Attributes Selection in Chinese Ontology Acquisition with FCA

Abstract

An ontology can be seen as a hierarchical description of concepts in a specific domain. One of the key issues in ontology construction is the acquisition of ontology hierarchy. Manual construction of ontology by experts is time-consuming and costly, and timely update is also difficult. In automatic or semi-automatic methods, the general procedure of ontology construction is to obtain domain specific terms first and then acquire the relations among them. Terms can be obtained through terminology extraction. Relationship among terms can then be acquired to construct an appropriate ontology hierarchy. Formal Concept Analysis (FCA) is an effective tool for the acquisition and visualization of ontology structure. In using FCA to acquire ontology structure, some existing methods are inclined to use a single type of attributes such as verbs or nouns or adjectives in the context of a term. This paper investigates the use of context words including nouns, verbs, adjectives, and adverbs as well as their combination. Experiments on a general corpus and a domain specific corpus show that the combined attributes of content word types give the best performance. Contrary to the belief that verbs serve as the best single type of attributes, nouns are better to use as they have overall better average performance over the whole spectrum of threshold values. Another experiment is conducted to examine context words which are qualified as domain specific terms from a so-called Core Term List which is acquired separately. The Core Term List contains terms complying with some specific standards as qualified domain terms. Experiments show a comparable or even better performance of using the Core Term List as context than using dynamically acquired nouns. In fact the core Term List approach gives comparable result to the combined content words. Thus, simple domain knowledge acquired apriori can serve as a good alternative attribute set especially in resource limited cases.