Topic Extraction from Scientific Literature for Competency Management Paul Buitelaar, Thomas Eigner DFKI Gmbh Language Technology Lab& Competence Center Semantic Web Stuhlsatzenhausweg 3 66123 Saarbrucken, Germany Abstract We describe an approach towards automatic, dynamic and time- critical support for competency management and expertise search through topic extraction from scientific publications. In the use case we present, we focus on the automatic extraction of scientific topics and technologies from publicly available publications using web sites like Google Scholar. We discuss an ex- periment for our own organization, DFKI, as example of a knowledge organiza- tion. The paper presents evaluation results over a sample of 48 DFKI research ers that responded to our request for a-posteriori evaluation of automatically ex- racted topics. The results of this evaluation are encouraging and provided us with useful feedback for further improving our methods. The extracted topics can be organized in an association network that can be used further to analyze how competencies are interconnected, thereby enabling also a better exchange of expertise and competence between researche 1 Introduction Competency management, the identification and management of experts on and their knowledge in certain competency areas, is a growing area of research as knowl- edge has become a central factor in achieving commercial success. It is of fundamen tal importance for any organization to keep up-to-date with the competencies it covers, in the form of experts among its work force. Identification of experts will be based mostly on recruitment information, but this is not sufficient as competency coverage (competencies of interest to the organization) and structure(interconnections between competencies) change rapidly over time. The automatic identification of competency coverage and structure, e.g. from publications, is therefore of increasing importance, as this allows for a sustainable, dynamic and time-critical approach to competency management o In this paper we present a pattern-based approach to the extraction of competencies a knowledge-based research organization(scientific topics, technologies) from publicly available scientific publications. The core assumption of our approach is that such topics will not occur in random fashion across documents, but instead occur onlyTopic Extraction from Scientific Literature for Competency Management Paul Buitelaar, Thomas Eigner DFKI GmbH Language Technology Lab & Competence Center Semantic Web Stuhlsatzenhausweg 3 66123 Saarbrücken, Germany paulb@dfki.de Abstract We describe an approach towards automatic, dynamic and time￾critical support for competency management and expertise search through topic extraction from scientific publications. In the use case we present, we focus on the automatic extraction of scientific topics and technologies from publicly available publications using web sites like Google Scholar. We discuss an ex￾periment for our own organization, DFKI, as example of a knowledge organiza￾tion. The paper presents evaluation results over a sample of 48 DFKI research￾ers that responded to our request for a-posteriori evaluation of automatically ex￾tracted topics. The results of this evaluation are encouraging and provided us with useful feedback for further improving our methods. The extracted topics can be organized in an association network that can be used further to analyze how competencies are interconnected, thereby enabling also a better exchange of expertise and competence between researchers. 1 Introduction Competency management, the identification and management of experts on and their knowledge in certain competency areas, is a growing area of research as knowl￾edge has become a central factor in achieving commercial success. It is of fundamen￾tal importance for any organization to keep up-to-date with the competencies it covers, in the form of experts among its work force. Identification of experts will be based mostly on recruitment information, but this is not sufficient as competency coverage (competencies of interest to the organization) and structure (interconnections between competencies) change rapidly over time. The automatic identification of competency coverage and structure, e.g. from publications, is therefore of increasing importance, as this allows for a sustainable, dynamic and time-critical approach to competency management. In this paper we present a pattern-based approach to the extraction of competencies in a knowledge-based research organization (scientific topics, technologies) from publicly available scientific publications. The core assumption of our approach is that such topics will not occur in random fashion across documents, but instead occur only