Abstracting and Generalising the FMA Ontology (ISMB Bio-Ont SIG 2009)

Eleni Mikroyannidi et al.

FMA is very large, but its complete use is time consuming. How can we make it smaller/more manageable without the loss of information? For instance, symmetric classes (left and right hand, foot, etc) are present a lot. So, instead of having 3 concepts (hand, l hand, r hand), just keep the hand concept and then use the “selector pattern” to add the information that hands can be left or right. This abstraction is followed by expansion to the original form, which can also fix apparent omissions at the same time.

This abstraction (which is the way to make it smaller) mechanism was applied to a subset of the FMA with many symmetries, and used the OWL version of FMA from Noy. The steps: user defines the symmetries and the ontology as input; creation of the selector hierachies; detection and abstraction of the symmetrical entities (this last step further includes: a number of preconditions are checked, and then rejected candidates for the pattern are reported in logs); common restrictions are moved in to the parent concept; symmetric concepts are collapsed.

The steps of the expansion algorithm are: detection of classes with existential restrictions referring to the Selector (e.g. has laterality some Laterality); then it creates new symmetrical sibling classes; then create the extistential restrictions of the symmetrical classes based on the restrictions of the parent class.

The FMA shrinks by up to 57% (in the subset they’ve used). In the expansion stage, most concepts are recreated, but there is some loss of restrictions when many symmetries are considered due to ommissions in the FMA. They need to extend the algorithm to reliably track all of the restrictions, especially when a concept refers to more than one symmetry.

FriendFeed discussion: http://ff.im/4wQlC

Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else’s. I’m happy to correct any errors you may spot – just let me know!

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