Monday, 4 February 2013

When will bio-ontologies grow up (and how will we know)?

Robert Stevens and I have published an experiment we did on evaluating levels of activity in bio-ontologies over the last decade. It felt like a decade ago since we did the work such was the delay by the journal in getting it out. Here's a summary of the full paper

At ICBO 2011 in Buffalo, NY, Robert Stevens and I were chatting outside my Hotel about which ontologies we use in our work and how one makes a choice. A few others there - I recall +Melanie Courtot and +Frank Gibson were also present - also had thoughts. There was a collective wisdom about ontology maturity, development and engineering in what we said and we felt it probably would change the landscape of this area of research forever. I wish I had been able to remember any of it.

Nevertheless, Robert and I went ahead and performed a bit of work looking at one aspect of ontology evaluation to see if we could glean some insights into the constitution of the various bio-ontologies in existence and see how far we had come. We limited our work to looking at what we called 'activity'. Some of the research questions we wanted to investigate were:

  • How frequently is an ontology updated?
  • What do these changes look like?
  • Who makes these changes?
  • Is there attribution for changes?
  • Can we see patterns (profiles) as ontologies mature?

"Ontology activity" by Aureja Jupp. 
Our method for doing this was relatively simple. Firstly, find the available ontology repositories. Secondly download the ontologies and record data about it - date, commiter etc. Thirdly, perform a syntactic diff between subsequent versions looking at number of classes added, deleted and that have add axiomatic changes (for example, have a new parent class, or part of assertion made on them). Finally, perform a bit of analysis on these results.

Activity and the Super-Ontologist

We performed the diff using a tool I had written several years ago now called Bubastis - there's also a Java library available since we did this work. The tool is fairly simple; it uses the OWL-API to read in OWL or OBO ontologies and performs a class-level set difference on axiom assertions. It also looks for newly declared named classes and similarly for named classes missing in previous versions.

I'm not going to go into everything here, you can read the paper for all the details, but here's a few of the interesting things we found.

1. Most activity in the community is in refining classes that already exist within an ontology. Alongside this, we also found that a lot of classes were deleted between versions which is in contrast to the perceived wisdom that classes are only made obsolete and not removed. It is arguable that for the OBO ontologies this is less of a crime; when we look at the details we can see some of these deletions are caused by name space changes between versions with the ID fragment at the end (e.g. the GO_1234567 bit) not changing. Nevertheless, this is a problem if one chooses to use OWL versions or use the full URIs for these ontologies.

2. Between 2009 and 2011 ontology activity remained fairly constant. We produced a metric by totalling all the changes and compared the two using a paired t-test which suggests that levels have not changed significantly.

3. Active ontologies tend to release often. This is perhaps not surprising to anyone familiar with software practices of releasing early and often, but was good to see. The perceived wisdom in software engineering is that this allows for rapid feedback from and response to users - something active ontologies have adopted.

4. Some ontologies may be dead. Dormancy may suggest the ontology is inactive or complete. The Xenopus anatomy ontology is currently inactive and is more likely to be a case of completeness rather than death. Nevertheless, monitoring when an ontology becomes moribund is almost certainly a worthwhile endeavor for efforts such as the OBO Foundry since an ontology could end up occupying an area, thereby preventing progress.

5. Lots of committers does not always lead to lots of activity. There are several factors to consider here. Firstly, a few of the project use automated bots to release the ontology so the committer name is not a good indicator of the number of editors. Nevertheless, there are some projects which contain many different committers which have low levels of activity and vice versa. This may be suggestive of many things - that large collaborate efforts suffer from "consensus paralysis" - but it may also suggest that tracking tacit contributions is difficult. Which raises other issues, namely of appropriate credit for such contributions. We arrived at more questions than answers with this one.

6. There lives amongst us a Super-Ontologist. The committer 'girlwithglasses' has made a total of 500 ontology revisions spanning 13 different ontology projects; She is truly the Ontological Eve.


We had many discussions when writing this work up as to what it all meant, and we've put most of this into the paper - if you're interested you'd be best of heading there. Perhaps, more than anything, the conclusion I was drawn to from all of this work was that ontology engineering is still immature. We leaned towards software engineering when we undertook this work; maturity profiles, diff analysis on code revisions, etc. but we were feeling our way towards what might be a good way of summarising this aspect of what you might call ontology quality. Software and ontologies are the same in many aspects but different in many others but I still think this is where our best hope lies in applying QC.

I've previously discussed why the lack of ontology engineering practices is a problem and I think we need more quantifiable approaches for developing and evaluating ontologies. In a workshop I attended in September one of the top desires of the biologist user community was advice on what ontologies they should use. I'm always tempted to name the ontologies I know and use. When we first started using ontologies we performed an analysis of coverage across the various ontologies to work out which would offer us most. Coverage is one metric - a simple but important one - but now there are so many ontologies which offer coverage, we need more than this to inform our decision. Our ontologies are growing up. We should probably help to point them in the right direction.

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