In this article, Robert nicely covers the different aspects that upper level ontologies need to consider to prescribe a coherent view of the world for its adopters.

Here are some specific comments that need to be addressed:

1. Instead of using electron as an example, i would use something more concrete – like a  car.

2. Explain the notation for ” )1,2)”

3. Elaborate on abstract entities – why are these important, and give some examples.

Take a class such as “Molecular function” in the Gene Ontology (GO) (and it must be noted that the GO is not guilty of this ontological crime), then we might add a series of subclasses describing, for instance, enzyme functions, motility functions, binding functions, and so on.  The temptation is to have a class called either “Miscellaneous”, “Function unknown” or “other”.

The semantics of many languages used for representing ontologies can easily help us out. If the function we want is not in the ontology underneath “molecular function” or we do not know the function, all we have to do is label our gene product as having “Molecular function”. Our description says the gene product has a function, but does not say what it is. The semantics of this statement are as clear as saying a gene product has an unknown function.

Another problem with “Unknown”, “Other” or “Miscellaneous” is that the types of object in that category keep changing. As the unknown becomes known, then subsets of that category keep disappearing. So, we have no real idea of what “unknown” means.

Finally, from the point of view of realism, it can also be argued that “unknown function” does not exist anyway. Gene products may not have “unknown function” but functions which we do not yet know – which is a different thing altogether.

[Article authored by Robert Stevens and Duncan Hull]

Within the OBO community, a definition means a natural language or textual description of that class. The OBO Foundry principles advocate a particular style for these textual definitions of genus and differentia.

One can also have logical definition of a concept. Here, at least in the Web Ontology Language (OWL), definition has a much more precise meaning; that of necessity and sufficiency. A necessary condition is a condition that an instance must fulfill to be a member of that class. Meeting that condition is, however, not enough to recognise that an instance is a member of a class. For example, having an X chromosome is a necessary condition for being a member of the class human male – it is not however sufficient because members of the class human female also have an X chromosome.

A necessary and sufficient condition is one, that when fulfilled, is enough to recognise that an instance is a member of a class. For example, having a Y chromosome is a necessary and sufficient condition for being a member of the class human male.

Necessary and sufficient conditions are also called complete. Classes with only necessary conditions are called partial descriptions.

[Article authored by Robert Stevens and Duncan Hull]

Languages such as the Web Ontology Language (OWL) and OBO Format allow this separation quite easily.

Sometimes terminologies or vocabularies are called ontologies. Rather, an ontology, via its labels or terms, can deliver a vocabulary, but the ontology itself is not a vocabulary. The difference is that the concept (sic) becomes the first class citizen, not the words used to describe the concept.

Many ontologies formalise this distinction by using “semantic free” identifiers for the concept. It is this identifier that is used, for example, as the means of annotation. The Gene Ontology [2] has a set of rules for change which can be found on the gene ontology website. Many ontologies also have rules or naming conventions for their terms.

[Article authored by Robert Stevens and Duncan Hull]

References

1. Cimino, J. J. (1998). Desiderata for controlled medical vocabularies in the twenty-first century. Methods of information in medicine 37 (4-5), 394-403. pmid:9865037
2. Ashburner, M., C. A. Ball, J. A. Blake, D. Botstein, H. Butler, J. M. Cherry, A. P. Davis, K. Dolinski, S. S. Dwight, J. T. Eppig, M. A. Harris, D. P. Hill, L. Issel-Tarver, A. Kasarskis, S. Lewis, J. C. Matese, J. E. Richardson, M. Ringwald, G. M. Rubin, and G. Sherlock (2000, May). Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature Genetics 25 (1), 25-29. DOI:10.1038/75556

Author: Robert Hoehndorf, European Bioinformatics Institute

What is an upper level ontology?

An ontology is a shared conceptualization of a domain. Ontologies are used to specify the meaning of the terms in a vocabulary that is used within some domain. To represent the meaning of terms, ontologies contain categories. These are organized in an is-a hierarchy, the taxonomy. Some categories are more general than others with respect to the is-a hierarchy. When defining these general categories within a domain, it is often possible to introduce more general categories. For example, in an ontology of cell components, the most general category may be Cell component. When we want to define Cell component, we can introduce another, more general category, and provide distinguishing properties. For example, we can define Cell component as an Object which is part of some Cell. Object is the more general category here, and Cell component will be a sub-category of Object. We can then further define Object as an Entity which has spatial extension and is wholly present at one point of time.

The assumption behind upper-level ontologies is that, when this generalization is performed in ontologies of multiple domains, we will come up with a small set of categories that is the same in all these domains. Most domains will deal with objects, processes, properties, relations, space, time, roles, functions, categories, individuals or similar. An upper-level ontology is an ontology that defines and axiomatizes these most general categories.

There is considerable disagreement about what these general categories that are relevant in any domain are. There is even more debate about what the properties of these categories are. For example, what are the properties of time? Are there only time points, and a time interval is defined as the set of all time points between two distinct time points? Or are time intervals primitive and time points are derived from infinite sets of time intervals that meet? Are there atomic time intervals or is time continuous?

Many of these questions have been debated in philosophy for thousands of years. As a result, upper level ontologies often rely much more on philosophical theories and commitments to particular philosophical views than domain ontologies. Upper level ontologies also rely much more on axioms than on formal or natural language definitions, because it is often hard to define an upper level category using other categories that are uniformly understood. Instead, the way these categories interact with other categories becomes more important. Therefore, although it is often sufficient for many domain ontologies to define categories in natural language or through explicit definitions, rich axiom systems are necessary to establish the meaning of upper level categories.

A foundation of a domain ontology in an upper ontology consists at least of assignments of super-categories from the upper level ontology for all the categories of the domain ontology. Because the domain ontology will usually be structured in the form of a taxonomy, only few categories will have to be explicitly assigned a super-category from the upper level ontology. A more expressive method of foundation is the ontological reduction of a domain category to an upper level ontology, where domain categories are explicitly or implicitly defined using the categories of the upper level ontology.

Why use upper level ontologies?

The main application of upper level ontologies is to provide semantic interoperability of ontologies across multiple domains. Because upper level ontologies provide general concepts which are common to all domains, they can provide a common ontological foundation for domain ontologies.

For example, consider an ontology for physics with a category Electron, and another ontology in the manufacturing domain with a category Transporter. Both Electon and Transporter may be defined as a sub-category of Object. Yet, Object in an ontology of physics and Object in an ontology of manufacturing may have different properties, they may in fact be completely different things. For example, instance of Object in physics may always have a temporal extension, or their location may not be determinable at the same time as their momentum. In the manufacturing domain, objects may always have a price, they may always have two or more components, and so on. An upper level ontology provides well-defined primitives to make these conflicts explicit, and provide a common foundation for both. Electron could be classified as a sub-category of Process, while Transporter becomes a sub-category of Endurant in an ontology. Therefore, upper level ontologies help to make the ontological commitment of a vocabulary explicit.

Upper level ontologies provide restrictions on the categories they provide through axioms. These restrictions are inherited by the domain ontologies which are founded in the upper level ontologies. Consequently, upper level ontologies provide a means to verify domain ontologies with respect to a particular foundation in an upper level ontology. This is particularily useful when a new ontology is developed with the intention to semantically interoperate with an already existing ontology.

When applied in the ontology development process, upper level ontologies provide a means to verify basic ontological constraits. They can also be used to verify the compatibility of the developed ontology with other ontologies that are founded in the same upper level ontology. Consequently, they can provide a high-level compatibility and plausibility check for domain ontologies and their semantic integration.

Categories and individuals

A fundamental upper level distinction is one between individuals (or particulars) and ontological categories. Although there is considerable discussion about the nature of individuals in philosophy, the common definitions states that an individual is an entity that cannot be instantiated. A category can be instantiated. The relation between a category and its instances is the instance-of relation.

Most upper level ontologies focus on the kinds of individuals that are present in some domain. However, there are other entities that are relevant in several domains. In any knowledge representation task or in the process of ontology engineering, we use categories. Categories have definitions, a history, an intension, there are axioms pertaining to categories, authors and creators of categories, categories may be consistently defined or inconsistently defined, and so on. Based on these properties, there are different kinds of categories. Consequently, there are upper level ontology who distinguish at a very basic level between categories and individuals. The instances of a Category category will be ontological categories such as Dog, Electron, Red, Species, while the instances of an Individual category will be individuals: my spider Nero, the red of the apple I eat now, the 1999 Berlin Marathon. Some of the instances of Category will be sub-categories (via is-a) of Individual, such as Dog or Red.

Whether the upper level ontology provides general upper level categories for both categories and individuals, or only for individuals, is the first distinguishing feature between upper level ontologies.

Time and space

A fundamental component of most upper level ontologies is a theory of space and time. The basic distinctions are between time points and time intervals, as well as spatial points and spatial regions.

A simple model of time are the real numbers (or dense linear orders). The basic entities in an ontology of time based on real numbers are time points, which correspond to real numbers. Time intervals are derived by pairs of two real numbers. For example, the real number e can be considered a time point, and (e,10) a time interval. Such an ontology of time has difficulties when time intervals are divided. If we want to devide the interval (0,2) into two intervals of exactly the same length, we have two options: either (0,1) and )1,2), or (0,1( and (1,2). In each case, there is one time interval for which we cannot determine the first or the last time point that belong to the interval, because one time interval will be half-open. This is often counter-intuitive.

To solve this approach, temporal-based ontologies of time were proposed. In these ontologies, time intervals are considered to be primitives and time points derived. Time intervals can meet other time intervals: an interval I meets an interval J when I and J do not overlap and there is no interval between I and J, i.e., I ends at the same time that J starts. Time points are derived as sets of intervals that meet one interval. In interval-based ontologies of time, time intervals can be divided into exactly two halfs, and for each a start and end point can be constructed.

In addition to point-based and interval-based ontologies of time, mixed approaches are being developed. The ontology of Brentano-time uses two temporal categories: time intervals and time boundaries. In Brentano-time, time intervals are primitive and each time interval has exactly one left and exactly one right boundary. Time boundaries are dependent on time intervals, and two time boundaries can coincide. When two time boundaries coincide, they are at the same time. When a right boundary of an interval coincides with the left boundary of another interval, these intervals meet. If two left boundaries coincide, the intervals start at the same time and overlap in their beginning. If two right boundaries overlap, the intervals end at the same time and they overlap at their end. Dividing a time interval in two parts yields two intervals, both with left and right boundaries. The right boundary of the first interval coincides with the left boundary of the second, yet both boundaries are distinct entities. This allows referencing both the last point of the first and the first of the second interval, while both intervals are divided into exactly two halfs.

Space is usually similar to time. Ontologies that use the real numbers as a model of time use as a model for space. Using time intervals as basic entities of time goes together with using spatial regions as primitives and deriving planes, lines and points from those. Similar to Brentano-time, Brentano-space treats spatial regions as primitives, and spatial regions have two-, one- and zero-dimensional boundaries which can coincide. Similar to time, we can ask how to divide a spatial region into exactly two halfs and find similar solutions in the different ontologies of space.

The ontology of space and time in upper level ontologies is our second distinguishing feature.

Objects and Processes

Based on the ontology of space and time, different categories of individuals can be derived. When the ontology of time is based on time points as primitives, three-dimensional objects which are present at points in time will naturally be available in the ontology. Based on the definition of time intervals in such a model, processes can be introduced in which objects may participate. Objects at time points are called endurants or continuants. An endurant is an individual which is wholly present at each time point at which it exists, and it persists through time. Wholly present means to be present with all its parts. In particular, endurants have no temporal parts.

The main problem for endurants is their persistence through time. How, in what sense, is John F. Kennedy as a child the same person as John F. Kennedy before his death? What makes an endurant persist through time, while loosing and gaining parts and changing most of its properties? The solution to this problem is to assign identity conditions to an endurant, such that an endurant is considered to be the same endurant as long as it has a property which assigns identity it. These identity conditions do not have to be intrinsic to the endurant, but can be assigned to it within specific contexts. Therefore, it may be that two objects at two different time points are the same with respect to one identity condition, and distinct with respect to another.

Endurants conflate presence at time points and persistence through time. In particular, there is not an instance of an endurant, but always an instance of an endurant at some time point. Similarily, endurants have parts only at time points and properties only at time points.

An alternative to using endurants in an ontology which uses time points is to separate both aspects: persistence through time and whole presence at time points. In such a setting, two categories must be introduced: one for entities existing at time points, another to provide the identity criterion for persistence through time.

On the other hand, occurrants are entitites which have temporal parts, they unfold through time. In particular, processes are occurrants. Endurants may participate in occurrants.

Examples of endurants are my spider Nero, the red of the apple in front of me or the Eiffel Tower. Examples for occurrants are the World War, the 1999 Berlin marathon or the process of writing this blog post.

Ontologies that employ a theory of time based on time intervals will contain temporally extended objects as primitives, and need to derive objects at time points in some form. Some ontologies get by without temporally non-extended entities, in particular the General Process Theory (GPT). These are strictly four-dimensional ontologies as all entities in these ontologies are are temporally extended. Objects may be very small processes, properties are layers of processes, etc.

Ontologies using Brentano-time and Brentano-space are bi-categorical in a different sense than endurant-based ontologies. Endurants are wholly present at time points. In Brentano-time, some entities are wholly present at time boundaries. These entities are called presentials. Similar to the case for endurants, criteria must be established for persistence through time, using a persistant category which provides identity criteria for persistence through time. Because Brentano-time is based on time intervals, additional constraints are usually established to require that the presentials belonging to one persisting object (persistant) are embedded in a connected process.

One particular feature of ontologies based on Brentano-time is that it is possible to have two distinct presentials at coinciding time boundaries (at the same time) which are identical with respect to some persistant. One application of this feature is to divide processes in two parts and assign properties to the participants of the objects. For example, a ball thrown into the air will move upwards for some time, and downwards for another. In Brentano-time it is possible to find the first presential in the downward process, and the last presential in the upward process, and both exist at coinciding time boundaries, therefore the same time.

A further kind of entity included in some ontologies are abstract entities. Abstract entities are independent of space of time. This means that they either exist outside of space and time, or they exist at all times and everywhere.

Further distinctions

Further distinctions drawn by upper level ontologies pertain to existential or ontological dependence. An entity a is existentially dependent on another entity b, if, whenever a exists, necessarily, b exists. The important ontological problem with existential dependence it the formalization of necessarily. For example, according to the axioms of set theory, whenever a exists, so does the singleton set {a}. Therefore, a is existentially dependent on its singleton — a rather counter-intuitive assertion.

Ontological properties (or qualities) are often considered to be existentially dependent on their bearer: whenever a property exists, necessarily, so does a bearer of the property. Similarily, relations can be dependent on their relata, roles on their players or processes on their participants.

The major distinctions drawn in most upper level ontologies pertain to those: individuals vs. categories, theories of space and time, persistence through time, the relation between objects and processes and dependent vs. independent entities.

Implemented top-level ontologies

Basic Formal Ontology: The Basic Formal Ontology (BFO) is an ontology of non-abstract individuals that uses real numbers as its model of space and time, and includes two categories of endurants (called Continuants) and occurrents.

Descriptive Ontology for Linguistic and Cognitive Engineering: The Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) is an ontology of individuals, both abstract and concrete. DOLCE uses real numbers as its model of space and time, and includes endurants, occurrents and abstract individuals.

General Formal Ontology: The General Formal Ontology (GFO) is an ontology of categories and individuals. It uses Brentano-time and Brentano-space and is a four-dimensional ontology. It includes processes, presentials and abstract individuals, and additionally contains a classification of ontological categories.

Further upper level ontologies include the Suggested Upper Merged Ontology (SUMO), the KR Ontology or the Cyc upper ontology.

Abstract

The is-a relation is often overused by ontology developers. Also, the is-a relation sometimes is confused with other relations. This article explains why it may happen and how to avoid that.

1. Introduction

Ontology developers tend to overuse the is-a (or subsumption) relation, and ontology development environments support this tendency. It is so easy in protégé to place a class which is related to another one as the subclass of that one. If the relation is different from the is-a, then it takes considerably more efforts to define how actually those classes are related.

2. The is-a relation

The importance of the is-a relation lies in the fact that is-a links between classes form a hierarchy (or in some cases, a lattice) of classes, a backbone of an ontology. The hierarchical organization of entities, virtually  always by is-a relations, enables inheritance of properties [Brachman, 1983]. The is-a relation can be easily confused with a number of other relations, i.e. instance-of, member-of, part-of [Brachman, 1983]. Taht is why it is important to strictly follow the agreed in the research community explicit definition of the semantic meaning of the is-a relation. A suit of of biomedical ontologies OBO uses the RO (Relations Ontology) defines this relation as follows:

For continuants:
C is-a C’ if and only if: given any c that instantiates C at a time t, c instantiates C’ at t.
For processes:
P is-a P’ if and only if: that given any p that instantiates P, then p instantiates P’.

The relation is transitive, reflexive, and anti-symmetric. This means that if A is-a B, and B is-a C holds; then A is-a C holds too. The relation A is-a A holds and if   A is-a B holds then B is-a A does not hold.

If this definition does not hold for the classes C and C’, than an ontology developer needs to identify by what other relation the classes C and C’ should be linked.

let us consider an example from MGED ontology (MO) v1.1.9 (note that MO has been significantly updated since v1.1.9):

‘growth condition’ (“a description of the conditions used to grow organisms or parts of the organism. This includes isolated environments such as cultures and open environments such as field studies.”) has subclass ‘water’ (“Water consumed by or enveloping the organism that the biosource is derived from”).

In this example, the is-a relation between the classes  ’growth condition’ and  ’water’ is used incorrectly. The class ‘water’ is not a sub-class of the class ‘growth condition’, because there are many instances of portions of water which are not used for growing. In fact, there is no any instance of a portion of water which is a  growth condition. However the is-a relation will be a valid one between the classes ‘growth condition’ and  ’water growth condition’ (“a description of the conditions used to grow organisms or parts of the organism where water is consumed by or enveloping the organism that the biosource is derived from”).

3. The part-of relation

The next after the is-a relation most used relation is the part-of relation. RO ( the relations ontology) defines this relation as follows:

For continuants:
C part-of C’ if and only if: given any c that instantiates C at a time t, there is some c’ such that c’ instantiates C’ at time t, and c *part-of* c’ at t.
For processes:
P part-of P’ if and only if: given any p that instantiates P at a time t, there is some p’ such that p’ instantiates P’ at time t, and p *part_of* p’ at t. (Here *part-of* is the instance-level part-relation.)

As with the is-a relation, the part-of relation is considered to be transitive, reflexive, and anti-symmetric.

For a number of applications such a definition of the part-of relation may be over-simplistic. GALEN defines has-grain relation [Rector et al., 2002], DOLCE (a Descriptive Ontology for Linguistic and Cognitive Engineering) distinguish five different parthood relations [Guarino et al., 1996], the Foundational Model of Anatomy (FMA) defines containment relations [Mejino et al., 2003]. Also, a parthood relation between i.e. physical objects has different semantic meaning compare with a  parthood relation between information content entities. Currently this distinction is not well defined, and the IAO (an ontology of information artefacts) project aims to provide a solution.

The relation part-of should not be confused with the relation member-of between an element and a set to which it belongs to (defined in IAO), and with the relation member-of-organization (defined in OBI, the ontology of biomedical investigations).

The is-a and part-of relations can be confused.

Novice ontology developers are often puzzled by how to correctly assert a part-of relation between classes, i.e. an arm is a part of a body. They may be puzzled by this, because firstly one needs to define both of the classes. It is not obvious a subclass of what class the class ‘arm’ is. A standard solution would be to define the classes ‘body’ and ‘body part’, where a body part can not exist by itself but needs a body to be a part of it. After the definition of the classes, it is strait-forward to assert an appropriate relation between them.

4. Other relations

An ontology can and should include a number of different relations, i.e. has-quality, has-participant, is-about. However, care should be taken that relations are logically defined and the assertion of the relations follows those definitions.

Sometimes it is hard to accurately define relations between classes. The basic rule is that if one can not clearly define the distinction between some entities, it maybe better not to distinguish them at all. For example, the class ‘material entity’ subsumes BFO (basic foundational ontology) derived ‘object’, ‘fiat object part’, and ‘object aggregate’, because the three level theory of granularity in BFO is inadequate for complex biological use cases.

5. Conclusion

Ontology developers should resist the attraction and ease of overusing is-a relations. RO and other ontologies provide a rich and powerful set of relations. The more relations are used in an ontology, the more types of queries are supported by such an ontology.

About the author

Larisa N. Soldatova is RC UK Fellow at the Computer Science Department, Aberystwyth University

References

Ron Brachman. What IS-A is and IS-A isn’t, Computer, 0018-9162: 30-36, 1983

N. Guarino, S. Pribbenow, andL. Vieu. Model-ingpartsandwholes. Data&KnowledgeEngineering,20(3):257–258,1996.

A. Rector, J. Rogers, A. Roberts, and C. Wroe. Scale and context: Issues in ontologies to link health and bio-informatics. In Proceedings of the AMIA 2002 Anual Symposium, pages 642–646, 2002.

J. L. V. Mejino, A. V. Agoncillo, K. L. Rickard, and C. Rosse. Representing complexity in part-whole relationships within the foundational model of anatomy. In Proceedings of the American Medical Informatics Association Fall Symposium, 2003.

Introduction

In this post, I want to address some of the issues raised by Sean Bechoffer in his thoughtful reflection. One of my aims with thinking about the knowledgeblog process was to come up with a form of publishing which is focused on the reader, reviewer and author, rather than the publisher. Clearly, Sean found it lacking in this respect and the process needs improving as a result.

Technology

I wanted to address what I think is the most answerable comment first.

As an aside, I found the Wordpress UI a nightmare to work with. I don’t really
like browser based editors, so prefer to author text using a text editor, and
then cut’n’paste into the web tool. Once I’d pasted text into the text box,
however, I found it messed with my underling HTML markup (adding lots of <br/>
elements and stripping all my <p/>’s out). Tables seemed problematic too,
although that may be something to do with the underlying style.

The short answer here is, yes, I agree. The management side of the Wordpress UI is, I think, generally, okay and reasonably easy to use. But the post editor is designed for people who want to write short, unstructured notes. I’ll talk more about Wiki’s later on, but I think that they have a similar problem.

I was quite keen with knowledgeblogging to separate out the process of editing from the process of publishing. Science is, I think, just too complex for a one-size-fits-all approach. People want to achieve different things and no tool stands out as being ideal. For collaborative editing, for example, Google Docs works really well. For structuring authoring, latex is excellent; with added maths, it is pretty much essential. Or consider my own technique, which uses asciidoc — some of my posts involve incorporating latex, Manchester syntax OWL and python; by using asciidoc, I can get it all syntax-highlighted, and can even incorporate directly from source which I can run. None of these solutions is ideal; authors need to be able to pick the one that benefits them the most.

HTML is a simple enough common-denominator which many tools can generate, and wordpress can (mostly) display. Of course, by allowing best-of-breed software, the knowledgeblog has problems; it’s never going to be as easy as an integrated solution. We need better documentation, enabling the use of these technologies.

I think that the solution here is to have a place to lodge articles describing different mechanisms for posting on knowledgeblog.org. My solution here is to use the knowledgeblog process; I plan to start “process.knowledgeblog.org”, describing how to use various aspects of the process, including different technologies and tools for knowledgeblogging.

Style of Content

Sean asks at several points about the style of articles that we were generating and whether the process was ideally suited to it.

1. Writing a number of short “encyclopedia style” articles relating to
   ontologies (and their use in bioinformatics).

2. Investigating new models for the publication process, in particular the use
   of a blog in order to manage the review process.

The rationale for A is clear, for B, the intention is to try and reduce some
of the overhead and time delay that can be present when using traditional
publishing routes. However, in my final analysis I think the difference
between the kinds of short article for an encyclopedia and longer scientific
papers means that the process hampered us somewhat in the production of our
initial articles

And later:

The reality is that what we were trying to do falls somewhere
between what’s offered by a blog and a wiki. A wiki may well have been a
better environment for supporting the collaborative writing and commenting
process for the encyclopedia.

Again, I think I largely agree with this. The process was designed to mimic the existing process of author/review/accept with some additional value coming from the blog software — referenced articles automatically get backlinked for example. The process normally happens asynchronously; different people do things at different times. There two key changes from the existing process are, I think, both good: first, review is public with the reviews forming part of the scientific record; second, there are no artificial deadlines coming from the publication process, so new articles can be published as they are ready.

To kick-start this, however, we needed content. Ideally, authors and reviewers would have generated content without a meeting; however, in practice, I thought this was less likely to happen; the number of articles which have been completed since the meeting seems to bear this out. The co-located meeting, however, did not ideally suit the process, as Sean suggests:

The fact that we were all co-located also meant that I wanted/expected quick
feedback — ”shouts across the room”.

I do not offer any solution to square this circle; the meeting was successful in generating content, but, by its nature, the process was not entirely suited to the meeting.

Wiki or Blog

Why aren't we doing this through a wiki?

Again, also a good question. In the environment of a meeting in a room then a wiki might well have been a better and an alternative solution for hosting ontogenesis. However, in the end, I don’t think that this is the right approach.

Firstly, the big issue is one of credit. It’s an issue of critical importance; scientists need credit for their work; we have to see our name attached to our words or, in time, we will be out of business. Now I may have opinions on whether this is a good thing or a bad thing, but it’s not really relevant; it is part of the way that the world is and we need the process to fit to this reality, not the other way around. A blog provides this; even with a review process, it is the authors post and they retain the credit. Or the blame; which leads to the second issue.

Wikipedia is a good example of what you can do with a wiki; like most academics (and everyone else!), I find it to be a tremendous resource and use it regularly. One thing, however, it is fairly poor at is reflecting differing opinions; as a minor example take, for instance, this post on LSIDs. This article spends more time describing what is wrong with LSIDs than describing what they are. In many ways two articles would be better, reflecting the opinions of those involved; consider the more extreme example of the global warming articles in wikipedia and conservapedia. Wikis designed for multi-author, collaborative generation of content; blogs are designed for single (or a few) author content, with interactions between the different authors. They seem a better fit.

Of course, time will tell. For instance, Ontology Design Patterns, appears to be implementing a peer-review and evaluation process using a wiki; they’ve used a similar approach, except with semantics in URLs rather than in categories differentiating between article and reviews. But, then how much are they making use of collaborative features of the wiki?

Versioning

The final issue to address is the thorny one of versioning, as Sean says:

It becomes a new article once the semantics are largely enough to make it new;
don't think that there is a hard or fast line here. But, ultimately, I think
we need to address this with open versioning.

and later:

Versioning. What is the versioning strategy that is used? Are articles
edited “in place”, or should edits result in a new article? In which case,
should all edits result in new articles, or can we fix typos? Who then
decides what edits are “acceptable”?

My answer here, is that once accepted, it should be possible to update an article only for technical reasons; English corrections, small errors or for updating metadata (“this article has been outdated”). Now, when does an article become a new article; in the absolute sense, I think that this is a hard question, but my answer in this more specific case is, when it has changed enough. This sort of editorial policy is one that needs to develop over time, based on specific examples. I think, however, we do need better support to ensure integrity of the scientific record. For this purpose, I think we need to extend wordpress; the version history of each post is available in the database, and I think that it needs to be uncovered, for public consumption, or at least all versions since the article was made public. Of course, the current articles were not written with this in mind, but I think that future articles should be.

Conclusions

Some strong criticisms were raised about the process of knowledgeblogging; I think that these are mostly valid, but I think are addressable with some changes to the process, some extensions to wordpress and, above all, better communication with the authors about what is required.

Despite the short-comings of the process, in one short meeting, we did generate substantial content and this, in turn, has generated significant interest. In the last month (February) alone, Ontogenesis has had around 1000 page views, which is significant compared to the average scientific book. Of course, this success is mostly the success of the content and not the process; if the process is not tailored further to the needs of the authors, they will not continue to contribute.

The process has fulfilled one key need; it has provided publicity and an audience in a timely manner. For this reason, I think that our initial experiment with knowledgeblogging has been a success — limited, guarded and in need of improvement, but a success none the less. Hopefully, we can build on this for the future.

This article could be split quite neatly in two articles. One is an excellent article that begins about a third of the way through the full piece. It covers the technical aspects of ontology building: subsumption hierarchies; necessary vs necessary and sufficient conditions for class membership; disjointness; relations; upper ontologies and their usefulness in restricting the choice of appropriate relations. It draws heavily on upper ontologies developed by philosophers (at least some of them realists) and shows why they are useful. It concludes with a clear and strong case for why good ontologies are needed in the biosciences. I have no argument with this article.

The other is, to me at least, a rather confusing attempt to argue that ontologies consist of concepts, as opposed to statements about reality. I find these arguments difficult to square with some of the statements made in the rest of the article. However, I’m also not convinced that there is much difference between the author’s position and a realist stance. There argument hinges on the subtle issue of the reality of classes and they don’t make other arguments commonly made against a realist stance – for example, the ‘argument from intellectual modesty’ (Smith et al., 2006), or the belief that ontology terms should simply follow the use of terms in language. In fact, they clearly argue for ontology as a means to overcome the latter:

“Ontology should be distinguished from thesauri…”

“Human beings can give multiple labels to … categories. This habit of giving multiple labels to the same category and the same label to different categories (polysemy) leads to grave problems…”

Their argument begins with what strikes me as a cheap rhetorical trick designed to close down debate:

“The definition here will not suit a lot of people and upset many (especially use of the word “concept”); We make no apology for this situation, only noting that the argument can take up resources better used in helping biologists describe and use their data more effectively.”

If the authors think this discussion is a waste of resources, then why bother spending a few paragraphs making their case? I suspect that they do actually care about the argument because they worry about the implications of taking a realist stance. If so, it would have been interesting to hear some of those concerns (on the realist status of maths for example) made more explicit.

There is also a notable lack of reference to any sources of opposing argument. For those who wish to pursue this argument further, waste of time though it might be, some references to counter arguments would be good. Either of these references (or both) would do nicely:

Smith, et al., 2006. Towards a reference terminology for ontology research and development in the biomedical domain. Proceedings of KR-MED 2006

Smith, 2004. Beyond Concepts: Ontology as Reality Representation. Proceedings of FOIS 2004

Of the arguments against a realist stance, the weakest uses a straw man:

“… with a computer science ontology … there is less concern with a true account of reality as it is information that is being processed, not reality.”

Who could argue? Surely the question is whether the information being processed is making assertions about reality or not? The authors case would be stronger if this line were deleted.

The heart of their argument is stated here:

“As human beings, we put these objects into categories or classes. These categories are a description of that which is described in a body of data. The categories themselves are a human conception. We live in a world of objects, but the categories into which humans put them are merely a way of describing the world; they do not themselves exist.”

A perhaps pedantic point: are classes “described in a body of data”? I would have thought it more likely they are assertions about reality that are a reasonable scientific interpretation of a body of data. This confusion of data and its interpretation as assertions occurs consistently throughout the article.

More importantly, what might it mean to state that a class is real? Even the authors seem to agree that there is regularity in the universe, whether we observe it or not. For example, later in the article, they state that:

“Each instance of a ‘Helium’ object was not discovered in 1903; most helium atoms existed prior to that date, but humans discovered and labelled that category at that date.”

In 1903, humans discovered something that already existed: a class of atoms that share specific properties. Surely this means that a definition of the class ‘helium atom’ is making assertions about reality. Is this not different from some arbitrary class defined as including say: all helium atoms, horses, unicorns and two bedroom flats in North London?

This is not to say that there is only one true way to categorise any one object, or that there is a clean dividing line between classes we might be happy to define as Universals (Smith ), such as Helium atoms, and more contingent classes.

In the abstract, the authors argue that the debate over a realist vs a conceptual stance is a distraction that “… can take up resources better used in helping biologists describe and use their data more effectively.” Why might a realist stance be useful in helping scientists?

I believe that a realist stance is useful for ontologies made up of scientific assertions (for example, about chemistry, anatomy, or physiology), because it gives us a way to judge the quality of an ontology. If the ontology makes assertions that run counter to what we have good reason to believe is true, then it is misleading as a knowledge-base about science and its use in inference and grouping of annotations will produce results that we have good reason to believe are incorrect. Surely such an ontology would be bad – even when judged purely in practical terms.

Having said all this, I’m happy for this article to pass review for the Ontogenesis Knowledge Blog as long as the authors add references to opposing arguments. The authors may wish to consider taking into account my points with regard to the abstract and the apparent use of a straw-man argument. The article already provides an excellent introduction to the basic technical aspects of ontology building. With the addition of references to opposing arguments, the article and this review should provide a good starting point for those interested in exploring the realism vs conceptual(ism?) debate further.

Minor corrections:

foundary -> foundry

polysemy – missing initial bracket

License

This paper is an open access work distributed under the terms of the Creative Commons Attribution License 3.0, which permits unrestricted use, distribution, and reproduction in any medium, provided that the original author and source are attributed.

“Back to books: Researchers should be recognized for writing books to convey and develop science.”

References

1. Nature, Vol. 463, No. 7281. (03 February 2010), pp. 588-588. DOI:10.1038/463588a

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