Cited By
View all- Ensan FDu W(2019)Ad hoc retrieval via entity linking and semantic similarityKnowledge and Information Systems10.1007/s10115-018-1190-158:3(551-583)Online publication date: 1-Mar-2019
Exploiting click-through data for entity retrieval
Authors: 
Bodo Billerbeck, 
Gianluca Demartini, 
Claudiu Firan, Tereza Iofciu, Ralf KrestelAuthors Info & Claims
Bodo Billerbeck, Gianluca Demartini, Claudiu Firan, Tereza Iofciu, Ralf KrestelAuthors Info & ClaimsPages 803 - 804
Abstract
We present an approach for answering Entity Retrieval queries using click-through information in query log data from a commercial Web search engine. We compare results using click graphs and session graphs and present an evaluation test set making use of Wikipedia "List of" pages.
References
[1]
Ricardo Baeza-Yates and Alessandro Tiberi. Extracting semantic relations from query logs. In KDD, 2007.
[2]
Tao Cheng, Xifeng Yan, and Kevin Chen-Chuan Chang. Entityrank: searching entities directly and holistically. In VLDB, 2007.
[3]
Nick Craswell and Martin Szummer. Random walks on the click graph. In SIGIR, 2007.
[4]
Desislava Petkova and W. Bruce Croft. Proximity-based document representation for named entity retrieval. In CIKM, 2007.
[5]
Gu Xu, Shuang-Hong Yang, and Hang Li. Named entity mining from click-through data using weakly supervised latent dirichlet allocation. In KDD, 2009.
Index Terms
- Exploiting click-through data for entity retrieval
Recommendations
Exploiting Entity Linking in Queries for Entity Retrieval
ICTIR '16: Proceedings of the 2016 ACM International Conference on the Theory of Information RetrievalThe premise of entity retrieval is to better answer search queries by returning specific entities instead of documents. Many queries mention particular entities; recognizing and linking them to the corresponding entry in a knowledge base is known as the ...
Exploiting the category structure of Wikipedia for entity ranking
The Web has not only grown in size, but also changed its character, due to collaborative content creation and an increasing amount of structure. Current Search Engines find Web pages rather than information or knowledge, and leave it to the searchers to ...
Ranking very many typed entities on wikipedia
CIKM '07: Proceedings of the sixteenth ACM conference on Conference on information and knowledge managementWe discuss the problem of ranking very many entities of different types. In particular we deal with a heterogeneous set of types, some being very generic and some very specific. We discuss two approaches for this problem: i) exploiting the entity ...
Comments
Information & Contributors
Information
Published In
July 2010
944 pages
ISBN:9781450301534
DOI:10.1145/1835449
- General Chairs:
- Fabio Crestani,
- Stéphane Marchand-Maillet,
- Program Chairs:
- Hsin-Hsi Chen,
- Efthimis N. Efthimiadis,
- Jacques Savoy
Copyright © 2010 Copyright is held by the owner/author(s).
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 19 July 2010
Check for updates
Author Tags
Qualifiers
- Poster
Conference
SIGIR '10
Sponsor:
SIGIR '10: The 33rd International ACM SIGIR conference on research and development in Information Retrieval
July 19 - 23, 2010
Geneva, Switzerland
Acceptance Rates
SIGIR '10 Paper Acceptance Rate 87 of 520 submissions, 17%;
Overall Acceptance Rate 792 of 3,983 submissions, 20%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 243Total Downloads
- Downloads (Last 12 months)1
- Downloads (Last 6 weeks)0
Reflects downloads up to 22 Sep 2024
Other Metrics
Citations
Cited By
View all- Ensan FDu W(2019)Ad hoc retrieval via entity linking and semantic similarityKnowledge and Information Systems10.1007/s10115-018-1190-158:3(551-583)Online publication date: 1-Mar-2019
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in