Building Topic Maps Using a Text Mining Approach

Abstract

Topic maps standard (ISO-13250) has been gradually recognized as an emerging standard for information exploration and knowledge organization in the web era. One advantage of topic maps is that they enable a user to navigate and access the documents he wants in an organized manner, rather than browsing through hyperlinks that are generally unstructured and often misleading. Nowadays, the topic maps are generally manually constructed by domain experts or users since the functionality and feasibility of automatically generated topic maps still remain unclear. In this work we propose a semi-automatic scheme to construct topic maps. We first apply a text mining process on a corpus of information resources to identify the topics and discover the relations among these topics. Necessary components of topic maps such as topics, topic types, topic occurrences, and topic associations may be automatically revealed by our method.