Learning to Integrate Web Taxonomies with Fine-Grained Relations: A Case Study Using Maximum Entropy Model

As web taxonomy integration is an emerging issue on the Internet, many research topics, such as personalization, web searches, and electronic markets, would benefit from further development of taxonomy integration techniques. The integration task is to transfer documents from a source web taxonomy to a target web taxonomy. In most current techniques, integration performance is enhanced by referring to the relations between corresponding categories in the source and target taxonomies. However, the techniques may not be effective, since the concepts of the corresponding categories may overlap partially. In this paper we present an effective approach for integrating taxonomies and alleviating the partial overlap problem by considering fine-grained relations using a Maximum Entropy Model. The experiment results show that the proposed approach improves the classification accuracy of taxonomies over previous approaches.