Title
Query answering over uncertain RDF knowledge bases: explain and obviate unsuccessful query results
Abstract
Several large uncertain knowledge bases (KBs) are available on the Web where facts are associated with a 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 (alpha ). However, as they usually have only a partial knowledge of the KB contents, their queries may be failing i.e., they return no result for the desired certainty level. 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 (alpha )minimal failing subqueries ((alpha )MFSs) and computes alternative relaxed queries, called (alpha )maXimal succeeding subqueries ((alpha )XSSs), 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 (alpha ), we propose three algorithms to compute the (alpha )MFSs and (alpha )XSSs for other thresholds, which also constitutes a relevant feedback for the user. Multiple experiments with the WatDiv benchmark show the relevance of our algorithms compared to a baseline method.
Year
DOI
Venue
2019
10.1007/s10115-019-01332-7
Knowledge and Information Systems
Keywords
Field
DocType
Uncertain knowledge bases, RDF quad, SPARQL queries, Empty answers, Named graph, Reification, Quadstore
Data mining,Empty set,Certainty,Computer science,Theoretical computer science,Named graph,RDF
Journal
Volume
Issue
ISSN
61
3
0219-3116
Citations 
PageRank 
References 
0
0.34
21
Authors
5
Name
Order
Citations
PageRank
Ibrahim Dellal100.34
Stéphane Jean218823.51
Allel Hadjali339149.62
Brice Chardin482.82
Mickaël Baron58311.19