Hyponymy extraction of domain ontology concept based on ccrfs and hierarchy clustering
This addresses ontology learning for domain-specific applications, but appears incremental as it combines existing techniques.
The paper tackles the problem of automatically extracting concept hierarchies from free text for domain ontologies by combining cascaded conditional random fields (CCRFs) for concept identification with hierarchical clustering for hyponymy relation extraction, reporting that the proposed method is efficient.
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.