Result Diversity for RDF Search
Hiba Arnaout, Shady Elbassuoni · 2016
RDF repositories are typically searched using triple-pattern queries. Often, triple-pattern queries will return too many results, making it difficult for users to find the most relevant ones. To remedy this, some recent works have proposed relevance-based ranking-models for triple-pattern queries. However it is often the case that the top-ranked results are homogeneous. In this paper, we propose a framework to diversify the results of triple-pattern queries over RDF datasets. We first define different notions for result diversity in the setting of RDF. We then develop an approach for result diversity based on the Maximal Marginal Relevance. Finally, we develop a diversity-aware evaluation metric based on the Discounted Cumulative Gain and use it on a benchmark of 100 queries over DBPedia.