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 α. 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 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 three algorithms to compute the αMFSs and αXSSs 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.

SUKE: Embedding Model for Prediction in Uncertain Knowledge Graph

“…Therefore, during training, this study takes the confidence of unknown facts as the score of the evaluator and introduces dynamic parameters to improve the generalization ability of the model. The loss function of unknown facts is defined as (8).…”

“…For example, Probability KG Probase [7] provides the prior probability distribution of each concept behind the terms, which effectively supports short text understanding tasks involving disambiguation. In addition, [8] conducted related research on the question answering task of uncertain knowledge bases. Therefore, we should consider the uncertainty of knowledge when learning embedding.…”

Provenance-Aware Knowledge Representation: A Survey of Data Models and Contextualized Knowledge Graphs

“…RDF reification 19 refers to making an RDF statement about another RDF statement by instantiating the rdf: 15 A controlled vocabulary is a finite set of IRI symbols denoting concept names or classes (atomic concepts), role names, properties, and relationships (atomic roles), and individual names (entities), where these three sets are pairwise disjoint. 16 Ontologies are formal conceptualizations of a knowledge domain with complex relationships, and optionally complex rules, suitable for inferring new statements, thereby making implicit knowledge explicit. 17 RDF Schema, an extension of RDF's vocabulary for creating vocabularies, taxonomies, and thesauri; see https://www.w3.org/TR/rdfschema/ for reference.…”

“…These benefits make RDF appealing for a wide range of applications; however, RDF has shortcomings when it comes to encapsulating metadata to statements. With the proliferation of heterogeneous structured data sources, such as triplestores and LOD datasets, capturing data provenance, 12 i.e., the origin or source of data [7], and the technique used to extract it, is becoming more and more important, because it enables the verification of data, the assessment of reliability [8], the analysis of the processes that generated the data [9], decision support for areas such as cybersecurity [10,11], cyberthreat intelligence [12], and cyber-situational awareness [13], and helps express trustworthiness [14,15], uncertainty [16], and data quality [17]. Yet, the RDF data model does not have a built-in mechanism to attach provenance to triples or elements of triples.…”

“…This paper is a critical review of alternate approaches to capture provenance for RDF, demonstrates their use in, and provides their quantitative comparison for, the cybersecurity domain (which is known to be reliant on provenance) from various perspectives. Section 2 covers extensions of the standard RDF data model, state-of-the-art annotation frameworks, and purpose-built knowledge organization systems (KOS), including controlled vocabularies 15 and ontologies, 16 typically written in RDFS 17 and OWL, 18 respectively. Section 3 discusses the requirements of RDF triplestores, quadstores, and graph databases to be suitable for storing provenance-aware RDF data, and Sect.…”