Answering relationship queries on the web

Finding relationships between entities on the Web, e.g., the connections between different places or the commonalities of people, is a novel and challenging problem. Existing Web search engines excel in keyword matching and document ranking, but they cannot well handle many relationship queries. This paper proposes a new method for answering relationship queries on two entities. Our method first respectively retrieves the top Web pages for either entity from a Web search engine. It then matches these Web pages and generates an ordered list of Web page pairs. Each Web page pair consists of one Web page for either entity. The top ranked Web page pairs are likely to contain the relationships between the two entities. One main challenge in the ranking process is to effectively filter out the large amount of noise in the Web pages without losing much useful information. To achieve this, our method assigns appropriate weights to terms in Web pages and intelligently identifies the potential connecting terms that capture the relationships between the two entities. Only those top potential connecting terms with large weights are used to rank Web page pairs. Finally, the top ranked Web page pairs are presented to the searcher. For each such pair, the query terms and the top potential connecting terms are properly highlighted so that the relationships between the two entities can be easily identified. We implemented a prototype on top of the Google search engine and evaluated it under a wide variety of query scenarios. The experimental results show that our method is effective at finding important relationships with low overhead.

[1]  Neil R. Smalheiser,et al.  The Arrowsmith Project: 2005 Status Report , 2005, ALT.

[2]  Daniel Mahler,et al.  Holistic Query Expansion Using Graphical Models , 2004, New Directions in Question Answering.

[3]  Padmini Srinivasan,et al.  Text mining: Generating hypotheses from MEDLINE , 2004, J. Assoc. Inf. Sci. Technol..

[4]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[5]  Jaideep Srivastava,et al.  Indirect Association: Mining Higher Order Dependencies in Data , 2000, PKDD.

[6]  Stephen E. Robertson,et al.  Okapi at TREC-7: Automatic Ad Hoc, Filtering, VLC and Interactive , 1998, TREC.

[7]  Kathleen R. McKeown,et al.  A Hybrid Approach for Answering Definitional Questions , 2003 .

[8]  Kevin Humphreys,et al.  New Directions in Question Answering , 2006, Information Retrieval.

[9]  Dan Klein,et al.  Evaluating strategies for similarity search on the web , 2002, WWW '02.

[10]  Sanda M. Harabagiu,et al.  Answering complex questions with random walk models , 2006, SIGIR '06.

[11]  Alberto O. Mendelzon,et al.  Database techniques for the World-Wide Web: a survey , 1998, SGMD.

[12]  Monika Henzinger,et al.  Finding Related Pages in the World Wide Web , 1999, Comput. Networks.

[13]  Martin F. Porter,et al.  An algorithm for suffix stripping , 1997, Program.

[14]  Sasha Blair-Goldensohn,et al.  Answering Definitional Questions: A Hybrid Approach , 2004, New Directions in Question Answering.

[15]  Weiguo Fan,et al.  Getting answers to natural language questions on the Web , 2002, J. Assoc. Inf. Sci. Technol..

[16]  Neel Sundaresan,et al.  Mining the Web for relations , 2000, Comput. Networks.