Diversifying the Results of Keyword Queries on Linked Data

Keyword search is a popular technique for retrieving information from the ever growing repositories of RDF graph data on the Web. However, keyword queries are inherently ambiguous, resulting in an overwhelming number of candidate results. These results correspond to different interpretations of the query. Most of the current keyword search approaches ignore the diversity of the result interpretations and might fail to provide a broad overview of the query aspects to the users who are interested in exploratory search. To address this issue, we introduce in this paper, a novel technique for diversifying keyword search results on RDF graph data. We generate pattern graphs which are structured queries corresponding to alternative interpretations of the given keyword query. We model the problem as an optimization problem aiming at selecting a set of k pattern graphs with maximum diversity. We devise a metric to estimate the diversity of a set of pattern graphs, and we design an algorithm that employs a greedy heuristic to generate a diverse list of k pattern graphs for a given keyword query.