Feature-based reformulation of entities in triple pattern queries

Abstract

Knowledge graphs encode uniquely identifiable entities to other entities or literal values by means of relationships, thus enabling semantically rich querying over the stored data. Typically, the semantics of such queries are often crisp thereby resulting in crisp answers. Query log statistics show that a majority of the queries issued to knowledge graphs are often entity centric queries. When a user needs additional answers the state-of-the-art in assisting users is to rewrite the original query resulting in a set of approximations. Several strategies have been proposed in past to address this. They typically move up the taxonomy to relax a specific element to a more generic element. Entities don't have a taxonomy and they end up being generalized. To address this issue, in this paper, we propose an entity centric reformulation strategy that utilizes schema information and entity features present in the graph to suggest rewrites. Once the features are identified, the entity in concern is reformulated as a set of features. Since entities can have a large number of features, we introduce strategies that select the top-k most relevant and informative ranked features and augment them to the original query to create a valid reformulation. We then evaluate our approach by showing that our reformulation strategy produces results that are more informative when compared with state-of-the-art.

LSQ 2.0: A linked dataset of SPARQL query logs

“…), and have updated the statistical analysis of the queries featured by LSQ to include the additional data provided by the new endpoints. -Since the initial release, LSQ has been used by a variety of diverse research works on SPARQL [2,3,11,14,15,17,18,21,22,26,[30][31][32]34,35,37,39,41,42,49,58,69,71,[74][75][76][78][79][80]83,[85][86][87][89][90][91]94,[97][98][99]102]. To exemplify the value of LSQ, we discuss the various ways in which the dataset has been used in these past years.…”

“…-Stegemann and Ziegler [89] propose new operators for the SPARQL language that allow for composing path queries more easily; the authors evaluated their approach with a user study and analysis of the extent to which their language is able to express the real-world queries found in LSQ. -Viswanathan et al [97] propose a different form of query relaxation, which generalises a specific resource to a variable on which specific restrictions are added that correspond to relevant characteristics of the resource; they use LSQ to understand how entities are queried in practice. -Potoniec [69] proposes an interactive system for learning SPARQL queries from positive and negative examples; 15 [99] present an approach for explaining missing results for a SPARQL query -based on answering "why-not" questions that ask why a specific result is not included -to help users refine their initial queries; the authors search LSQ for queries useful for their approach.…”