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

来源 :Journal of Computer Science & Technology | 被引量 : 0次 | 上传用户:ws1984003
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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. With the rapid growth of Web databases, it is necessary to extract and integrate large-scale data available in Deep Web automatically. Current web search engines conduct page-level ranking, which are for inadequate for entity-oriented vertical search. , we present an entity-level ranking mechanism called LG-ERM for Deep Web queries based on local scoring and global aggregation. Like 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. These experiments demonstrate the feasibility and effectiveness of the key techniques of LG-ERM.
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