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Hierarchical Type Constrained Topic Entity Detection for Knowledge Base Question Answering

Authors:

Xiaolong JinAuthors Info & Claims

WWW '18: Companion Proceedings of the The Web Conference 2018

Pages 35 - 36

https://doi.org/10.1145/3184558.3186916

Published: 23 April 2018 Publication History

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Abstract

Topic entity detection is to find out the main entity asked in a question, which is significant in question answering. Traditional methods ignore the information of entities, especially entity types and their hierarchical structures, restricting the performance. To take full advantage of Knowledge Base(KB) and detect topic entities correctly, we propose a deep neural model to leverage type hierarchy and relations of entities in KB. Experimental results demonstrate the effectiveness of the proposed method.

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Cited By

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Zafar ASahoo SBhardawaj HDas AEkbal A(2024)KI-MAGNeurocomputing10.1016/j.neucom.2023.127141571:COnline publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1016/j.neucom.2023.127141
Andreasen TBordogna GTré GKacprzyk JLarsen HZadrożny S(2024)The power and potentials of Flexible Query Answering SystemsData & Knowledge Engineering10.1016/j.datak.2023.102246149:COnline publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1016/j.datak.2023.102246
Zafar ASahoo SVarshney DDas AEkbal A(2024)KIMedQA: towards building knowledge-enhanced medical QA modelsJournal of Intelligent Information Systems10.1007/s10844-024-00844-162:3(833-858)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s10844-024-00844-1
Show More Cited By

Index Terms

Hierarchical Type Constrained Topic Entity Detection for Knowledge Base Question Answering
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Information extraction
      2. Question answering

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Information & Contributors

Information

Published In

WWW '18: Companion Proceedings of the The Web Conference 2018

April 2018

2023 pages

ISBN:9781450356404

General Chairs:
Pierre-Antoine Champin
Université Claude Bernard Lyon 1, France
,
Fabien Gandon
Inria, Université Côte d'Azur, CNRS, I3S, France
,
Lionel Médini
Université Claude Bernard Lyon 1, CNRS, LIRIS, France
,
Program Chairs:
Mounia Lalmas
Spotify, UK
,
Panagiotis G. Ipeirotis
New York University, USA

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]

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 23 April 2018

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Poster

Funding Sources

National Natural Science Foundation of China
National Key Research and Development Program of China

Conference

WWW '18

Sponsor:

IW3C2

WWW '18: The Web Conference 2018

April 23 - 27, 2018

Lyon, France

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

View all

Zafar ASahoo SBhardawaj HDas AEkbal A(2024)KI-MAGNeurocomputing10.1016/j.neucom.2023.127141571:COnline publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1016/j.neucom.2023.127141
Andreasen TBordogna GTré GKacprzyk JLarsen HZadrożny S(2024)The power and potentials of Flexible Query Answering SystemsData & Knowledge Engineering10.1016/j.datak.2023.102246149:COnline publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1016/j.datak.2023.102246
Zafar ASahoo SVarshney DDas AEkbal A(2024)KIMedQA: towards building knowledge-enhanced medical QA modelsJournal of Intelligent Information Systems10.1007/s10844-024-00844-162:3(833-858)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s10844-024-00844-1
Zafar AVarshney DKumar Sahoo SDas AEkbal A(2024)Are my answers medically accurate? Exploiting medical knowledge graphs for medical question answeringApplied Intelligence10.1007/s10489-024-05282-854:2(2172-2187)Online publication date: 31-Jan-2024
https://dl.acm.org/doi/10.1007/s10489-024-05282-8
Zhang YWan CXiao KWan QLiu DLiu X(2023)rHDP: An Aspect Sharing-Enhanced Hierarchical Topic Model for Multi-Domain CorpusACM Transactions on Information Systems10.1145/363135242:3(1-31)Online publication date: 29-Dec-2023
https://dl.acm.org/doi/10.1145/3631352
Shen YYang MLi YWang DZheng HChen D(2023)Knowledge-Based Reasoning Network for Relation DetectionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2021.312375134:8(5051-5063)Online publication date: Aug-2023
https://doi.org/10.1109/TNNLS.2021.3123751
Xie JWang YGe WXu J(2022)A relation detection method based on multi semantic similarityXibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University10.1051/jnwpu/2021396138739:6(1387-1394)Online publication date: 21-Mar-2022
https://doi.org/10.1051/jnwpu/20213961387
Huang XZhang JXu ZOu LTong J(2021)A knowledge graph based question answering method for medical domainPeerJ Computer Science10.7717/peerj-cs.6677(e667)Online publication date: 1-Sep-2021
https://doi.org/10.7717/peerj-cs.667
Tao SQiu RPing YXu WMa H(2020)Making Explainable Friend Recommendations Based on Concept Similarity Measurements via a Knowledge GraphIEEE Access10.1109/ACCESS.2020.30146708(146027-146038)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3014670
Qiu YWang YJin X(2018)Joint Detection of Topic Entity and Relation for Simple Question AnsweringKnowledge Science, Engineering and Management10.1007/978-3-319-99247-1_33(371-382)Online publication date: 17-Aug-2018
https://dl.acm.org/doi/10.1007/978-3-319-99247-1_33

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