OntoLoki lives!

(Update June 8, 2012.  This paper was not accepted at first submission (see sad story), but can be accessed as Chapter 4 in my dissertation.  I've had enough interest in the concepts it contains that it is probably worth resubmitting it somewhere, someday, somehow...)


As noted in previous posts labeled with the tag OntoLoki, I've been working off and on for a few years now (yikes) on a program for automatic ontology evaluation.  Now, we are getting ready to submit our first paper on the subject and would like to open things up for comments.  I labeled it as a technical report in hopes of starting a tradition of such things in our laboratory.  It seems like a good way to keep the locals on track and have another chance for reviews before things go out into the scary world of official peer review.  I suppose I could drop this into Nature Preceedings again, but I'm tempted to wait until its gone through more revision cycles before I do so as that is likely to form a more permanent record than I am really ready to commit to I think.

Here is the longish abstract to whet your appetite.  I was thinking of blogging the rest of the document as distinct posts for each section - thoughts on that?  

As always I really appreciate any time you spend here and any ideas that you choose to share.

Abstract

Background: The delineation of clear, logical definitions for each class in an ontology and the consistent application of these definitions to the assignment of instances to classes are important criteria for ontology evaluation. If ontologies are specified with formal, property-based restrictions on class membership, then such consistency can be checked automatically using existing technology. If no such logical restrictions are applied however, as is the case with many current biological ontologies, there are currently no automated methods for measuring the semantic consistency of instance assignment on an ontology-wide scale, nor for inferring the patterns of properties that might define a particular class.

Objective: The aim of this study is to identify, implement, and test a new method for automatic, data-driven ontology evaluation that is suitable for the evaluation of ontologies with no formally defined restrictions on class membership. The method should quantify the consistency of instance classification within such an ontology based on patterns of properties found to be associated with the instances of particular classes.

Design: We constructed a program that takes as its input an OWL/RDF knowledge base containing an ontology, instances associated with each of the classes in the ontology, and properties of those instances. For each class, it outputs: 1) a rule for determining class membership based on the properties of the instances and 2) a quantitative score for the class that reflects the ability of the identified rule to correctly predict class membership for the instances in the knowledge base. To test the proposed method, we constructed a series of knowledge bases that varied from perfectly consistent through to completely random and evaluated each one using the implementation. In addition to this artificial control study, two other well-known biological ontologies were evaluated using public data to provide indications of the behavior of the system in realistic contexts.

Results: In the first experiment, the method produced direct quantitative assessments of the different versions of the knowledge bases that correlated directly with the known level of consistency of instance assignment for each knowledge base. The evaluations of the other ontologies indicated that the method was successful at detecting relevant patterns associated with the instances of classes in real biological ontologies based on publicly available data.

Conclusion: The results indicate that the suggested method can be used to conduct objective, automatic, data-driven evaluations of biological ontologies without formal class definitions in regards to the property-based consistency of instance-assignment. This inductive method complements existing, purely deductive approaches to automatic consistency checking, offering not just the potential to help in the ontology engineering process but also in the knowledge discovery process.

open kimono part 2

In a comment on open kimono part 1 "Luke" said that perhaps the question is more what the benefits of blogging are than the costs. As I'm pretty new to this scene, I think I will experiment a bit and tell you my results as they come in before going straight for my own predictions.

Experiment number 1 - Below is a problem I'm working on in the area of ontology quality evaluation. Lets see if blogging it out before trying to publish it / finish my research helps, hurts or doesn't matter..

Last year Barry Smith published an article indicating that the quality of a category in a terminology is best estimated through an inspection of the characteristics of the instances that get [manually] assigned to it. Basically, to evaluate a category (concept, class, set, whatever...) you try to find a pattern that is both consistent across the instances in the category and unique (the pattern is not present in instances not assigned to that category). Before any DL people start shouting - of course, if everyone wrote ontologies with logical restrictions on membership for each class, then we wouldn't need to worry about this as the desiderata would be enforced by reasoner, but, living in the world where the most powerful ontology developing force (OBO) is not just reticent, but seems actively opposed to the description logic approach, its still an important consideration. So..

A clever undergrad (Gavin Ha) and I have produced a program that:

1. reads in an OWL/RDF document including both classes and instances
   
2. builds a table with instances as rows and classes and other properties of the instances as the columns
   
3. prunes out unwanted columns (depending..)
   
4. sends the table off to alorithms from WEKA that attempt to build classifiers (e.g. decision trees) for a specified class within the ontology based on the attributes of the instances.
   

The idea being that, if we can find a pattern, the class is 'good', if not, then the class is 'less good' and we can quantify the goodness based on the number of instances the pattern correctly classifies.

So far, it works great on a positive control containing about 25 classes with 500 instances strictly obeying predetermined rules for class membership. It discovers those rules blinded and scores the classes appropriately (e.g. %correct for instances that should be in or out of the class). One of the nice things about this is that, depending on the classifier chosen, the rules discovered in the data can be automatically represented as OWL restrictions and thus given to ontology engineers as suggestions.

Now, of course, we are trying this out on bits of the gene ontology to see if there are any patterns that can be mined from the databases that correpond to GO classes. Trouble is, the GO is both large and, in many areas, relatively sparsely instantiated. The question that arises is, for a given class (term..) when we try to find a pattern than discriminates its members from those outside the class, how do we choose the negative set? In our test case, the negative set for any particular class was simply everything outside of it, but when the set in question contains only 10-100 things and everything else contains a million things this isn't really a good way to go.

Thinking of starting with only the closest neighbors for the class in question (kind of like the logic behind SVMs).

Any thoughts?