research-article

Leveraging related entities for knowledge base acceleration

Authors:

Xitong Liu,

Hui FangAuthors Info & Claims

Web-KR '13: Proceedings of the 4th international workshop on Web-scale knowledge representation retrieval and reasoning

Pages 1 - 4

https://doi.org/10.1145/2512405.2512407

Published: 01 November 2013 Publication History

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Abstract

Knowledge bases such as Wikipedia have been shown to be effective to improve the performance in many information tasks. Clearly, the effectiveness is based upon the quality of these knowledge bases. A high-quality knowledge base should have up-to-date complete information. However, constructing a high-quality knowledge base is not an easy task because it would require significant manual efforts to collect relevant documents, extract valuable information and update the knowledge bases accordingly. In this paper, we aim to automate this labor-intensive process. Specifically, we focus on how to collect relevant documents with regard to an entity from sheer volume of Web data automatically. To solve the problem, we propose to construct the profile of the entity by leveraging a set of its related entities and then discuss how to use the training data to weight the related entities. Experiments over the TREC 2012 KBA collection shows that the proposed method can outperform state-of-the-art methods.

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Cited By

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Reinanda RMeij Ede Rijke MMukhopadhyay SZhai CBertino ECrestani FMostafa JTang JSi LZhou XChang YLi YSondhi P(2016)Document Filtering for Long-tail EntitiesProceedings of the 25th ACM International on Conference on Information and Knowledge Management10.1145/2983323.2983728(771-780)Online publication date: 24-Oct-2016
https://dl.acm.org/doi/10.1145/2983323.2983728
Kawahara SSeki KUehara K(2016)Detecting Vital Documents Using Negative Relevance Feedback in Distributed Realtime Computation FrameworkComputational Linguistics10.1007/978-981-10-0515-2_14(193-208)Online publication date: 20-Feb-2016
https://doi.org/10.1007/978-981-10-0515-2_14
Wang JSong DWang QZhang ZSi LLiao LLin CBaeza-Yates RLalmas MMoffat ARibeiro-Neto B(2015)An Entity Class-Dependent Discriminative Mixture Model for Cumulative Citation RecommendationProceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/2766462.2767698(635-644)Online publication date: 9-Aug-2015
https://dl.acm.org/doi/10.1145/2766462.2767698
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Index Terms

Leveraging related entities for knowledge base acceleration
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

Web-KR '13: Proceedings of the 4th international workshop on Web-scale knowledge representation retrieval and reasoning

November 2013

26 pages

ISBN:9781450324243

DOI:10.1145/2512405

Program Chairs:
Yi Zeng
Chinese Academy of Sciences, China
,
Spyros Kotoulas
IBM Research, Ireland
,
Zhisheng Huang
VU University Amsterdam, The Netherlands

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 ACM 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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 November 2013

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CIKM'13

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

November 1, 2013

California, San Francisco, USA

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Web-KR '13 Paper Acceptance Rate 4 of 4 submissions, 100%;

Overall Acceptance Rate 4 of 4 submissions, 100%

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Cited By

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Reinanda RMeij Ede Rijke MMukhopadhyay SZhai CBertino ECrestani FMostafa JTang JSi LZhou XChang YLi YSondhi P(2016)Document Filtering for Long-tail EntitiesProceedings of the 25th ACM International on Conference on Information and Knowledge Management10.1145/2983323.2983728(771-780)Online publication date: 24-Oct-2016
https://dl.acm.org/doi/10.1145/2983323.2983728
Kawahara SSeki KUehara K(2016)Detecting Vital Documents Using Negative Relevance Feedback in Distributed Realtime Computation FrameworkComputational Linguistics10.1007/978-981-10-0515-2_14(193-208)Online publication date: 20-Feb-2016
https://doi.org/10.1007/978-981-10-0515-2_14
Wang JSong DWang QZhang ZSi LLiao LLin CBaeza-Yates RLalmas MMoffat ARibeiro-Neto B(2015)An Entity Class-Dependent Discriminative Mixture Model for Cumulative Citation RecommendationProceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/2766462.2767698(635-644)Online publication date: 9-Aug-2015
https://dl.acm.org/doi/10.1145/2766462.2767698
Abbes RPinel-Sauvagnat KHernandez NBoughanem MWainwright RCorchado JBechini AHong J(2015)Leveraging temporal expressions to filter vital documents related to an entityProceedings of the 30th Annual ACM Symposium on Applied Computing10.1145/2695664.2695910(1093-1098)Online publication date: 13-Apr-2015
https://dl.acm.org/doi/10.1145/2695664.2695910
Kenter TBalog Kde Rijke M(2015)Evaluating document filtering systems over timeInformation Processing & Management10.1016/j.ipm.2015.03.00551:6(791-808)Online publication date: Nov-2015
https://doi.org/10.1016/j.ipm.2015.03.005
Wang JLiao LSong DMa LLin CRui Y(2015)Resorting Relevance Evidences to Cumulative Citation Recommendation for Knowledge Base AccelerationWeb-Age Information Management10.1007/978-3-319-21042-1_14(169-180)Online publication date: 6-Jun-2015
https://doi.org/10.1007/978-3-319-21042-1_14
Gebremeskel Gde Vries A(2015)Entity-Centric Stream Filtering and Ranking: Filtering and Unfilterable DocumentsAdvances in Information Retrieval10.1007/978-3-319-16354-3_33(303-314)Online publication date: 2015
https://doi.org/10.1007/978-3-319-16354-3_33
Gebremeskel GHe JVries ALin J(2014)Cumulative Citation Recommendation: A Feature-Aware Comparison of Approaches2014 25th International Workshop on Database and Expert Systems Applications10.1109/DEXA.2014.49(193-197)Online publication date: Sep-2014
https://doi.org/10.1109/DEXA.2014.49
Zeng YKotoulas SHuang ZHe QIyengar ANejdl WPei JRastogi R(2013)Web-KR 2013Proceedings of the 22nd ACM international conference on Information & Knowledge Management10.1145/2505515.2505819(2563-2564)Online publication date: 27-Oct-2013
https://dl.acm.org/doi/10.1145/2505515.2505819

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