Using Information Extraction Rules for Extending Domain Ontologies

In the FRODO project [1] we aim at the development of a “Framework for Distributed Organizational Memories” (OMs). We start with the observation that knowledge and expertise is always heavily distributed in an organization. We accept the fact that this is not an intermediary, imperfect state which should be overcome by a central, ontologically structured information system, but rather a natural and meaningful situation (because during the introduction of OM systems it is normal to start with small, focussed systems which should interoperate later; because much expertise is better to be created, hold, and maintained locally; or because in the case of interorganizational collaborations or virtual teams a deeper integration of information systems cannot be achieved). Hence, a main goal of the FRODO project is to develop a scalable, extensible OM middleware built for easy integration of new components and linking of collaborating components [2]. FRODO builds upon the KnowMore framework for contextually-aware, ontology-based OMs [3,4], but relaxes some constraints of the original model, especially the idea of a centralized OM using one overall set of organizational ontologies. Besides the technical provisions for such a distributed, highly dynamic environment, we lay special emphasis on considerations and methods which are necessary to realize such a scenario in industrial practice. In each industrial environment, besides the questions of smooth introduction of new technology regarding human factors and organizational processes, and besides the question of modeling tools and method support for knowledge (in particular ontologies for structuring OMs or parts of OMs) acquisition, at least two other factors are of utmost importance: One is the predominance of informal, i.e. essentially textbased, representations of knowledge. This is not only just a matter of fact, but really useful, because the cost of formalization is often not in the right relation to the potential benefits such that many informal parts of the scenario are economically reasonable [5]. One implication is that also methods for building formal models must be affordable. The other is the fact that ontologies are not a stand-alone component built once and then remaining untouched, but a living element in the overall scenario, used for different purposes, communicating with other system parts, and representing knowledge about a continuously changing world [10]. These two assumptions lead to two characteristics of our approach: