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

Abstract

Bringing 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.