Acquiring Topical Concept Network from Multiple Web Information Sources

Wikipedia provides conceptual description for specific entry and organizes these entries to form a concept category system. It has become a common information source for automatic knowledge acquisition. However, only relying on Wikipedia’s information is not enough for acquiring the relationships between the concepts, while such relationships are one of the important components of symbolic knowledge representation. Other kinds of information sources are needed for this purpose. Therefore, we propose an approach for acquiring the relationships between the concepts from multiple Web information sources. These concept relationships will form a topical concept network. This approach conducts the following steps. First, based on a provided concept, named as the topical term, it obtains a group of concepts and the links between them from the Wikipedia category system. The concept group is centered on the topical term by some kind of relevance. Secondly, it exploits the search engine for collecting the related Web information as references for discovering and verifying the relationships between the concepts in the concept group by integrating different well-established methods in the information retrieval and natural language processing fields. Finally, it produces a topical concept network, in which the nodes concepts obtained in the first step and the edges are the relationships obtained in the second step. The experiments have been conducted on several topical terms from different domains and the results shows the feasibility and the effectiveness of the proposed approach.