Matching Human Actors Based on Their Texts: Design and Evaluation of an Instance of the ExpertFinding Framework

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dc.contributor.authorReichling, Tim
dc.contributor.authorSchubert, Kai
dc.contributor.authorWulf, Volker
dc.date.accessioned2023-06-08T11:43:57Z
dc.date.available2023-06-08T11:43:57Z
dc.date.issued2005
dc.description.abstractBringing together human actors with similar interests, skills or expertise is a major challenge in community-based knowledge management. We believe that writing or reading textual documents can be an indicator for a human actor's interests, skills or expertise. In this paper, we describe an approach of matching human actors based on the similarity of text collections that can be attributed to them. By integrating standard methods of text analysis, we extract and match user profiles based on a large collection of documents. We present an instance of the ExpertFinder Framework which measures the similarity of these profiles by means of the Latent Semantic Indexing (LSI) algorithm. The quality of the algorithmic approach was evaluated by comparing its results with judgments of different human actors.en
dc.identifier.doi10.1145/1099203.1099213
dc.identifier.urihttps://dl.eusset.eu/handle/20.500.12015/4855
dc.language.isoen
dc.publisherAssociation for Computing Machinery
dc.relation.ispartofProceedings of the 2005 ACM International Conference on Supporting Group Work
dc.subjectcommunity building
dc.subjectexpertise sharing
dc.subjectuser profiling
dc.subjectkeyword extraction
dc.subjectknowledge management
dc.subjectlatent semantic indexing
dc.titleMatching Human Actors Based on Their Texts: Design and Evaluation of an Instance of the ExpertFinding Frameworken
gi.citation.publisherPlaceNew York, NY, USA
gi.citation.startPage61–70
gi.conference.locationSanibel Island, Florida, USA

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