Using BabelNet in bridging the gap between natural language queries and linked data concepts
Khadija M. Elbedweihy, Stuart N. Wrigley, Fabio Ciravegna, Ziqi Zhang · 2013
Many semantic search tool evaluations have reported a user preference for free natural language as a query input approach as opposed to controlled or view-based inputs. Although the exibility offered by this approach is a significant advantage, it can also be a major difficulty. Allowing users complete freedom in the choice of terms increases the difficulty for the search tools to match terms with the underlying data. This causes either a mismatch which affects precision, or a missing match which affects recall. In this paper, we present an empirical investigation on the use of named entity recognition, word sense disambiguation, and ontology-based heuristics in an approach attempting to bridge this gap between user terms and ontology concepts, properties and entities. We use the dataset provided by the Question Answering over Linked Data (QALD-2) workshop in our analysis and tests.