An Ontology-Based Interactive System for Understanding User Queries.

The use of ontologies in applications like dialogue systems, question-answering or decision-support is gradually gaining attention. In such applications, keyword-based user queries are mapped to ontology entities and then the respective application logic is activated. This task is not trivial as user queries may often be vague and imprecise or simply don't match the entities recognised by the application. This is for example the case in symptom-checking dialogue systems where users can enter text like "I am not feeling well", "I sleep terribly", and more, which cannot be directly matched to entities found in formal medical ontologies. In the current paper we present a framework for automatically building a small dialogue for the purposes of bridging the gap between user queries and a set of pre-defined (target) ontology concepts. We show how we can use the ontology and statistical techniques to select an initial small set of candidate concepts from the target ones and how these can then be grouped into categories using their properties in the ontology. Using these groups we can ask the user questions in order to try and reduce the set of candidates to a single concept that captures the initial user intention. The effectiveness of this approach is hindered by well-known underspecification of ontologies which we address by a concept enrichment pre-processing step based on information extraction techniques. We have instantiated our framework and performed a preliminary evaluation largely motivated by a real-world symptom-checking application obtaining encouraging results.