USING MULTIPLE SEMANTIC MEASURES FOR COREFERENCE RESOLUTION IN ONTOLOGY POPULATION

The problem of populating an ontology consists in adding to it some new, domain-specific content from an input expressed, in particular, in a natural language. We focus on an important aspect in the ontology population process – finding and resolving coreferences, i.e., similar mentions of entities in the input text. Our contribution is a novel formal framework that extends the state-of-the-art approaches to coreference resolution by using multiple semantic similarity properties in the resolution process, i.e., we extend the list of the ontological properties used for coreference resolution with additional properties such as inverse, symmetry, intersection, union, etc. We use the proposed framework to improve our previously proposed algorithm for coreference resolution used in our general approach to text analysis and information extraction for populating subject domain ontologies. We describe a multi-agent implementation of our information extraction system and we show that using additional semantic similarity measures for evaluating coreferential candidates improves the quality of the coreference resolution process, especially for complex objects whose coreferencing has not been yet studied in detail.