research-article

Learning to Extract Conditional Knowledge for Question Answering using Dialogue

Authors:

Wei-Ying MaAuthors Info & Claims

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

Pages 277 - 286

https://doi.org/10.1145/2983323.2983777

Published: 24 October 2016 Publication History

Abstract

Knowledge based question answering (KBQA) has attracted much attention from both academia and industry in the field of Artificial Intelligence. However, many existing knowledge bases (KBs) are built by static triples. It is hard to answer user questions with different conditions, which will lead to significant answer variances in questions with similar intent. In this work, we propose to extract conditional knowledge base (CKB) from user question-answer pairs for answering user questions with different conditions through dialogue. Given a subject, we first learn user question patterns and conditions. Then we propose an embedding based co-clustering algorithm to simultaneously group the patterns and conditions by leveraging the answers as supervisor information. After that, we extract the answers to questions conditioned on both question pattern clusters and condition clusters as a CKB. As a result, when users ask a question without clearly specifying the conditions, we use dialogues in natural language to chat with users for question specification and answer retrieval. Experiments on real question answering (QA) data show that the dialogue model using automatically extracted CKB can more accurately answer user questions and significantly improve user satisfaction for questions with missing conditions.

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Cited By

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://doi.org/10.1007/s10489-024-05282-8
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
Zhang DCheng SYin D(2021)A Multi-semantic Knowledge Graph Construction Scheme Suitable for Intelligent Power Customer ServiceInternational Conference on Frontiers of Electronics, Information and Computation Technologies10.1145/3474198.3478153(1-7)Online publication date: 21-May-2021
https://dl.acm.org/doi/10.1145/3474198.3478153
Show More Cited By

Index Terms

Learning to Extract Conditional Knowledge for Question Answering using Dialogue
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Discourse, dialogue and pragmatics
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Information extraction

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cover image ACM Conferences

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

October 2016

2566 pages

ISBN:9781450340731

DOI:10.1145/2983323

General Chairs:
Snehasis Mukhopadhyay
Indiana University Purdue University Indianapolis, USA
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign, USA
,
Program Chairs:
Elisa Bertino
Purdue University
,
Fabio Crestani
University of Lugano
,
Javed Mostafa
University of North Carolina
,
Jie Tang
Tsinghua University
,
Luo Si
Alibaba Group Inc & Purdue University
,
Xiaofang Zhou
University of Queensland
,
Yi Chang
Yahoo Research
,
Yunyao Li
IBM Research - Almaden
,
Parikshit Sondhi
WalmartLabs

Copyright © 2016 ACM.

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Published: 24 October 2016

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CIKM'16: ACM Conference on Information and Knowledge Management

October 24 - 28, 2016

Indiana, Indianapolis, USA

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CIKM '16 Paper Acceptance Rate 160 of 701 submissions, 23%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

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://doi.org/10.1007/s10489-024-05282-8
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
Zhang DCheng SYin D(2021)A Multi-semantic Knowledge Graph Construction Scheme Suitable for Intelligent Power Customer ServiceInternational Conference on Frontiers of Electronics, Information and Computation Technologies10.1145/3474198.3478153(1-7)Online publication date: 21-May-2021
https://dl.acm.org/doi/10.1145/3474198.3478153
Sun YGui WHan CZhang YZhang S(2020)AIServiceX: A Knowledge Graph-Based Intelligent Question-Answering System for Personal ServicesServices – SERVICES 202010.1007/978-3-030-59595-1_7(85-92)Online publication date: 17-Sep-2020
https://doi.org/10.1007/978-3-030-59595-1_7
Sun YMeng LZhang Y(2020)Semantic Enhancement Based Dynamic Construction of Domain Knowledge GraphCognitive Computing – ICCC 202010.1007/978-3-030-59585-2_9(107-114)Online publication date: 14-Sep-2020
https://doi.org/10.1007/978-3-030-59585-2_9
Yu QLam WChang YZhai CLiu YMaarek Y(2018)Review-Aware Answer Prediction for Product-Related Questions Incorporating AspectsProceedings of the Eleventh ACM International Conference on Web Search and Data Mining10.1145/3159652.3159718(691-699)Online publication date: 2-Feb-2018
https://dl.acm.org/doi/10.1145/3159652.3159718
Lee YKoh J(2018)Conditional Relationship Extraction for Diseases and Symptoms by a Web Search-Based Approach2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)10.1109/WI.2018.00-38(554-561)Online publication date: Dec-2018
https://doi.org/10.1109/WI.2018.00-38
Meng ZMou LJin ZLim EWinslett MSanderson MFu ASun JCulpepper SLo EHo JDonato DAgrawal RZheng YCastillo CSun ATseng VLi C(2017)Hierarchical RNN with Static Sentence-Level Attention for Text-Based Speaker Change DetectionProceedings of the 2017 ACM on Conference on Information and Knowledge Management10.1145/3132847.3133110(2203-2206)Online publication date: 6-Nov-2017
https://dl.acm.org/doi/10.1145/3132847.3133110
Chen JShao JShen FHe CGao LShen HLim EWinslett MSanderson MFu ASun JCulpepper SLo EHo JDonato DAgrawal RZheng YCastillo CSun ATseng VLi C(2017)Movie Fill in the Blank with Adaptive Temporal Attention and Description UpdateProceedings of the 2017 ACM on Conference on Information and Knowledge Management10.1145/3132847.3132922(1039-1048)Online publication date: 6-Nov-2017
https://dl.acm.org/doi/10.1145/3132847.3132922

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