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Combining Local Scoring and Global Aggregation to Rank Entities for Deep Web Queries

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Abstract

With the rapid growth of Web databases, it is necessary to extract and integrate large-scale data available in Deep Web automatically. But current Web search engines conduct page-level ranking, which are becoming inadequate for entity-oriented vertical search. In this paper, we present an entity-level ranking mechanism called LG-ERM for Deep Web queries based on local scoring and global aggregation. Unlike traditional approaches, LG-ERM considers more rank influencing factors including the uncertainty of entity extraction, the style information of the entities and the importance of the Web sources, as well as the entity relationship. By combining local scoring and global aggregation in ranking, the query result can be more accurate and effective to meet users' needs. The experiments demonstrate the feasibility and effectiveness of the key techniques of LG-ERM.

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Authors and Affiliations

  1. College of Information Science and Engineering, Northeastern University, Shenyang, 110004, China

    Yue Kou (Student Member, CCF), De-Rong Shen (Senior Member, CCF), Ge Yu (Senior Member, CCF, Member, ACM, IEEE) & Tie-Zheng Nie (Student Member, CCF)

Authors
  1. Yue Kou
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  2. De-Rong Shen
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  3. Ge Yu
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  4. Tie-Zheng Nie
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Corresponding author

Correspondence to Yue Kou.

Additional information

Supported by the National Natural Science Foundation of China under Grant No. 60673139 and the National High Technology Development and Research 863 Program of China under Grant No. 2008AA01Z146.

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Kou, Y., Shen, DR., Yu, G. et al. Combining Local Scoring and Global Aggregation to Rank Entities for Deep Web Queries. J. Comput. Sci. Technol. 24, 626–637 (2009). https://doi.org/10.1007/s11390-009-9263-y

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