ACM Home Page
Please provide us with feedback. Feedback
Answering relationship queries on the web
Full text PdfPdf (399 KB)
Source
International World Wide Web Conference archive
Proceedings of the 16th international conference on World Wide Web table of contents
Banff, Alberta, Canada
SESSION: Knowledge discovery table of contents
Pages: 561 - 570  
Year of Publication: 2007
ISBN:978-1-59593-654-7
Authors
Gang Luo  IBM T.J. Watson Research Center
Chunqiang Tang  IBM T.J. Watson Research Center
Ying-li Tian  IBM T.J. Watson Research Center
Sponsor
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 16,   Downloads (12 Months): 129,   Citation Count: 9
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1242572.1242648
What is a DOI?

ABSTRACT

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.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
 
2
 
3
S. Blair-Goldensohn, K. McKeown, and A. H. Schlaikjer. Answering Definitional Questions: a Hybrid Approach. New Directions in Question Answering 2004: 47--58.
 
4
 
5
6
 
7
8
9
 
10
D. Mahler. Holistic Query Expansion Using Graphical Models. New Directions in Question Answering 2004: 203--214.
 
11
 
12
 
13
M. F. Porter. An Algorithm for Suffix Stripping. Program 14(3): 130--137, 1980.
 
14
J. Prange. Making the Case for Advanced Question Answering. Keynote Speech to Pragmatics of Question Answering Workshop at HLT/NAACL 2004.
 
15
 
16
 
17
S. E. Robertson, S. Walker, and M. Hancock-Beaulieu. Okapi at TREC-7: Automatic Ad Hoc, Filtering, VLC and Interactive. TREC 1998: 199--210.
 
18
A. Singhal. Modern Information Retrieval: A Brief Overview. IEEE Data Eng. Bull. 24(4): 35--43, 2001.
 
19
N. R. Smalheiser. The Arrowsmith Project: 2005 Status Report. Discovery Science 2005: 26--43.
 
20
SMART Stopword List. http://www.lextek.com/manuals/onix/stopwords2.html, 2006.
 
21
 
22
 
23
 
24

CITED BY  10

Collaborative Colleagues:
Gang Luo: colleagues
Chunqiang Tang: colleagues
Ying-li Tian: colleagues