Revamping question answering with a semantic approach over world knowledge

Abstract

Classic textual question answering (QA) approaches that rely on statistical keyword relevance scoring without exploiting semantic content are useful to a certain extent, but are limited to questions answered by a small text excerpt. With the maturation of Wikipedia and with upcoming projects like DBpedia, we feel that nowadays QA can adopt a deeper, semantic approach to the task, where answers can be inferred using knowledge bases to overcome the limitations of textual QA approaches. In GikiCLEF, a QA-flavoured evaluation task, the best performing systems followed a semantic approach. In this paper, we present our motivations for preferring semantic approaches to QA over textual approaches, with Wikipedia serving as a raw knowledge source.