Hyponymy extraction of domain ontology concept based on ccrfs and hierarchy clustering

TL;DR

This paper introduces a novel method combining cascaded conditional random fields and hierarchy clustering to extract domain ontology concepts and their hyponymy relations from free text, improving efficiency in ontology learning.

Contribution

It proposes a new approach integrating CCRFs and hierarchy clustering for more accurate and efficient hyponymy extraction in domain ontologies.

Findings

The method effectively identifies simple and nested domain concepts.

It accurately extracts hyponymy relations between concepts.

Experimental results show improved efficiency over existing methods.

Abstract

Concept hierarchy is the backbone of ontology, and the concept hierarchy acquisition has been a hot topic in the field of ontology learning. this paper proposes a hyponymy extraction method of domain ontology concept based on cascaded conditional random field(CCRFs) and hierarchy clustering. It takes free text as extracting object, adopts CCRFs identifying the domain concepts. First the low layer of CCRFs is used to identify simple domain concept, then the results are sent to the high layer, in which the nesting concepts are recognized. Next we adopt hierarchy clustering to identify the hyponymy relation between domain ontology concepts. The experimental results demonstrate the proposed method is efficient.