A General Framework for Representing, Reasoning and Querying with Annotated Semantic Web Data

Zimmermann, Antoine; Lopes, Nuno; Polleres, Axel; Straccia, Umberto

doi:10.2139/ssrn.3198940

Cited by 31 publications

(62 citation statements)

References 37 publications

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“…The literature describes several approaches to extract data provenance/annotated information from RDF(S) data [6,4,3,12,2]. A first major distinction is that we extract how-provenance instead of only why-provenance 10 of [6,4,3,2]. Both [6,3] address the problem of extracting data provenance for RDF(S) entailed triples, but do not support SPARQL.…”

Section: Discussionmentioning

confidence: 99%

“…The theory developed in [4] implements the difference operator using a negation, but it does not handle duplicate solutions according to the semantics of SPARQL because of idempotence of sum; additionally, the proposed difference operator to handle why-provenance discards the information in the right hand argument. The most complete work is [2] which develops a framework for annotated Semantic Web data, supporting RDFS entailment and providing a query language extending many of the SPARQL features in order to deal with annotated data, exposing annotations at query level via annotation variables, and including aggregates and subqueries (but not property path patterns). However, the sum operator is idempotent in order to support RDFS entailment, and by design the UNION operator is not interpreted in the annotation domain.…”

Section: Discussionmentioning

confidence: 99%

“…The capability of extracting full data how-provenance for SPARQL semantics as prescribed in [12] has been shown possible with our work, refuting their claim that existing algebras could not be used for SPARQL. Our approach, like [12], rests on a translation of SPARQL into annotated relational algebra contrasting with the abstract approach of [6,4,3,2]. The authors in [12] argue that this translation process does not affect the output provenance information for the case of (positive) SPARQL.…”

Section: Discussionmentioning

confidence: 99%

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Provenance for SPARQL Queries

Damásio

Analyti

Antoniou

2012

The Semantic Web – ISWC 2012

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Determining trust of data available in the Semantic Web is fundamental for applications and users, in particular for linked open data obtained from SPARQL endpoints. There exist several proposals in the literature to annotate SPARQL query results with values from abstract models, adapting the seminal works on provenance for annotated relational databases. We provide an approach capable of providing provenance information for a large and significant fragment of SPARQL 1.1, including for the first time the major non-monotonic constructs under multiset semantics. The approach is based on the translation of SPARQL into relational queries over annotated relations with values of the most general m-semiring, and in this way also refuting a claim in the literature that the OPTIONAL construct of SPARQL cannot be captured appropriately with the known abstract models.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Provenance for SPARQL Queries

Damásio

Analyti

Antoniou

2012

The Semantic Web – ISWC 2012

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“…Hartig defined semantics for fuzzy RDF graphs formed by associating trust degree in triplets, and they proposed an evaluation algorithm for fuzzy RDF data with tSPARQL. Zimmermann et al described a generic framework for representing and reasoning with annotated Semantic Web data. The authors formalized the annotated language, the corresponding deductive system and address the query answering problem.…”

Section: Related Studymentioning

confidence: 99%

A method for fuzzy quantified querying over fuzzy Resource Description Framework graph

2019

Int J Intell Syst

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Vagueness (or imprecision) naturally arises in real‐world Resource Description Framework (RDF) data. The way we query RDF data is a crucial subject because of the vagueness and the wide connectivity of such data. Fuzzy quantified queries have been studied over several types of data and allow to sum up large volumes of data in a very intuitive manner. However, there are a few works that have been led on RDF graph database. In this study, we dealt with a specific type of fuzzy quantified queries in the context of a (fuzzy) RDF graph database. We first show how these queries can be expressed and interpreted in a quantified graph pattern, which is an extension of graph patterns by supporting linguistic quantifier on edges. A query processing strategy based on subgraph matching queries is also proposed. Then, we develop an algorithm for evaluating quantified RDF graph patterns, that is, enumerating all RDF isomorphism from the given RDF graph patterns into the data graph. The algorithm is based on a backtracking strategy, which incrementally finds partial solutions by adding joinable candidate vertices or abandoning them when it determines they cannot be completed. Finally, we perform some experiments to study its performances. The results of these experiments show that the method of dealing with fuzzy quantification in a query is feasible.

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“…More precisely, in fuzzy RDF, triples are annotated with a degree of truth in [0, 1]. Zimmermann et al (2011) introduced a general framework for representing, reasoning, and querying with annotated Semantic Web data and provided a generic method for combining multiple annotation domains allowing to represent, for example, temporally annotated fuzzy RDF. Also, Lv et al (2008) proposed a quite general fuzzy extension of the RDF including fuzzy RDF syntax and fuzzy RDF semantics.…”

Section: Representation Languages Of Fuzzy Ontologiesmentioning

confidence: 99%

A survey on fuzzy ontologies for the Semantic Web

Zhang¹,

Cheng²,

Ma³

2016

The Knowledge Engineering Review

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Ontology, as a standard (World Wide Web Consortium recommendation) for representing knowledge in the Semantic Web, has become a fundamental and critical component for developing applications in different real-world scenarios. However, it is widely pointed out that classical ontology model is not sufficient to deal with imprecise and vague knowledge strongly characterizing some real-world applications. Thus, a requirement of extending ontologies naturally arises in many practical applications of knowledge-based systems, in particular the Semantic Web. In order to provide the necessary means to handle such vague and imprecise information there are today many proposals for fuzzy extensions to ontologies, and until now the literature on fuzzy ontologies has been flourishing. To investigate fuzzy ontologies and more importantly serve as helping readers grasp the main ideas and results of fuzzy ontologies, and to highlight an ongoing research on fuzzy approaches for knowledge semantic representation based on ontologies, as well as their applications on various domains, in this paper, we provide a comprehensive overview of fuzzy ontologies. In detail, we first introduce fuzzy ontologies from the most common aspects such as representation (including categories, formal definitions, representation languages, and tools of fuzzy ontologies), reasoning (including reasoning techniques and reasoners), and applications (the most relevant applications about fuzzy ontologies). Then, the other important issues on fuzzy ontologies, such as construction, mapping, integration, query, storage, evaluation, extension, and directions for future research, are also discussed in detail. Also, we make some comparisons and analyses in our whole review.ontology definition languages, such as RDF(S), SHOE, OIL, DAML, DAML + OIL, and web ontology language (OWL), have been developed over the past years (Horrocks et al., 2003). The OWL (Smith et al., 2004) and its successor OWL 2 (Cuenca Grau et al., 2008) is the W3C recommended ontology representation language. The theoretical underpinnings of OWL and ontologies are strongly based on Description Logics (DLs), a subset of first-order logic especially suitable for representing structured knowledge (Horrocks & Sattler, 2001;Baader et al., 2003;Horrocks et al., 2003).Although ontology is a quite expressive formalism, it features limitations, mainly with what can be said about fuzzy information. Nowadays, in ontology-based and many applications information is often vague and imprecise. This is a well-known problem especially for semantics-based applications of the Semantic Web, such as knowledge management, e-commerce, and web portals. For example, a task like a 'doctor appointment' could look like: 'Make me an appointment with a doctor close to my home not too early and of good references' (Stoilos et al., 2010). The conceptual formalism supported by typical ontology may not be sufficient to represent such information and knowledge. Therefore, many proposals have attempted to apply different formalisms...

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A General Framework for Representing, Reasoning and Querying with Annotated Semantic Web Data

Cited by 31 publications

References 37 publications

Provenance for SPARQL Queries

Provenance for SPARQL Queries

A method for fuzzy quantified querying over fuzzy Resource Description Framework graph

A survey on fuzzy ontologies for the Semantic Web

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