The SemSets model for ad-hoc semantic list search

The amount of semantic data on the web has been growing rapidly in recent years. One of the key challenges triggered by this growth is the ad-hoc querying, i.e., the ability to retrieve answers from semantic resources using natural language queries. This facilitates interaction with semantic resources for the users so they can benefit from the knowledge covered by semantic data without the complexities of semantic query languages. In this paper, we focus on semantic queries, where the aim is to retrieve objects belonging to a set of semantically related entities. An example of such an ad-hoc type query is "Apollo astronauts who walked on the Moon". In order to address the task, we propose the SemSets retrieval model that exploits and combines traditional document-based information retrieval, link structure of the semantic data and entity membership in semantic sets, in order to provide the answers. The novelty of the approach lies in the utilization of semantic sets, i.e., groups of semantically related entities. We propose two approaches to identify such semantic sets from the knowledge bases; the first one requires involvement of an expert user knowledgeable of the data set structure, the second one is fully automatic and provides results that are comparable with those delivered by the expert users. As demonstrated in the experimental evaluation, the proposed model has the state-of-the-art performance on the SemSearch2011 data set, which has been designed especially for the semantic list search evaluation.