Ontology-based recommender system for information support in knowledge-intensive processes

Knowledge-intensive processes are difficult to support because of their complexity, high variability and unpredictable information requirements. Therefore such process types are handled manually by knowledge workers with expertise in the domain. Yet to make informed decisions, knowledge workers require a multitude of domain specific, case-related information. This often leads to a time-consuming search for information and knowledge required to address the issues occurring in the case. To reduce the time spent searching for information, we propose an ontology-based recommender system that provides case-related information based on documents gathered in accumulated similar cases. The recommender system builds models of domain specific concepts for past cases as well as for the current case, which are used for case similarity calculation. To evaluate the performance of parts of our approach we used the OHSUMED document collection and compared the cosine similarity measure of ontological case model against textual case model.