Improving the Spatial Keyword Preference Query with Linked Open Data

Abstract

This paper presents a Spatial Keyword Preference Query (SKPQ) enhanced by Linked Open Data. This query selects objects based on the textual description of features in their neighborhood. The spatial relationship between objects and features is explored by the SKPQ using a Spatial Inverted Index. In our approach, the spatial relationship is explored using SPARQL. However, the main benefit of using SPARQL is obtained by measuring the textual relevance between features’ description and user’s keywords. The object description in Linked Open Data is much richer than traditional spatial databases, which leads to a more precise similarity measure than the one employed in the traditional SKPQ. We present an enhanced SKPQ and two experimental evaluations of the proposed approach, comparing it with the traditional SKPQ. The first conducted experiment indicate a relative NDCG improvement of the proposed approach over the traditional SKPQ of 20% when using random query keywords. The second experiment shows that using real query keywords, our approach obtained a significant increase in the MAP score.