ArticleJournal of computational biology : a journal of computational molecular cell biology2014
Interaction relation ontology learning.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Ontology is widely used in semantic computing and reasoning, and various biomedicine ontologies have become institutionalized to make the heterogeneous knowledge computationally amenable. Relation words, especially verbs, play an important role when describing the interaction between biological entities in molecular function, biological process, and cellular component; however, comprehensive research and analysis are still lacking. In this article, we propose an automatic method to build interaction relation ontology by investigating relation verbs, analyzing the syntactic relation of PubMed abstracts to perform relation vocabulary expansion, and integrating WordNet into our method to construct the hierarchy of relation vocabulary. Five attributes are populated automatically for each word in interaction relation ontology. As a result, the interaction relation ontology is constructed; it contains a total of 963 words and covers the most relation words used in existing methods of proteins interaction relation.
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