  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.
