research-article

Open Domain Question Answering via Semantic Enrichment

Authors:

Ming-Wei ChangAuthors Info & Claims

WWW '15: Proceedings of the 24th International Conference on World Wide Web

Pages 1045 - 1055

https://doi.org/10.1145/2736277.2741651

Published: 18 May 2015 Publication History

Abstract

Most recent question answering (QA) systems query large-scale knowledge bases (KBs) to answer a question, after parsing and transforming natural language questions to KBs-executable forms (e.g., logical forms). As a well-known fact, KBs are far from complete, so that information required to answer questions may not always exist in KBs. In this paper, we develop a new QA system that mines answers directly from the Web, and meanwhile employs KBs as a significant auxiliary to further boost the QA performance. Specifically, to the best of our knowledge, we make the first attempt to link answer candidates to entities in Freebase, during answer candidate generation. Several remarkable advantages follow: (1) Redundancy among answer candidates is automatically reduced. (2) The types of an answer candidate can be effortlessly determined by those of its corresponding entity in Freebase. (3) Capitalizing on the rich information about entities in Freebase, we can develop semantic features for each answer candidate after linking them to Freebase. Particularly, we construct answer-type related features with two novel probabilistic models, which directly evaluate the appropriateness of an answer candidate's types under a given question. Overall, such semantic features turn out to play significant roles in determining the true answers from the large answer candidate pool. The experimental results show that across two testing datasets, our QA system achieves an 18%~54% improvement under F_1 metric, compared with various existing QA systems.

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Cited By

Setty VKlein MBen-David AJäschke RKelly M(2024)Extreme Classification for Answer Type Prediction in Question AnsweringProceedings of the 2023 ACM/IEEE Joint Conference on Digital Libraries10.1109/JCDL57899.2023.00041(232-236)Online publication date: 26-Jun-2024
https://dl.acm.org/doi/10.1109/JCDL57899.2023.00041
Andreasen TBordogna GTré GKacprzyk JLarsen HZadrożny S(2024)The power and potentials of Flexible Query Answering Systems: A critical and comprehensive analysisData & Knowledge Engineering10.1016/j.datak.2023.102246149(102246)Online publication date: Jan-2024
https://doi.org/10.1016/j.datak.2023.102246
Franciscatto MErpen de Bona LTrois CDidonet Del FabroFabro MDamasceno Lima J(2024)Situational Data Integration in Question Answering systems: a survey over two decadesKnowledge and Information Systems10.1007/s10115-024-02136-066:10(5875-5918)Online publication date: 18-Jun-2024
https://doi.org/10.1007/s10115-024-02136-0
Show More Cited By

Index Terms

Open Domain Question Answering via Semantic Enrichment
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Logic programming and answer set programming
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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cover image ACM Other conferences

WWW '15: Proceedings of the 24th International Conference on World Wide Web

May 2015

1460 pages

ISBN:9781450334693

General Chairs:
Aldo Gangemi
National Research Council, Italy & Paris 13 University-CNRS, France
,
Stefano Leonardi
Sapienza University of Rome, Italy
,
Alessandro Panconesi
Sapienza University of Rome, Italy

Copyright © 2015 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

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

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

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Published: 18 May 2015

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WWW '15: 24th International World Wide Web Conference

May 18 - 22, 2015

Florence, Italy

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WWW '15 Paper Acceptance Rate 131 of 929 submissions, 14%;

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

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Cited By

Setty VKlein MBen-David AJäschke RKelly M(2024)Extreme Classification for Answer Type Prediction in Question AnsweringProceedings of the 2023 ACM/IEEE Joint Conference on Digital Libraries10.1109/JCDL57899.2023.00041(232-236)Online publication date: 26-Jun-2024
https://dl.acm.org/doi/10.1109/JCDL57899.2023.00041
Andreasen TBordogna GTré GKacprzyk JLarsen HZadrożny S(2024)The power and potentials of Flexible Query Answering Systems: A critical and comprehensive analysisData & Knowledge Engineering10.1016/j.datak.2023.102246149(102246)Online publication date: Jan-2024
https://doi.org/10.1016/j.datak.2023.102246
Franciscatto MErpen de Bona LTrois CDidonet Del FabroFabro MDamasceno Lima J(2024)Situational Data Integration in Question Answering systems: a survey over two decadesKnowledge and Information Systems10.1007/s10115-024-02136-066:10(5875-5918)Online publication date: 18-Jun-2024
https://doi.org/10.1007/s10115-024-02136-0
Guo MZhou ZGotz DWang Y(2023)GRAFS: Graphical Faceted Search System to Support Conceptual Understanding in Exploratory SearchACM Transactions on Interactive Intelligent Systems10.1145/358831913:2(1-36)Online publication date: 31-Mar-2023
https://dl.acm.org/doi/10.1145/3588319
Peng Y(2023)Query-Driven Knowledge Graph Construction using Question Answering and Multimodal FusionCompanion Proceedings of the ACM Web Conference 202310.1145/3543873.3587567(1119-1126)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543873.3587567
Wang NChen TRen CWang H(2023)Document-level relation extraction with multi-layer heterogeneous graph attention networkEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106212123(106212)Online publication date: Aug-2023
https://doi.org/10.1016/j.engappai.2023.106212
Qamar FLatif SShah A(2023)Techniques, datasets, evaluation metrics and future directions of a question answering systemKnowledge and Information Systems10.1007/s10115-023-02019-w66:4(2235-2268)Online publication date: 22-Dec-2023
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Chi RLiu X(2023)Improvement of Graph Convolution Network of Missing Data Based on P SystemsAdvanced Intelligent Computing Technology and Applications10.1007/978-981-99-4752-2_25(298-309)Online publication date: 31-Jul-2023
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Houssein EIbrahem NZaki ASayed A(2022)Semantic Protocol and Resource Description Framework Query Language: A Comprehensive ReviewMathematics10.3390/math1017320310:17(3203)Online publication date: 5-Sep-2022
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Acheampong KTian W(2022)Advancement of Textual Answer Triggering: Cognitive BoostingIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2020.302273110:1(361-372)Online publication date: 1-Jan-2022
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