TY - GEN
T1 - EntityRank
T2 - 33rd International Conference on Very Large Data Bases, VLDB 2007
AU - Cheng, Tao
AU - Yan, Xifeng
AU - Chang, Kevin Chen Chuan
N1 - Funding Information:
This material is based on work partially supported by NSF Grants IIS-0133199,IIS-0313260, the 2004, 2005 IBM Faculty Awards.
Publisher Copyright:
Copyright 2007 VLDB Endowment, ACM.
PY - 2007
Y1 - 2007
N2 - As the Web has evolved into a data-rich repository, with the standard "page view," current search engines are becoming increasingly inadequate for a wide range of query tasks. While we often search for various data "entities" (e.g., phone number, paper PDF, date), today's engines only take us indirectly to pages. While entities appear in many pages, current engines only find each page individually. Toward searching directly and holistically for finding information of finer granularity, we study the problem of entity search, a significant departure from traditional document retrieval. We focus on the core challenge of ranking entities, by distilling its underlying conceptual model Impression Model and developing a probabilistic ranking framework, EntityRank, that is able to seamlessly integrate both local and global information in ranking. We evaluate our online prototype over a 2TB Web corpus, and show that EntityRank performs effectively.
AB - As the Web has evolved into a data-rich repository, with the standard "page view," current search engines are becoming increasingly inadequate for a wide range of query tasks. While we often search for various data "entities" (e.g., phone number, paper PDF, date), today's engines only take us indirectly to pages. While entities appear in many pages, current engines only find each page individually. Toward searching directly and holistically for finding information of finer granularity, we study the problem of entity search, a significant departure from traditional document retrieval. We focus on the core challenge of ranking entities, by distilling its underlying conceptual model Impression Model and developing a probabilistic ranking framework, EntityRank, that is able to seamlessly integrate both local and global information in ranking. We evaluate our online prototype over a 2TB Web corpus, and show that EntityRank performs effectively.
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M3 - Conference contribution
AN - SCOPUS:85011015609
T3 - 33rd International Conference on Very Large Data Bases, VLDB 2007 - Conference Proceedings
SP - 387
EP - 398
BT - 33rd International Conference on Very Large Data Bases, VLDB 2007 - Conference Proceedings
A2 - Gehrke, Johannes
A2 - Koch, Christoph
A2 - Garofalakis, Minos
A2 - Aberer, Karl
A2 - Kanne, Carl-Christian
A2 - Neuhold, Erich J.
A2 - Ganti, Venkatesh
A2 - Klas, Wolfgang
A2 - Chan, Chee-Yong
A2 - Srivastava, Divesh
A2 - Florescu, Dana
A2 - Deshpande, Anand
PB - Association for Computing Machinery, Inc
Y2 - 23 September 2007 through 27 September 2007
ER -