tutorial

Solution mining for specific contextualised problems: towards an approach for experience mining

Authors:

Christian Severin Sauer,

Thomas Roth-BerghoferAuthors Info & Claims

WWW '12 Companion: Proceedings of the 21st International Conference on World Wide Web

Pages 729 - 738

https://doi.org/10.1145/2187980.2188193

Published: 16 April 2012 Publication History

Abstract

In this paper we describe the task of automated mining for solutions to highly specific problems. We do so under the premise of mapping the split view on context, introduced by Brézillon and Pomerol, onto three different levels of abstraction of a problem domain. This is done to integrate the notion of activity or focus and its influence on the context into the mining for a solution. We assume that a problem's context describes key characteristics to be decisive criteria in the mining process to mine successful solutions for it. We further detail on the process of a chain of sub problems and their foci adding up to a meta problem solution and how this can used to mine for such solutions. Through a guiding example we introduce basic steps of the solution mining process and common aspects we deem interesting to be analysed closer in upcoming research on solution mining. We further examine the possible integration of these newly established outlines for automatic solution mining for highly specific problems into a SEASALTexp, a currently developed architecture for explanation-aware extraction and case-based processing of experiences from Internet communities. We thereby gained first insights in issues occurring while trying to integrate automatic solution mining.

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Cited By

Liu CZhou SLiu DLi JHuang ZMa X(2023)CoArgue : Fostering Lurkers’ Contribution to Collective Arguments in Community-based QA PlatformsProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580932(1-17)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580932
Liu CHuang ZLiu DZhou SPeng ZMa X(2022)PlanHelperProceedings of the ACM on Human-Computer Interaction10.1145/35555556:CSCW2(1-26)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555555
Xu ZDang Y(2021)Solution knowledge mining and recommendation for quality problem-solvingComputers & Industrial Engineering10.1016/j.cie.2021.107313(107313)Online publication date: Apr-2021
https://doi.org/10.1016/j.cie.2021.107313
Show More Cited By

Index Terms

Solution mining for specific contextualised problems: towards an approach for experience mining
1. Information systems
  1. Information systems applications

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cover image ACM Other conferences

WWW '12 Companion: Proceedings of the 21st International Conference on World Wide Web

April 2012

1250 pages

ISBN:9781450312301

DOI:10.1145/2187980

General Chairs:
Alain Mille
Université de Lyon, France
,
Fabien Gandon
INRIA, France
,
Jacques Misselis
HP, France
,
Program Chairs:
Michael Rabinovich
Case Western Reserve University, USA
,
Steffen Staab
University of Koblenz-Landau, Germany

Copyright © 2012 ACM.

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

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Published: 16 April 2012

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Univ. de Lyon

WWW 2012: 21st World Wide Web Conference 2012

April 16 - 20, 2012

Lyon, France

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

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Cited By

Liu CZhou SLiu DLi JHuang ZMa X(2023)CoArgue : Fostering Lurkers’ Contribution to Collective Arguments in Community-based QA PlatformsProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580932(1-17)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580932
Liu CHuang ZLiu DZhou SPeng ZMa X(2022)PlanHelperProceedings of the ACM on Human-Computer Interaction10.1145/35555556:CSCW2(1-26)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555555
Xu ZDang Y(2021)Solution knowledge mining and recommendation for quality problem-solvingComputers & Industrial Engineering10.1016/j.cie.2021.107313(107313)Online publication date: Apr-2021
https://doi.org/10.1016/j.cie.2021.107313
Kumar SChowdary C(2020)Semantic model to extract tips from hotel reviewsElectronic Commerce Research10.1007/s10660-020-09446-922:4(1059-1077)Online publication date: 13-Nov-2020
https://doi.org/10.1007/s10660-020-09446-9
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
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
https://dl.acm.org/doi/10.1145/3038912.3052632
Ryu JIhm HMyaeng SBenjamins Rd'Aquin MGordon A(2013)Automatic organization of human task goals for web-scale problem solving knowledgeProceedings of the seventh international conference on Knowledge capture10.1145/2479832.2479846(81-88)Online publication date: 23-Jun-2013
https://dl.acm.org/doi/10.1145/2479832.2479846

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