in specific scientific discourse contexts that can be precisely defined and used as pat terns for topic extraction The remainder of the paper is structured as follows In section 2 we describe related work in competency management and argue for an approach based on natural lan- guage processing and ontology modeling. We describe our specific approach to topic extraction for competency management in detail in section 3. The paper then contin ues with the description of an experiment that we performed on topic extraction for competency management in our own organization, DFKI. Finally, we conclude the paper with some conclusions that can be drawn from our research and ideas for future work that arise from these 2 Related work Competency management is a growing area of knowledge management that is con cerned with the"identification of skills, knowledge, behaviors, and capabilities needed to meet current and future personnel selection needs, in alignment with the differentia tions in strategies and organizational priorities. [1] Our particular focus here is on aspects of competency management relating to the identification and management of nowledge about scientific topics and technologies, which is at the basis of compe- tency management. Most of the work on competency management has been focused on the develop ment of methods for the identification, modeling, and analysis of skills and skills gaps and on training solutions to help remedy the latter. An important initial step in this process is the identification of skills and knowledge of interest, which is mostly done through interviews, surveys and manual analysis of existing competency models. Re- cently, ontology-based approaches have been proposed that aim at modeling the do main model of particular organization types(e.g. computer science, health-care) through formal ontologies, over which matchmaking services can be defined for bring ing together skills and organization requirements(e.g. [213]) The development of formal ontologies for competency management is important but there is an obvious need for automated methods in the construction and dynamic maintenance of such ontologies. Although some work has been done on developing automated methods for competency management through text and web mining(e.g. [4) this is mostly restricted to the extraction of associative networks between people according to documents or other data they are associated with. Instead, for the purpose of automated and dynamic support of competency management a richer analysis of competencies and semantic relations between them is needed, as can be extracted from text through natural language processing 3 Approach Our approach towards the automatic construction and dynamic maintenance of on tologies for competency management is based on the extraction of relevant competenin specific scientific discourse contexts that can be precisely defined and used as pat￾terns for topic extraction. The remainder of the paper is structured as follows. In section 2 we describe related work in competency management and argue for an approach based on natural lan￾guage processing and ontology modeling. We describe our specific approach to topic extraction for competency management in detail in section 3. The paper then contin￾ues with the description of an experiment that we performed on topic extraction for competency management in our own organization, DFKI. Finally, we conclude the paper with some conclusions that can be drawn from our research and ideas for future work that arise from these. 2 Related Work Competency management is a growing area of knowledge management that is con￾cerned with the “identification of skills, knowledge, behaviors, and capabilities needed to meet current and future personnel selection needs, in alignment with the differentia￾tions in strategies and organizational priorities.” [1] Our particular focus here is on aspects of competency management relating to the identification and management of knowledge about scientific topics and technologies, which is at the basis of compe￾tency management. Most of the work on competency management has been focused on the develop￾ment of methods for the identification, modeling, and analysis of skills and skills gaps and on training solutions to help remedy the latter. An important initial step in this process is the identification of skills and knowledge of interest, which is mostly done through interviews, surveys and manual analysis of existing competency models. Re￾cently, ontology-based approaches have been proposed that aim at modeling the do￾main model of particular organization types (e.g. computer science, health-care) through formal ontologies, over which matchmaking services can be defined for bring￾ing together skills and organization requirements (e.g. [2], [3]). The development of formal ontologies for competency management is important, but there is an obvious need for automated methods in the construction and dynamic maintenance of such ontologies. Although some work has been done on developing automated methods for competency management through text and web mining (e.g. [4]) this is mostly restricted to the extraction of associative networks between people according to documents or other data they are associated with. Instead, for the purpose of automated and dynamic support of competency management a richer analysis of competencies and semantic relations between them is needed, as can be extracted from text through natural language processing. 3 Approach Our approach towards the automatic construction and dynamic maintenance of on￾tologies for competency management is based on the extraction of relevant competen-