On Addressing the Empty Answer Problem in Uncertain Knowledge Bases

Abstract

Recently, several large Knowledge Bases (KBs) have been constructed by mining the Web for information. As an increasing amount of inconsistent and non-reliable data are available, KBs facts may be uncertain and are then associated with an explicit certainty degree. When querying these uncertain KBs, users seek high quality results i.e., results that have a certainty degree greater than a given threshold α. However, as they usually have only a partial knowledge of the KBs contents, their queries may be failing i.e., they return no result for the desired certainty. To prevent this frustrating situation, instead of returning an empty set of answers, our approach explains the reasons of the failure with a set of αMinimal Failing Subqueries (αMFSs), and computes alternative relaxed queries, called αMaXimal Succeeding Subqueries (αXSSs), that are as close as possible to the initial failing query. Moreover, as the user may not always be able to provide an appropriate threshold α, we propose two algorithms to compute the αMFSs and αXSSs for other thresholds. Our experiments on the WatDiv benchmark show the relevance of our algorithms compared to a baseline method.

Query answering over uncertain RDF knowledge bases: explain and obviate unsuccessful query results

“…This paper is an extension of our earlier conference work [14]. We have substantially developed, revised and improved our proposal.…”

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.…”

“…-Benedetti and Bergamaschi [17] present a system (LODeX) that allows users to explore SPARQL endpoints more easily through a formal model defined over the endpoint schema; they show that LODeX is able to generate 77.6% of the 5 million queries contained in the original LSQ dataset. -Dellal et al [31] proposes query relaxation methods for queries with empty results, based on finding minimal failing subqueries (generating empty results) and maximal succeeding subqueries (generating non-empty results) to aid the user [37]. The paper refers to LSQ to establish that queries with empty results are common in practice.…”

Borders of Theories for Cooperative Querying over Uncertain Databases

“…This work was adapted to the RDF context by Fokou et al [5], for which they proposed the LBA approach to compute both MFSs and XSSs. LBA was then extended in αLBA [6] to take into account uncertain RDF data. McSherry [7] also proposed an approach (abbreviated as MCS in this paper) based on the exploration of the subquery lattice to compute MFSs, for use during interactive query relaxation sessions.…”

“…Moreover, relational algebra operators are easily extended to this model [12]. [6] Uncertain RDF multiple lattice MFS and XSS MCS [7] Recom. Syst.…”