Taxonomy plays an important role in many applications by organizing domain knowledge into a hierarchy of is-a relations between terms. Previous works on the taxonomic relation identification from text corpora
lack in two aspects: 1) They do not consider the trustiness of individual source texts, which is important to filter out incorrect relations from unreliable sources.
2) They also do not consider collective evidence from synonyms and contrastive terms, where synonyms may provide additional supports to taxonomic relations, while contrastive terms may contradict
them. In this paper, we present a method of taxonomic relation identification that incorporates the trustiness of source texts
measured with such techniques as PageRank
and knowledge-based trust, and the
collective evidence of synonyms and contrastive
terms identified by linguistic pattern
matching and machine learning. The
experimental results show that the proposed
features can consistently improve
performance up to 4%-10% of F-measure.