Explanation Retrieval in Semantic Networks - Understanding Spreading Activation based Recommendations

Spreading Activation is a well-known semantic search technique to determine the relevance of nodes in a semantic network. When used for decision support, meaningful explanations of semantic search results are crucial for the user’s acceptance and trust. Usually, explanations are generated based on the original network. Indeed, the data accumulated during the spreading activation process contains semantically extremely valuable information. Therefore, our approach exploits the so-called spread graph, a specific data structure that comprises the spreading progress data. In this paper, we present a three-step explanation retrieval method based on spread graphs. We show how to retrieve the most relevant parts of a network by minimization and extraction techniques and formulate meaningful explanations. The evaluation of the approach is then performed with a prototypical decision support system for automotive safety analyses.