Semantic search for context-aware learning

The thirst for information in complex working environments calls for intelligent systems which optimally assist the user, i.e. which offer the user the most relevant information. The aim is to decrease the time the user has to spend on his hunt for information and to offer him the best fitting help and learning material in an on-demand manner. We present an approach for the semantic retrieval of help and learning material which takes the working context into account. Based on the semantic structure of an ontology with attached binding weights a context-aware ranking of help and learning material is generated. The semantic search results fit better to the learner's actual situation than e.g. a pure full-text search, because the underlying ontology-based retrieval is aware of relations in the search domain and uses this knowledge in a way aligned to the learning process as well as to the specific domain. The results of the semantic search are presented for an application scenario in radar-based image interpretation. The advantages of the semantic approach are shown by a comparison with a state-of-the-art full-text search engine.