poster

Constructing query-specific knowledge bases

Authors:

Jeffrey Dalton,

Laura DietzAuthors Info & Claims

AKBC '13: Proceedings of the 2013 workshop on Automated knowledge base construction

Pages 55 - 60

https://doi.org/10.1145/2509558.2509568

Published: 27 October 2013 Publication History

Abstract

Abstract Large general purpose knowledge bases (KB) support a variety of complex tasks because of their structured relationships. However, these KBs lack coverage for specialized topics or use cases. In these scenarios, users often use keyword search over large unstructured collections, such as the web. Instead, we propose constructing a 'knowledge sketch' that leverages existing KB data elements and relevant text documents to construct query-specific KB data. A knowledge sketch is a distribution over entities, documents, and relationships between entities, all for a specific information need. In our experiments we construct knowledge sketches for queries from the TREC 2004 Robust track, which emphasizes complex queries which perform poorly with existing text retrieval approaches.

References

[1]

Michael Bendersky, Donald Metzler, and W. Bruce Croft. Learning concept importance using a weighted dependence model. In Proceedings of the third ACM international conference on Web search and data mining, WSDM '10, pages 31--40, New York, NY, USA, 2010. ACM.

Digital Library

[2]

Michael Bendersky, Donald Metzler, and W. Bruce Croft. Effective query formulation with multiple information sources. In Proceedings of the fifth ACM international conference on Web search and data mining, WSDM '12, pages 443--452, New York, NY, USA, 2012. ACM.

Digital Library

[3]

Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor. Freebase: a collaboratively created graph database for structuring human knowledge. In Proceedings of the 2008 ACM SIGMOD international conference on Management of data, SIGMOD '08, pages 1247--1250, New York, NY, USA, 2008. ACM.

Digital Library

[4]

Jeffrey Dalton and Laura Dietz. A Neighborhood Relevance Model for Entity Linking. In Proceedings of the 10th International Conference in the RIAO series (OAIR), 2013, 2013.

Digital Library

[5]

Laura Dietz. Directed Factor Graph Notation for Generative Models. Technical report, Max Planck Institute for Informatics, Saarbrücken, Germany, 2010.

[6]

Ofer Egozi, Evgeniy Gabrilovich, and Shaul Markovitch. Concept-based feature generation and selection for information retrieval. In Proceedings of the 23rd national conference on Artificial intelligence - Volume 2, AAAI'08, pages 1132--1137. AAAI Press, 2008.

Digital Library

[7]

Jonathan L. Elsas, Jaime Arguello, Jamie Callan, and Jaime G. Carbonell. Retrieval and feedback models for blog feed search. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '08, pages 347--354, New York, NY, USA, 2008. ACM.

Digital Library

[8]

J. R. Frank, M. Kleiman-Weiner, D. A. Roberts, F. Niu, C. Zhang, C. Re, and I. Soboroff. Building an Entity-Centric stream filtering test collection for TREC 2012. In Proceedings of the Text REtrieval Conference (TREC), 2012.

[9]

Evgeniy Gabrilovich and Shaul Markovitch. Computing semantic relatedness using Wikipedia-based explicit semantic analysis. In Proceedings of the 20th international joint conference on Artifical intelligence, IJCAI'07, pages 1606--1611, San Francisco, CA, USA, 2007. Morgan Kaufmann Publishers Inc.

Digital Library

[10]

Heng Ji, Ralph Grishman, and Hoa Dang. Overview of the TAC2011 Knowledge Base Population Track. In TAC 2011 Proceedings Papers, 2011.

[11]

Victor Lavrenko and W. Bruce Croft. Relevance based language models. In Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '01, pages 120--127, New York, NY, USA, 2001. ACM.

Digital Library

[12]

Donald Metzler and W. Bruce Croft. A Markov random field model for term dependencies. In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '05, pages 472--479, New York, NY, USA, 2005. ACM.

Digital Library

[13]

Jay M. Ponte and W. Bruce Croft. A language modeling approach to information retrieval. In Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '98, pages 275--281, New York, NY, USA, 1998. ACM.

Digital Library

[14]

Jeffrey Pound, Peter Mika, and Hugo Zaragoza. Ad-hoc object retrieval in the web of data. In Proceedings of the 19th international conference on World wide web, WWW '10, pages 771--780, New York, NY, USA, 2010. ACM.

Digital Library

[15]

Nico Schlaefer, Jennifer C. Carroll, Eric Nyberg, James Fan, Wlodek Zadrozny, and David Ferrucci. Statistical source expansion for question answering. In Proceedings of the 20th ACM international conference on Information and knowledge management, CIKM '11, pages 345--354, New York, NY, USA, 2011. ACM.

Digital Library

[16]

Fabian M. Suchanek, Gjergji Kasneci, and Gerhard Weikum. Yago: a core of semantic knowledge. In Proceedings of the 16th international conference on World Wide Web, WWW '07, pages 697--706, New York, NY, USA, 2007. ACM.

Digital Library

[17]

Ellen M. Voorhees. Overview of the TREC 2004 robust retrieval track. In Proceedings of TREC 2004, 2004.

[18]

Michael L. Wick and Andrew McCallum. Query-Aware MCMC. In Advances in Neural Information Processing Systems, pages 2564--2572, 2011.

[19]

Yang Xu, Gareth J. F. Jones, and Bin Wang. Query dependent pseudo-relevance feedback based on wikipedia. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, SIGIR '09, pages 59--66, New York, NY, USA, 2009. ACM.

Digital Library

Cited By

El Zein DCâmara ADa Costa Pereira CTettamanzi A(2023)RULKNE: Representing User Knowledge State in Search-as-Learning with Named EntitiesProceedings of the 2023 Conference on Human Information Interaction and Retrieval10.1145/3576840.3578330(388-393)Online publication date: 19-Mar-2023
https://dl.acm.org/doi/10.1145/3576840.3578330

Index Terms

Constructing query-specific knowledge bases
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

Recommendations

Subjective Knowledge Base Construction Powered By Crowdsourcing and Knowledge Base
SIGMOD '18: Proceedings of the 2018 International Conference on Management of Data

Knowledge base construction (KBC) has become a hot and in-time topic recently with the increasing application need of large-scale knowledge bases (KBs), such as semantic search, QA systems, the Google Knowledge Graph and IBM Watson QA System. Existing ...
A simulation framework for knowledge acquisition evaluation
ACSC '05: Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38

Knowledge acquisition (KA) plays an important role in building knowledge based systems (KBS). However, evaluating different KA techniques has been difficult because of the costs of using human expertise in experimental studies. In this paper, we first ...
Incremental knowledge base construction using DeepDive

Populating a database with information from unstructured sources--also known as knowledge base construction (KBC)--is a long-standing problem in industry and research that encompasses problems of extraction, cleaning, and integration. In this work, we ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

AKBC '13: Proceedings of the 2013 workshop on Automated knowledge base construction

October 2013

124 pages

ISBN:9781450324113

DOI:10.1145/2509558

Program Chairs:
Fabian M. Suchanek
Max Planck Institute for Informatics, Germany
,
Sebastian Riedel
University College London, UK
,
Sameer Singh
University of Massachusetts Amherst, USA
,
Partha Pratim Talukdar
Carnegie Mellon University, USA

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 October 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Poster

Conference

CIKM'13

Sponsor:

CIKM'13: 22nd ACM International Conference on Information and Knowledge Management

October 27 - 28, 2013

California, San Francisco, USA

Acceptance Rates

AKBC '13 Paper Acceptance Rate 9 of 19 submissions, 47%;

Overall Acceptance Rate 9 of 19 submissions, 47%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
153
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)1

Reflects downloads up to 15 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

El Zein DCâmara ADa Costa Pereira CTettamanzi A(2023)RULKNE: Representing User Knowledge State in Search-as-Learning with Named EntitiesProceedings of the 2023 Conference on Human Information Interaction and Retrieval10.1145/3576840.3578330(388-393)Online publication date: 19-Mar-2023
https://dl.acm.org/doi/10.1145/3576840.3578330

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.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents