Extracting knowledge from web communities and linked data for case-based reasoning systems

Web communities and the Web 2.0 provide a huge amount of experiences and there has been a growing availability of Linked Open Data. Making experiences and data available as knowledge to be used in case-based reasoning CBR systems is a current research effort. The process of extracting such knowledge from the diverse data types used in web communities, to transform data obtained from Linked Data sources, and then formalising it for CBR, is not an easy task. In this paper, we present a prototype, the Knowledge Extraction Workbench KEWo, which supports the knowledge engineer in this task. We integrated the KEWo into the open-source case-based reasoning tool myCBR Workbench. We provide details on the abilities of the KEWo to extract vocabularies from Linked Data sources and generate taxonomies from Linked Data as well as from web community data in the form of semi-structured texts.

[1]  Barry Smyth,et al.  Case-based recommender systems , 2005, The Knowledge Engineering Review.

[2]  Henrik Eriksson,et al.  The evolution of Protégé: an environment for knowledge-based systems development , 2003, Int. J. Hum. Comput. Stud..

[3]  Armin Stahl,et al.  Learning of knowledge-intensive similarity measures in case-based reasoning , 2004 .

[4]  Enric Plaza,et al.  Principle and Praxis in the Experience Web : A Case Study in Social Music , 2009 .

[5]  Ralph Bergmann,et al.  Structural Case-Based Reasoning and Ontology-Based Knowledge Management: A Perfect Match? , 2003, J. Univers. Comput. Sci..

[6]  T. Roth-Berghofer,et al.  Rapid Prototyping of CBR Applications with the Open Source Tool myCBR , 2008 .

[7]  D. Song,et al.  Reuse of search experience for resource transformation , 2009 .

[8]  Juan A. Recio-García,et al.  Extending CBR with Multiple Knowledge Sources from Web , 2010, ICCBR.

[9]  Christian Severin Sauer,et al.  Integration of Linked Open Data in Case-Based Reasoning Systems , 2010, LWA.

[10]  Christian Severin Sauer,et al.  Web Community Knowledge Extraction for myCBR 3 , 2011, SGAI Conf..

[11]  Michael Grüninger,et al.  Introduction , 2002, CACM.

[12]  Pedro A. González-Calero,et al.  JColibri: An Object-Oriented Framework for Building CBR Systems , 2004, ECCBR.

[13]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[14]  Klaus-Dieter Althoff,et al.  Extraction of Adaptation Knowledge from Internet Communities , 2009, LWA.

[15]  Tim Berners-Lee,et al.  Linked Data - The Story So Far , 2009, Int. J. Semantic Web Inf. Syst..

[16]  T. Roth-Berghofer,et al.  Explanation Capabilities of the Open Source Case-Based Reasoning Tool myCBR , 2008 .

[17]  Kenneth Ward Church,et al.  Word Association Norms, Mutual Information, and Lexicography , 1989, ACL.

[18]  Ralph Bergmann,et al.  Experience Management: Foundations, Development Methodology, and Internet-Based Applications , 2002 .

[19]  Gregory Piatetsky-Shapiro,et al.  The KDD process for extracting useful knowledge from volumes of data , 1996, CACM.

[20]  Marco Antonio Gómez-Martín,et al.  Extending jCOLIBRI for Textual CBR , 2005, ICCBR.

[21]  Andreas Dengel,et al.  Case Acquisition from Text: Ontology-Based Information Extraction with SCOOBIE for myCBR , 2010, ICCBR.

[22]  J. Jenkins,et al.  Word association norms , 1964 .

[23]  Jihie Kim,et al.  An intelligent discussion-bot for answering student queries in threaded discussions , 2006, IUI '06.

[24]  Klaus-Dieter Althoff,et al.  Deriving case base vocabulary from web community data , 2010 .

[25]  Pierre-Antoine Champin,et al.  The case-based experience web , 2009 .

[26]  Danah Boyd,et al.  Social network sites: definition, history, and scholarship , 2007, IEEE Engineering Management Review.