%0 Journal Article %1 Cross2013861 %A Cross, Valerie %A Yu, Xinran %A Hu, Xueheng %D 2013 %J International Journal of Approximate Reasoning %K - <!-- <keyword Handled Not Tag id=#k0005#>Semantic similarity %P 861 - 875 %R http://dx.doi.org/10.1016/j.ijar.2013.03.003 %T Unifying ontological similarity measures: A theoretical and empirical investigation %U http://www.sciencedirect.com/science/article/pii/S0888613X13000637 %V 54 %X Abstract This paper theoretically and empirically investigates ontological similarity. Tversky’s parameterized ratio model of similarity 3 is shown as a unifying basis for many of the well-known ontological similarity measures. A new family of ontological similarity measures is proposed that allows parameterizing the characteristic set used to represent an ontological concept. The three subontologies of the prominent Gene Ontology (GO) are used in an empirical investigation of several ontological similarity measures. Another study using well known semantic similarity within two different anatomy ontologies, the \NCIT\ anatomy and the mouse anatomy, is also presented for comparison to several of the \GO\ results. A discussion of the correlation among the measures is presented as well as a comparison of the effects of two different methods of determining a concept’s information content, corpus-based and ontology-based.

@article{Cross2013861, abstract = {Abstract This paper theoretically and empirically investigates ontological similarity. Tversky’s parameterized ratio model of similarity [3] is shown as a unifying basis for many of the well-known ontological similarity measures. A new family of ontological similarity measures is proposed that allows parameterizing the characteristic set used to represent an ontological concept. The three subontologies of the prominent Gene Ontology (GO) are used in an empirical investigation of several ontological similarity measures. Another study using well known semantic similarity within two different anatomy ontologies, the \{NCIT\} anatomy and the mouse anatomy, is also presented for comparison to several of the \{GO\} results. A discussion of the correlation among the measures is presented as well as a comparison of the effects of two different methods of determining a concept’s information content, corpus-based and ontology-based. }, added-at = {2015-01-14T21:25:14.000+0100}, author = {Cross, Valerie and Yu, Xinran and Hu, Xueheng}, biburl = {https://www.bibsonomy.org/bibtex/217b11e2a1802225eb88547afaa557cdb/diverzulu}, doi = {http://dx.doi.org/10.1016/j.ijar.2013.03.003}, file = {:D\:\\ASUS-FINO\\Inkhognito_M\\David\\PhD\\Web Semântica\\Biomedicine Ontology\\Automated Acquisition\\2013-IJAR-Unifying ontological similarity measures - A theoretical and empirical investigation.pdf:PDF}, interhash = {9d475fde0c08f1bc8f80bcf878135ed3}, intrahash = {17b11e2a1802225eb88547afaa557cdb}, issn = {0888-613X}, journal = {International Journal of Approximate Reasoning }, keywords = {- <!-- <keyword Handled Not Tag id=#k0005#>Semantic similarity}, owner = {David}, pages = {861 - 875}, timestamp = {2015-01-14T21:25:14.000+0100}, title = {Unifying ontological similarity measures: A theoretical and empirical investigation }, url = {http://www.sciencedirect.com/science/article/pii/S0888613X13000637}, volume = 54, year = 2013 }