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A semantic web of know-how: linked data for community-centric tasks

Authors:

Paolo Pareti,

Ewan Klein,

Adam BarkerAuthors Info & Claims

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

Pages 1011 - 1016

https://doi.org/10.1145/2567948.2578846

Published: 07 April 2014 Publication History

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Abstract

This paper proposes a novel framework for representing community 'know-how' on the Semantic Web. Procedural knowledge generated by web communities typically takes the form of natural language instructions or videos and is largely unstructured. The absence of semantic structure impedes the deployment of many useful applications, in particular the ability to discover and integrate know-how automatically. We discuss the characteristics of community know-how and argue that existing knowledge representation frameworks fail to represent it adequately. We present a novel framework for representing the semantic structure of community know-how and demonstrate the feasibility of our approach by providing a concrete implementation which includes a method for automatically acquiring procedural knowledge for real-world tasks.

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Garigliotti DBalog KHose KBjerva J(2023)Recommending tasks based on search queries and missionsNatural Language Engineering10.1017/S1351324923000219(1-25)Online publication date: 17-May-2023
https://doi.org/10.1017/S1351324923000219
Luo RZhu QChen QWang SWei ZSun WTang SDemartini GZuccon GCulpepper JHuang ZTong H(2021)Operation Diagnosis on Procedure GraphProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482157(3288-3292)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482157
Wang SPhan NWang YZhao YStorey MAdams BHaiduc S(2019)Extracting API tips from developer question and answer websitesProceedings of the 16th International Conference on Mining Software Repositories10.1109/MSR.2019.00058(321-332)Online publication date: 26-May-2019
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Index Terms

A semantic web of know-how: linked data for community-centric tasks
1. Information systems
  1. Information retrieval
    1. Search engine architectures and scalability
      1. Distributed retrieval
      2. Peer-to-peer retrieval
  2. Information storage systems
    1. Storage architectures
      1. Distributed storage

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Published In

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

April 2014

1396 pages

ISBN:9781450327459

DOI:10.1145/2567948

General Chair:
Chin-Wan Chung
Korea Advanced Institute of Science and Technology, Korea
,
Program Chairs:
Andrei Broder
Google Inc., USA
,
Kyuseok Shim
Seoul National University, Korea
,
Torsten Suel
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]

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

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Association for Computing Machinery

New York, NY, United States

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Published: 07 April 2014

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WWW '14: 23rd International World Wide Web Conference

April 7 - 11, 2014

Seoul, Korea

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Garigliotti DBalog KHose KBjerva J(2023)Recommending tasks based on search queries and missionsNatural Language Engineering10.1017/S1351324923000219(1-25)Online publication date: 17-May-2023
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Luo RZhu QChen QWang SWei ZSun WTang SDemartini GZuccon GCulpepper JHuang ZTong H(2021)Operation Diagnosis on Procedure GraphProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482157(3288-3292)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482157
Wang SPhan NWang YZhao YStorey MAdams BHaiduc S(2019)Extracting API tips from developer question and answer websitesProceedings of the 16th International Conference on Mining Software Repositories10.1109/MSR.2019.00058(321-332)Online publication date: 26-May-2019
https://dl.acm.org/doi/10.1109/MSR.2019.00058
Lagos NGallé MChernov ASándor Á(2017)Enriching how-to guides with actionable phrases and linked dataWeb Intelligence10.3233/WEB-17036415:3(189-203)Online publication date: 11-Aug-2017
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Guy IMejer ANus ARaiber FBarrett RCummings RAgichtein EGabrilovich E(2017)Extracting and Ranking Travel Tips from User-Generated ReviewsProceedings of the 26th International Conference on World Wide Web10.1145/3038912.3052632(987-996)Online publication date: 3-Apr-2017
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Wang RRho SChen BCai W(2017)Modeling of large-scale social network services based on mechanisms of information diffusionFuture Generation Computer Systems10.1016/j.future.2016.03.01874:C(291-301)Online publication date: 1-Sep-2017
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Chernov ALagos NGallé MSándor ÁBourdeau JHendler JNkambou RHorrocks IZhao B(2016)Enriching How-to Guides by Linking Actionable PhrasesProceedings of the 25th International Conference Companion on World Wide Web10.1145/2872518.2890585(939-944)Online publication date: 11-Apr-2016
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Yang ZNyberg EBaeza-Yates RLalmas MMoffat ARibeiro-Neto B(2015)Leveraging Procedural Knowledge for Task-oriented SearchProceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/2766462.2767744(513-522)Online publication date: 9-Aug-2015
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Pareti PTestu BIchise RKlein EBarker A(2015)A Linked Data Approach to Know-HowKnowledge Engineering and Knowledge Management10.1007/978-3-319-17966-7_24(168-171)Online publication date: 21-Apr-2015
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Pareti PTestu BIchise RKlein EBarker A(2014)Integrating Know-How into the Linked Data CloudKnowledge Engineering and Knowledge Management10.1007/978-3-319-13704-9_30(385-396)Online publication date: 2014
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Interlinking the Social Web with Semantics

Semantic web reasoners and languages

RDF, Jena, SparQL and the 'Semantic Web'

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