Automatic Feeding of an Innovation Knowledge Base Using a Semantic Representation of Field Knowledge

In this paper, by considering a particular application field, the innovation, we propose an automatic system to feed an innovation knowledge base (IKB) starting from texts located on the Web.

To facilitate the extraction of concepts from texts we distinguished in our work two knowledge types: primitive knowledge and definite knowledge. Each one is separately represented. Primitive knowledge is directly extracted from natural language texts and temporally organized in a specific base called TKB (Temporary Knowledge Base). The entry of the base IKB is the knowledge filtered from the TKB by some specified rules. After each filtering step, the TKB is emptied for starting new extractions from other texts sources.

The filtering rules are specified using variables representing interesting concepts. Their specifications result from the semantics of the innovation operators involved in the innovation process. The variables are initiated from a semantic representation of the operators. The content of the base IKB can be displayed as text annotations. Hence the feeding system is coupled with a user interface allowing the exploration of these annotations through their dynamic insertion in the associated texts.

In this paper, we present the application field and our approach for representing and for feeding the IKB innovation base. We also provide a number of experiment results and we indicate work we plan to undertake in order to improve our system.