Improving web search result categorization using knowledge from web taxonomy

Finding relevant information from a long list of search results returned by general search engine can be difficult. The categorization technique is applied to solve this problem. One possible approach is by using some external resources such as Open Directory Project (ODP) to map search result´s URLs into the ODP categories. However, the ODP can only map some part of all URLs that returned from search engine. In this paper, we present a method of Web search result categorization based on classification technique by applying external information from the ODP. First, we categorize the search results by using information from the ODP as training data set. We then generate the categorizers from the training data based on centroid-based classification algorithm for categorized remaining uncategorized search results. The experimental result of proposed method achieved high performance of categorization comparing with an effective ODP classifier from previous work.