Extracting hyponymy of domain entity using Cascaded Conditional Random Fields

Abstract

Entity hyponymy is an important semantic relation to build the domain ontology or knowledge graphs. Traditional extraction methods of domain concepts hyponymy are limited to manual annotation or specific patterns. Aiming at this problem, this paper proposed a new method of extracting hypernym–hyponym relations of domain entity with the CCRFs (Cascaded Conditional Random Fields), i.e., a two-layer CRFs model is employed to learn the hyponymy of domain entity concept. The lower-level of the CCRFs model is used to model the words by considering the dependence of long distance among words and identify the domain entity concept, which need to be combined in order. The pairs of entity concept can be obtained on the basis of the definition template characteristics. Then label the semantic pairs of concepts in high-level model by integrating assemblage characteristics and hyponymy demonstratives in feature template, finally identify the hypernym–hyponym relations between domain entities. Experiments on real-world data sets demonstrate the performance of the proposed algorithms.