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

Sauer, Christian and Roth-Berghofer, Thomas (2012) Solution mining for specific contextualised problems: towards an approach for experience mining. In: Proceedings of the 21st International Conference on World Wide Web, 16 Apr 2012, Lyon, France.

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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.

Item Type: Conference or Workshop Item (Paper)
ISBN: 9781450312301
Identifier: 10.1145/2187980.2188193
Page Range: pp. 729-738
Identifier: 10.1145/2187980.2188193
Additional Information: © 2012 ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 21st International Conference on World Wide Web, https://doi.org/10.1145/2187980.2188193.
Keywords: Experience Web, Experience Mining, Solution Mining, Context
Subjects: Computing
Depositing User: Christian Sauer
Date Deposited: 26 May 2016 13:02
Last Modified: 04 Nov 2024 12:52
URI: https://repository.uwl.ac.uk/id/eprint/2177

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