A search engine usually returns a long list of web search results corresponding to a query from the user. Users must spend a lot of time for browsing and navigating the search results for the relevant results. Many research works applied the text clustering techniques, called web search results clustering, to handle the problem. Unfortunately, search result document returned from search engine is a very short text. It is difficult to cluster related documents into the same group because a short document has low informative content. In this paper, we proposed a method to cluster the web search results with high clustering quality using graph-based clustering with concept which extract from the external knowledge source. The main idea is to expand the original search results with some related concept terms. We applied the Wikipedia as the external knowledge source for concept extraction. We compared the clustering results of our proposed method with two well-known search results clustering techniques, Suffix Tree Clustering and Lingo. The experimental results showed that our proposed method significantly outperforms over the well-known clustering techniques.
Collaboration network of the authors
8 authors ·
12 co-authorship links
· 9 across different organisations
Hover or tap a name to highlight its co-authors. Double-click to open the author's page.
Kasetsart University
Thammasat University
King Mongkut's University of Technology North Bangkok
National Electronics and Computer Technology Center
Tokyo Institute of Technology
Different organisations
Same organisation
Affiliation unknown
Thicker line = more joint articles · bigger circle = more articles
King Mongkut's University of Technology North Bangkok
1
Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.