Comparing of Term Clustering Frameworks for Modular Ontology Learning
This work addresses ontology learning for domain-specific applications, but it is incremental as it compares existing clustering methods on new data.
The paper tackled the problem of building modular ontologies from domain-specific text using term clustering, finding that Affinity Propagation (AP) was more effective for co-occurred terms and Non-negative Matrix Factorization (NMF) encoding performed well in feature compression.
This paper aims to use term clustering to build a modular ontology according to core ontology from domain-specific text. The acquisition of semantic knowledge focuses on noun phrase appearing with the same syntactic roles in relation to a verb or its preposition combination in a sentence. The construction of this co-occurrence matrix from context helps to build feature space of noun phrases, which is then transformed to several encoding representations including feature selection and dimensionality reduction. In addition, the content has also been presented with the construction of word vectors. These representations are clustered respectively with K-Means and Affinity Propagation (AP) methods, which differentiate into the term clustering frameworks. Due to the randomness of K-Means, iteration efforts are adopted to find the optimal parameter. The frameworks are evaluated extensively where AP shows dominant effectiveness for co-occurred terms and NMF encoding technique is salient by its promising facilities in feature compression.