research-article

IMPROVE-QA: An Interactive Mechanism for RDF Question/Answering Systems

Authors:

Xinbo Zhang,

Lei ZouAuthors Info & Claims

SIGMOD '18: Proceedings of the 2018 International Conference on Management of Data

Pages 1753 - 1756

https://doi.org/10.1145/3183713.3193555

Published: 27 May 2018 Publication History

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Abstract

RDF Question/Answering(Q/A) systems can interpret user's question N as SPARQL query Q and return answer set $Q(D)$ over RDF repository D to the user. However, due to the complexity of linking natural phrases with specific RDF items (e.g., entities and predicates), it remains difficult to understand users' questions precisely, hence $Q(D)$ may not meet users' expectation, offering wrong answers and dismissing some correct answers. In this demo, we design an I Interactive Mechanism aiming for PRO motion V ia feedback to Q/A systems (IMPROVE-QA), a whole platform to make existing Q/A systems return more precise answers (denoted as $\mathcal Q^\prime (D)$) to users. Based on user's feedback over $Q(D)$, IMPROVE-QA automatically refines the original query Q into a new query graph $\mathcal Q^\prime $ with minimum modifications, where $\mathcal Q^\prime (D)$ provides more precise answers. We will also demonstrate how IMPROVE-QA can apply the "lesson'' learned from the user in each query to improve the precision of Q/A systems on subsequent natural language questions.

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Cited By

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Song YLu JZhao XWong RZhang H(2024)Demonstration of FeVisQA: Free-Form Question Answering over Data Visualization2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00417(5417-5420)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00417
Zope BMishra SShaw KVora DKotecha KBidwe R(2022)Question Answer System: A State-of-Art Representation of Quantitative and Qualitative AnalysisBig Data and Cognitive Computing10.3390/bdcc60401096:4(109)Online publication date: 7-Oct-2022
https://doi.org/10.3390/bdcc6040109
Wang YXu XHong QJin JWu T(2022)Top-k star queries on knowledge graphs through semantic-aware bounding match scoresKnowledge-Based Systems10.1016/j.knosys.2020.106655213:COnline publication date: 23-Apr-2022
https://dl.acm.org/doi/10.1016/j.knosys.2020.106655
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Published In

SIGMOD '18: Proceedings of the 2018 International Conference on Management of Data

May 2018

1874 pages

ISBN:9781450347037

DOI:10.1145/3183713

General Chairs:
Gautam Das
University of Texas at Arlington, USA
,
Christopher Jermaine
Rice University, USA
,
Philip Bernstein
Microsoft Research, 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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 May 2018

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SIGMOD/PODS '18

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SIGMOD

SIGMOD/PODS '18: International Conference on Management of Data

June 10 - 15, 2018

TX, Houston, USA

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SIGMOD '18 Paper Acceptance Rate 90 of 461 submissions, 20%;

Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Song YLu JZhao XWong RZhang H(2024)Demonstration of FeVisQA: Free-Form Question Answering over Data Visualization2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00417(5417-5420)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00417
Zope BMishra SShaw KVora DKotecha KBidwe R(2022)Question Answer System: A State-of-Art Representation of Quantitative and Qualitative AnalysisBig Data and Cognitive Computing10.3390/bdcc60401096:4(109)Online publication date: 7-Oct-2022
https://doi.org/10.3390/bdcc6040109
Wang YXu XHong QJin JWu T(2022)Top-k star queries on knowledge graphs through semantic-aware bounding match scoresKnowledge-Based Systems10.1016/j.knosys.2020.106655213:COnline publication date: 23-Apr-2022
https://dl.acm.org/doi/10.1016/j.knosys.2020.106655
Sakor ASingh KPatel AVidal Md'Aquin MDietze SHauff CCurry ECudre Mauroux P(2020)Falcon 2.0Proceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3412777(3141-3148)Online publication date: 19-Oct-2020
https://dl.acm.org/doi/10.1145/3340531.3412777
Wu ZKao BWu TYin PLiu QCaverlee JHu XLalmas MWang W(2020)PERQProceedings of the 13th International Conference on Web Search and Data Mining10.1145/3336191.3371782(663-671)Online publication date: 20-Jan-2020
https://dl.acm.org/doi/10.1145/3336191.3371782
Wang YKhan AWu TJin JYan H(2020)Semantic Guided and Response Times Bounded Top-k Similarity Search over Knowledge Graphs2020 IEEE 36th International Conference on Data Engineering (ICDE)10.1109/ICDE48307.2020.00045(445-456)Online publication date: Apr-2020
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Schwertner MRigo SAraujo DSilva AEskofier B(2019)Fostering Natural Language Question Answering Over Knowledge Bases in Oncology EHR2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)10.1109/CBMS.2019.00102(501-506)Online publication date: Jun-2019
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Wang YGe ZYan HXu XXia Y(2019)Semantic locality‐based approximate knowledge graph queryConcurrency and Computation: Practice and Experience10.1002/cpe.534531:24Online publication date: 17-May-2019
https://doi.org/10.1002/cpe.5345

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Reformulation-Based Query Answering for RDF Graphs with RDFS Ontologies

Interactive Spatial Keyword Querying with Semantics

Natural Language Question/Answering: Let Users Talk With The Knowledge Graph

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