Abstract
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.
Highlights
The process of ontology population is the actively studied problem of adding new instances of concepts to the ontology
The solution for the ontology population task is interrelated with the elaboration of natural language processing (NLP) techniques applied in the process of information extraction (IE), with coreference resolution as one of the most challenging NLP tasks
Our main contribution in this paper is a formal framework for coreference resolution in the process of ontology population
Summary
The process of ontology population is the actively studied problem of adding new instances of concepts to the ontology. We use the proposed framework to improve our coreference resolution algorithm suggested in [6] for making the decision on the candidate admissibility, which is used in our general approach to text analysis and information extraction for populating subject domain ontology. Afterwards a semantic coreference algorithm runs on the RDF graph to revise the results of the textbased step: instances are merged if they belong to the same class in the domain ontology and their string similarity is higher than a predefined threshold These approaches to coreference resolution provide insufficient completeness, in particular, due to the poor use of the features of ontology classes and relations.
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