to portions that most concern them.

The URLs you choose to serve up external documentation from should ideally be permanent. Anecdotally, many URLs embedded in ontologies are now dead. You can use the OBO PURL system to mint PURLs for your documentation.

Links to external documentation can be embedded in the ontology using annotation properties such as rdfs:seeAlso

For example, the Uberon class for the appendicular skeleton has a seeAlso link to the Uberon wiki page on the appendages and the appendicular skeleton.

Add documentation to individual axioms where appropriate

As well as class-level annotation, individual axioms can be annotated with URLs, giving an additional level of granularity. This can be very useful, for example to show why a particular synonym was chosen, or why a particular part-of link is justified.

A useful pattern is annotating embedded documentation with a link to external documentation that provides more details.

Unfortunately not all browsers render annotations on axioms, but this is something that can hopefully be resolved soon.

A “legacy documentation pattern” that you will see in some ontologies like GO is to annotate an annotation assertion with a CURIE-style identifier that denotes a content meeting. For example, the class directional locomotion has its text definition axiom annotated with a dbxref “GOC:mtg_MIT_16mar07”. On all browsers like AmiGO, OLS, and OntoBee this shows up as a string “GOC:mtg_MIT_16mar07”. Obviously this is pretty impenetrable to the average user, and this should actually link to the the relevant wiki page. We are actively working to fix this!

Don’t wait: document prospectively

The best time to document is as you are editing the ontology (or even beforehand). Documenting retrospectively is harder.

And remember, good documentation is often a love letter to your future self.

Run ontology content meetings and clearly document key decisions

The Gene Ontology (GO) has a history of running face-to-face ontology content meetings, usually based around a particular biological topic. During these meetings ontology developers experienced with the GO, annotators, and subject matter experts get together to thrash out new areas of the ontology, or improve existing areas. Many other ontologies do this too — for example, see table 1 from the latest HPO NAR paper.

Organization	Location	Focus
Undiagnosed Diseases Network (UDN); Stanford Center for Inherited Cardiovascular Diseases (SCICD)	Stanford University, CA, USA (March 2017)	Cardiology
European Reference Network for Rare Eye Disease (ERN-EYE)	Mont Sainte-Odile, France (October 2017)	Ophthalmology
National Institute of Allergy and Infectious Disease (NIAID)	National Institutes of Health, Bethesda, MD, USA (May and July 2018)	Allergy and immunology
Neuro-MIG European network for brain malformations (www.neuro-mig.org)	St Julians, Malta; Lisbon, Portugal (February 2018; September 2018)	Malformations of cortical development (MCD)
European Society for Immunodeficiencies (ESID) and the European Reference network on rare primary immunodeficiency, autoinflammatory and autoimmune diseases (ERN-RITA)	Vienna Austria (September 2018)	Inborn errors of immunity.

Community workshops and collaborations aimed at HPO content expansion and refinement (from Köhler et al 2019).

One thing that is lacking is a shared set of guidelines across OBO for running a successful content meeting. One thing that is important is to take good notes, make summaries of these, and link this to the relevant areas of the ontology.

A situation you want to avoid is ten years down the line needing to refactor some crucial area of the ontology, and having some vague recollection that you modeled as X rather than Y because that was the preference of the experts, but having no documentation on exactly why they preferred things this way.

Don’t present the user with impenetrable jargon

It is easy for groups of ontology developers to lapse into jargon, whether it is domain-specific jargon, ontology-jargon, or jargon related to their ontology development processes (is there a jargon ontology?).

As an example of ontology jargon, see some classes from BFO such as generically dependent continuant.

b is a generically dependent continuant = Def. b is a continuant that g-depends_on one or more other entities. (axiom label in BFO2 Reference: [074-001]) [http://purl.obolibrary.org/obo/bfo/axiom/074-001 ]

Although BFO is intended to be hidden from average users, it frequently ‘leaks’ for example through ontology imports.

Jargon can be useful as an efficient way for experts to communicate, but as far as possible this should be minimized, with the intended audience clearly labeled, and supplemental documentation for average users.

Pay particular attention to documenting key abstractions (and simplify where possible)

Sometimes ontology developers like to introduce abstractions that give them the ability to introduce finer-grained distinctions necessary for some use case. An example is the endochondral element example introduced earlier. This can introduce complexity into an ontology, so it’s particular important that these abstractions are well-documented.

One thing that consistently causes confusion in users who are not steeped in a particular mindset is proliferation of similar-seeming classes under different BFO categories. For example, having classes for a disease-as-disposition, a disorder-as-material-entity, a disease-course-as-process, a disease-diagnosis-as-data-item, etc. You can’t assume your users have read the BFO manual. It’s really important to document both your rationale for introducing these duplicative classes, and provide easy to consume documentation about how to select the appropriate class for different purposes.

Or perhaps you don’t actually need all of those different upper level categories in your ontology at all? This is the subject of a future post…

Sometimes less is more

More is not necessarily better. If a user has to wade through philosophical musings in order to get to the heart of the matter, they are less likely to actually read the docs.

Additionally, creating too much documentation can create a maintenance burden for yourself. This is especially true if the same information is communicated in multiple different places in the documentation.

Achieving the right balance can be hard. If you are too concise then the danger is the user has insufficient context.

Perfection is the enemy of the good: something is better than nothing

Given enough resources, everything would be perfectly documented, and the documentation would always be in sync. However, this is not always achievable. Rather than holding off on making perfect documentation, it’s better to just put what you have out there.

Perhaps the current state of documentation is a google doc packed with unresolved comments in the margins, or a confusing GitHub ticket with lots of unthreaded comments. It’s important that this can be easily navigated to from relevant sections of the ontology, by at least other ontology developers. I would also advocate for inlining links to this documentation from inside the ontology; this can be clearly labeled as being links to internal documentation so as not to violate the no-jargon principle.

Overall it is both hard and time-consuming to write optimal documentation. When I look back at documentation I have written I often feel I haven’t done a great job, I use jargon too much, or crucial nuances are not well communicated. But we are still learning as a community what the best practices are here, and most of us are drastically under-resourced for ontology development, so all we can do is our best and hope to learn and improve.

OntoTip: Single-inheritance principle considered dangerous

This is one post in a series of tips on ontology development, see the parent post for more details.

A Porphyrian tree. With apologies to The Shamen, Ebeneezer Goode

The idea of classification using tree structures can be traced to the 3rd century CE and the Greek philosopher Porphyry’s Trees depicting Aristotle’s categories. The tree has enjoyed a special status despite the realization that nature can be classified along multiple axes, leading to polyhierarchies or Directed Acyclic Graphs (DAGs).

It is unfortunately still lore in some parts of that ontology community that Multiple Inheritance (MI) is bad and that Single Inheritance (SI) ontologies are somehow purer or better. This is dangerous advice, although there is a kernel of truth here. Unfortunately this kernel of truth has been misunderstood and miscommunicated, usually with bad results.

In fact, it is good ontology engineering practice to never assert MI, only infer it (see the forthcoming post on ‘Rector Normalization’). Following the Rector normalization methodology, the “primitive skeleton” should ideally form a tree, but the domain ontologies defined using these skeletons will be inferred polyhierarchies, with MI up the wazoo. This has no effect on the end-user who still consumes the ontology as a polyhierarchy, but has a huge benefit for the ontology maintainer. It should also be noted here that we are only talking about SubClass (aka is-a, aka subsumption) relationships here (see further on for notes on part-of).

Figure: Simplified example of Rector Normalization: two primitive ontologies combined into compositional classes yielding a polyhierarchy.

And additionally, it should be noted that it is also true that some ontologies do engage in IsA-overloading, which can lead to problems. The problem is that this kernel of truth has been miscommunicated, and some still cling to a purist notion of SI that is harmful. This miscommunication has resulted in ontologies that deliberately omit important links. Users are often unaware of this fact, and unaware that they are getting incomplete results.

Examples of problematic promulgations of SI

You may be reading this and are wondering why SI in ontologies would even be a thing, given that ever since the 2000 Gene Ontology paper (and probably before this) the notion of an ontology classification as a MI/DAG has been de rigueur. You may be thinking “why is he even wasting his time writing this post, it’s completely obvious”. If so, congratulations! Maybe you don’t need this article, but it may still be important for you to know this as a user, since some of the ontologies you use may have been infected with this advice. If you are part of the ontology community you have probably heard conflicting or confusing advice about SI. I hope to dispel that bad advice here. I wish I didn’t have to write this post but I have seen so much unnecessary confusion caused by this whole issue, I really want to put it to bed forever.

 

Here are some examples of what I consider confusing or conflicting advice:

 

1) This seminal paper from Schulze-Kremer and Smith has some excellent advice, but also includes the potentially dangerous:

Multiple inheritance should be carefully applied to make sure that the resulting subclasses really exist. Single inheritance is generally safer and easier to understand.

This is confusing advice. Of course, any axiom added to an ontology has to be applied carefully. It’s not clear to me that SI is easier to understand. Of course, maintaining a polyhierarchy is hard, but the advice here should be to infer the polyhierarchy.

 

2) The book Building Ontologies with BFO has what I consider conflicted and confusing advice. It starts well, with the recommended advice to not assert MI, but instead to infer it. It then talks about the principle of single inheritance. I hold that elevating SI to “principle” is potentially dangerous due to the likelihood of miscommunication. It lists 5 purported reasons for adhering to SI:

1. computational benefits [extremely dubious, computers can obviously handle graphs fine]
2. Genus-differentia definitions [I actually agree, see future post]
3. enforces discipline on ontology maintainers to select the “correct” parent [dubious, and talk of “enforcing discipline” is a red flag]
4. ease of combining ontologies [very dubious]
5. users find it easier to find the terms they require using an “official” SI monohierarchical version of the ontology [dubious/wrong. this confuses a UI issue with an ontology principle, and conflicts with existing practice].

3) The Foundational Model of Anatomy (FMA) is a venerable ontology in the life sciences, it’s FAQ contains a very direct statement advocating SI:

2) Why do the FMA authors use single inheritance?

The authors believe that single inheritance assures the true essence of a class on a given context.

I don’t understand what this means, and as I show in the example below, this adherence to the SI is to the detriment of users of the FMA.

4) The disease ontology HumanDO.obo file is single-inheritance, as is the official DO browser.

doid-non-classified.obo : DO’s single asserted is_a hierarchy (HumanDO.obo), this file does not contain logical definitions

Figure: official browser for DO is SI: lung carcinoma is classified as lung cancer, but no parentage to carcinoma. Users querying the SI version for carcinoma would not get genes associated with lung carcinoma. Most other browsers such as OLS and the Alliance disease pages show the MI version of DO, where this class correctly has two is-a parents.

The SI principle leads to massively incomplete query results; an example from the FMA

I will choose the FMA as an example of why the SI principle in its strict form is dangerous. The following figure shows the classification of proximal phalanx of middle finger (PPoMF) in FMA. The FMA’s provided is-a links are shown as black arrows. I have indicated the missing is-a relationship with a dashed red line.

The FMA is missing a relationship here. Of course, many ontologies have missing relationships, but in this case the missing relationship is by design. If the FMA were to add this relationship it would be in violation of one of its stated core principles. In elevating this purist SI principle, the FMA is less useful to users.  For example, if I want to classify skeletal phenotypes, and I have a phenotype involving the proximal phalanx of middle finger (PPoMF), and a user queries for proximal phalanx of [any] finger (PPoF), the user will not get the expected results. Unfortunately, there many cases of this in the FMA, since so many of the 70k+ classes are compositional in nature, and many FMA users may be unaware of these missing relationships. One of the fundamental use cases of ontologies is to be able to summarize data at different levels, and to discard this in the name of purity seems to me to be fundamentally wrongheaded. It is a simple mathematical fact that when you have compositional classes in an ontology, you logically have multiple inheritance.

This exemplifies a situation where SI is the most dangerous, when the following factors occur together: (1) the ontology is SI (2) the ontology has many compositional classes (there are over 70k classes in the FMA) (3) there are no logical definitions / Rector normalization, hence no hope of inferring the complete classification (4) The ontology is widely used.

The FMA is not the only ontology to do this. Others do this, or have confusing or contradictory principles around SI. I have chosen to highlight the FMA as it is a long established ontology, it is influential and often held up as an exemplar ontology by formal ontologists, and it is unambiguous in its promulgation of SI as a principle. It should be stressed that FMA is excellent in other regards, but is let down by allowing dubious philosophy to trump utility.

 

The advice we should be giving

The single most important advice we should be giving is that ontologies must be useful for users, and they must be complete, and correct. Any other philosophical or engineering principle or guideline that interferes with this must be thrown out.

We should also be giving advice on how to build ontologies that are easy to maintain, and have good practice to ensure completeness and correctness. One aspect of this is that asserting multiple is-a parents is to be avoided, these should be inferred. In fact this advice is largely subsumed within any kind of tutorial on building OWL ontologies using reasoning. Given this, ontologists should stop making pronouncements on SI or MI at all, as this is prone to misinterpretation. Instead the emphasis for ontology developers should be on good engineering practice.

TL;DR

multiple inheritance is fine. If you’re building ontologies add logical definitions to compositional classes, and use reasoning to infer superclasses.

OntoTip: Lift/Borrow/Steal Software Engineering Principles

This is one post in a series of tips on ontology development, see the parent post for more details.

The main premise of this piece is that ontology developers can learn from the experience of software engineers. Ontologists are fond of deriving principles based on abstract concepts or philosophical traditions, whereas more engineering-oriented principles such as those found in software engineering have been neglected, which is to our detriment.

Figure: An appreciation of engineering practice and in particular software development principles is often overlooked by ontologists.

 

In its decades-long history, software development has matured, encompassing practices such as modular design, version control, design patterns, unit testing, continuous integration, and a variety of methodologies , from waterfall top-down design through to extreme and agile development.  Many of these are relevant to ontology development, more than you might think. Even if a particular practice is not directly applicable, knowledge of it can help; thinking like a software engineer can be useful. For example, most good software engineers have internalized the DRY principle (Don’t Repeat Yourself), and will internally curse themselves if they end up duplicating chunks of code or logic as expedient hacks. They know that they are accumulating technical debt (for example, necessitating parallel updates in multiple places). The DRY principle and the DRY way of thinking should also permeate ontology development. Similarly, software developers cultivate a sense of ‘code smell’,  and will tell you if a piece of code has a ‘bad smell’ (naturally, this is always code written by someone else).

Don’t worry if you don’t have any experience programming, as the equivalent ontology engineering practices and intuitions can be learned through training, experience, the use of appropriate tools, and sharing experience with others. Unfortunately, tools are not yet as mature for ontology engineering as they are for software, but we are trying to address this with the Ontology Development Kit, an ongoing project to provide a framework for ordinary ontology developers to apply standard engineering principles and practice.

Computer scientists and software engineers are also fortunate in having a large body of literature covering their discipline in a holistic fashion; this includes classics such as The Mythical Man Month, The “Gang of Four” Design Patterns book, Martin Fowler’s blog and his book Refactoring. While there are good textbooks on ontologies these tend to be less engineering-focused, at best analogs of the (excellent, but sometimes theoretical) Structure and Interpretation of Computer Programs. Exceptions include the excellent, practical engineering-oriented ontogenesis blog.

An incomplete list of transferrable software concept, principles, and practice includes:

• Version control: use GitHub or equivalent (why? see the top answer here, just replace “code” with “ontology”)
• Use continuous integration (see here), social coding, non-private issue trackers
• Commit early, commit often
• Read about design patterns, and anti-patterns. Recognize and act on bad smells
• Avoid spaghetti logic
• Separate source from compiled product (see ROBOT workflows).
• Be use case and requirements driven. Usability of the ontology is key. Your goal is not to make a perfect ontology to be marveled at by ontologists, but to build something that is usable by people in your domain.
• At the same time, build for extensibility. Understand the concept of  technical debt.
• Test, test, test
• Learn debugging tools [see future blog post]
• Provide documentation for users
• Provide documentation for ontology developers (and your future self, you will thank your past self); especially your design patterns and design decisions. Inline documentation is your friend
• Think modular; reuse components from other ontologies, or from modules within your own ontology
• DRY: Don’t Repeat Yourself
• While good engineering is essential, the so-called soft skills are actually harder. Learn to acquire these.
• Ask for help early and often. And work to support inclusivity and a culture where people feel safe to ask questions.

 

I hold that all of the above are either directly transferrable or have strong analogies with ontology development. I hope to expand on many of these on this blog and other forums, and encourage others to do so.

Also, I can’t emphasize strongly enough that I’m not saying that engineering principles are more important than other attributes such as an understanding of a domain. Obviously an ontology constructed with either insufficient knowledge of the domain or inattention to users of the ontology will be rubbish. My point is just that a little time spent honing the skills and sensibilities described here can potentially go a very long way to improving sustainability and maintenance of ontologies.

OntoTips: A series of assorted ontology development guidelines

I am planning a series of blog posts describing some general tips I have found useful in working with groups developing biological ontologies. These are not intended to be rigid rules enforced by ontological high priests; they are intended to provide empirically backed  assistance to ontology developers of different levels of abilities, based on lessons learned in the trenches. I’m going to attempt to stay away from both hand-waving abstraction and platitudinous obvious truths, and focus on concrete practical examples of utility to ontology developers. I also hope to generate constructive and interesting discussion around some of the more controversial recommendations.

The following is a list of tips I intended to cover. I will add hyperlinks as I write each individual tip. I’m not sure when I will finish writing all articles so apologies for any teasers.

• OntoTip: Lift/Borrow/Steal Software Engineering Principles
• OntoTip: Single-inheritance principle considered dangerous
• OntoTip: Learn the Rector Normalization pattern
• OntoTip: Logical Axioms are your Friends
• OntoTip: Beware of Over-Axiomatization
• OntoTip: Write simple, concise clear text definitions that are consistent with the logical ones
• OntoTip: Clearly document your design decisions
• OntoTip: Don’t Over-specify OWL definitions
• OntoTip: Always make sure child terms have siblings
• OntoTip: Avoid Complex Boolean Constructs
• OntoTip: Embrace Simple Powerful Models of the World
• OntoTip: Shadow Concepts Considered Harmful 
• OntoTip: Model the World Directly
• OntoTip: Your ontology may be used in ways you had never imagined
• OntoTip: Communicate with developers of imported ontologies
• OntoTip: Undercommit to BFO, commit to domain upper ontologies