Querying for Provenance, Trust, Uncertainty and Other Meta Knowledge in RDF

Dividino, Renata; Sizov, Sergej; Staab, Steffen; Schueler, Bernhard

doi:10.2139/ssrn.3199429

Cited by 17 publications

(43 citation statements)

References 16 publications

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“…We next consider the semantics of SPARQL when RDF is adorned with trust information [Hartig 2008[Hartig , 2009Dividino et al 2009]. In this setting, for a given SPARQL query, the goal is to find which result mappings are trusted based on the trustworthiness of the input mappings.…”

Section: Sparql On Annotated Rdfmentioning

confidence: 99%

“…We remark that Dividino et al [2009] define p − fuzzy q as min( p, 1 − q). Although this definition collapses to − trust when p and q can only be 0 or 1, it is not compatible with the standard bag semantics.…”

Section: Sparql On Annotated Rdfmentioning

confidence: 99%

“…Toward this end, several models for annotated RDF data have been proposed [Carroll et al 2005;Mazzieri and Dragoni 2008;Hartig 2009;Dividino et al 2009;Huang and Liu 2009;Zimmermann et al 2012;Straccia 2013] in an attempt to represent various dimensions of data quality such as trust, truth of imprecise information, or the probability of the validity of the data. In all these cases, when annotated data are transformed through SPARQL queries, one needs to compute appropriate annotations for the query results.…”

Section: Introductionmentioning

confidence: 99%

“…In all these cases, when annotated data are transformed through SPARQL queries, one needs to compute appropriate annotations for the query results. For instance, in the case of trust assessment [Hartig 2008[Hartig , 2009Dividino et al 2009], the trustworthiness of query results is determined based on the trustworthiness of source datasets from which they were derived. Similarly, for uncertain and fuzzy datasets, the degrees of truth of query results are derived based on the degrees of truth associated with the original data [Dividino et al 2009;Huang and Liu 2009;Zimmermann et al 2012;Straccia 2013].…”

Section: Introductionmentioning

confidence: 99%

“…If we are only interested in one kind of annotations, and source annotations are static and common for all users as well as available at query evaluation time, such computations can be performed during query evaluation [Hartig 2008[Hartig , 2009Dividino et al 2009;Huang and Liu 2009;Zimmermann et al 2012;Straccia 2013].…”

Section: Introductionmentioning

confidence: 99%

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Algebraic Structures for Capturing the Provenance of SPARQL Queries

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HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Section: Sparql On Annotated Rdfmentioning

confidence: 99%

Section: Sparql On Annotated Rdfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Algebraic Structures for Capturing the Provenance of SPARQL Queries

Geerts

Unger

Karvounarakis

et al. 2016

J. ACM

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Explaining Axiom Pinpointing

Peñaloza

2019

Lecture Notes in Computer Science

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Axiom pinpointing refers to the task of finding the specific axioms in an ontology which are responsible for a consequence to follow. This task has been studied, under different names, in many research areas, leading to a reformulation and reinvention of techniques. In this work, we present a general overview to axiom pinpointing, providing the basic notions, different approaches for solving it, and some variations and applications which have been considered in the literature. This should serve as a starting point for researchers interested in related problems, with an ample bibliography for delving deeper into the details.

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Publishing Uncertainty on the Semantic Web: Blurring the LOD Bubbles

Djebri

Tettamanzi

Gandon

2019

Graph-Based Representation and Reasoning

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The open nature of the Web exposes it to the many imperfections of our world. As a result, before we can use knowledge obtained from the Web, we need to represent that fuzzy, vague, ambiguous and uncertain information. Current standards of the Semantic Web and Linked Data do not support such a representation in a formal way and independently of any theory. We present a new vocabulary and a framework to capture and handle uncertainty in the Semantic Web. First, we define a vocabulary for uncertainty and explain how it allows the publishing of uncertainty information relying on different theories. In addition, we introduce an extension to represent and exchange calculations involved in the evaluation of uncertainty. Then we show how this model and its operational definitions support querying a data source containing different levels of uncertainty metadata. Finally, we discuss the perspectives with a view on supporting reasoning over uncertain linked data.

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Querying for Provenance, Trust, Uncertainty and Other Meta Knowledge in RDF

Cited by 17 publications

References 16 publications

Algebraic Structures for Capturing the Provenance of SPARQL Queries

Algebraic Structures for Capturing the Provenance of SPARQL Queries

Explaining Axiom Pinpointing

Publishing Uncertainty on the Semantic Web: Blurring the LOD Bubbles

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