M.: Exploiting disjointness axioms to improve semantic similarity measures

land Motivation: Representing a domain of knowledge has been tradi-tionally accomplished in biology through creating hierarchies of terms. Recently, the advances in description logics and the creation of expres-sive ontology languages such as OWL have stimulated the community to use axioms that express logical relationships other than class-subclass, for example disjointness. This is improving the coverage and validity of the knowledge contained in ontologies. However, current semantic tools still need to adapt to this more expressive information. In this paper, we propose a method to integrate disjointness axioms, which are being incorporated in real-world ontologies such as the Gene Ontology and the Chemical Entities of Biological Interest ontology, into semantic similarity, the measure that estimates the closeness in meaning between concepts. Results: We present a modification of the measure of shared informa-tion content, which extends the base measure to allow the incorporation of disjointness information. To evaluate our approach, we applied it to several randomly selected datasets extracted from the ChEBI ontology; in 93.8 % of these datasets, our measure performed better than the base measure of shared information content, supporting the idea that semantic similarity is indeed more accurate if it extends beyond the hierarchy of terms of the ontology. ∗To whom correspondence should be addressed