Annotated RDF

Udrea, Octavian; Recupero, Diego Reforgiato; Subrahmanian, V. S.

doi:10.1007/11762256_36

Cited by 19 publications

(22 citation statements)

References 12 publications

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“…The ordering is given by the set inclusion operator ⊆ [12]. • Timestamp labels L is the set N of natural numbers and is the usual numerical ordering [12]. In the first case, there is no relation among labels 1 , and we will see how in that case statements with different labels are completely unrelated.…”

Section: Labeling Domainsmentioning

confidence: 99%

“…A work introducing partial orders in RDF is annotated RDF [12]. Annotated RDF extends a fragment of RDF Schema with annotation taken from a partially ordered set with a bottom value.…”

Section: Uncertainty In Logical Languagesmentioning

confidence: 99%

“…The interval [x, y] denotes the set of all natural numbers t such that x ≤ t ≤ y. The ordering is given by the set inclusion operator ⊆ [12]. • Timestamp labels L is the set N of natural numbers and is the usual numerical ordering [12].…”

Section: Labeling Domainsmentioning

confidence: 99%

“…al. [12]; annotated RDF is defined by scratch, using simplified definitions compared to RDF semantics [1]: we will instead follow the standard RDF model theory.…”

Section: Labeled Rdfmentioning

confidence: 99%

“…We could for example state that the Acme CEO is a more reliable source than a generic Acme employee, or that a Regent of Atlantide University is more reliable than a student of Atlantide University. It is not clear which relative order there is between, say, Acme CEO and Regent of Atlantide University; indeed, it is common to have only a partial order among sources.A work introducing partial orders in RDF is annotated RDF [12]. Annotated RDF extends a fragment of RDF Schema with annotation taken from a partially ordered set with a bottom value.…”

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confidence: 99%

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Labeled RDF

Mazzieri

Dragoni

2010

International Conference on Fuzzy Systems

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There are different proposals to extend RDF model theory allowing a treatment of uncertainty within the language. This works proposes a unified approach for RDF graph and statement labeling. This approach is more general than fuzzy and temporal labels and can be used to label RDF graphs with membership degrees, timestamps, or with a combination of labelings. Starting from a definition of labeled sets and labeled logic, we define labeled RDF and labeled RDF Schema. A partial ordering among labels allows qualitative treatment of uncertainty. I. UNCERTAINTY IN LOGICAL LANGUAGESRDF model theory [1], like classical logic, is based on the strongest simplification about truth: a sentence can be either true of false. This simplification of reality is indeed the strongest conceivable simplification. It is possible to better approximate shades of truth allowing more than two values.Three valued logic [2] allows a truth value to be undefined or undetermined, and imposes a rule of regularity: if some undefined propositional letter p is given a value of either true or false, then the value of any formula using p should never change from true to false or from false to true [3].Four valued logic [4] distinguishes uncertainty due to undefined truth values to uncertainty due to inconsistent truth values. The four truth values, denoted as t (true), f (false), ⊥ (undetermined), and (inconsistent) are partially ordered according to truth (t is "more true" than f , whereas and ⊥ have intermediate values), and according to amount of knowledge (⊥ has the minimum amount, the maximum, and t and f have intermediate values). Four-valued logic allows paraconsistent reasoning, i.e. reasoning in presence of inconsistencies; an application is the mapping of a description logic-based OWL ontology to an ontology in a fourvalued description logic, allowing to reason with inconsistent ontologies [5]. Four-valued logic can further be generalized to bilattice-based logic [6], where the set of truth value is a lattice with two partial orders.Annotated logic programs [7] contain labeled clauses; labels forms a lattice. Annotated logic programs generalize to a lattice of labels the concept of quantitative deduction [8], where each clause receive a numerical attenuation value.Fuzzy logic is a logic where a proposition can be true or false to some degree. Extending RDF with a fuzzy logic semantics [9][10] allows to attach a degree to each assertion. Truth degrees are given by numbers, so each degree is comparable with any other degree, i.e. they have a total ordering. ).In a previous work [9] we introduced a fuzzy semantics for RDF and RDF Schema. The fuzzy semantics allows to deal with the kind of uncertainty due to vagueness. Although one of the motivating use case of fuzzy RDF was the encoding of trust metadata [11], the use of fuzzy membership values to express trustworthiness is not appropriate. When the extralogic value attached to a tripe is related to provenance, a numerical value is difficult to interpret. While it is true that we can genericall...

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Section: Labeling Domainsmentioning

confidence: 99%

“…A work introducing partial orders in RDF is annotated RDF [12]. Annotated RDF extends a fragment of RDF Schema with annotation taken from a partially ordered set with a bottom value.…”

Section: Uncertainty In Logical Languagesmentioning

confidence: 99%

Section: Labeling Domainsmentioning

confidence: 99%

“…al. [12]; annotated RDF is defined by scratch, using simplified definitions compared to RDF semantics [1]: we will instead follow the standard RDF model theory.…”

Section: Labeled Rdfmentioning

confidence: 99%

mentioning

confidence: 99%

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Labeled RDF

Mazzieri

Dragoni

2010

International Conference on Fuzzy Systems

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Mapping fuzzy RDF(S) into fuzzy object‐oriented databases

Fan

2019

Int J Intell Syst

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The Resource Description Framework (RDF) and RDF Schema (RDFS), being a recommendation of World Wide Web Consortium (W3C), have been widely used to exchange and reason information about resources on the web. It is a common case that information about resources may be uncertain under an environment of open web. To deal with uncertainties in resource information, we propose, in this paper, a general fuzzy RDF(S) model, which contains a fuzzy RDFS layer and a fuzzy RDF layer. In particular, we investigate how to formally map the fuzzy RDF(S) model to the fuzzy object‐oriented database model in the paper. We develop mapping rules and implement a prototype system to demonstrate the feasibility of our approach.

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Knowledge Representation and Rule Mining in Entity-Centric Knowledge Bases

Suchanek

Lajus

Boschin

et al. 2019

Lecture Notes in Computer Science

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Entity-centric knowledge bases are large collections of facts about entities of public interest, such as countries, politicians, or movies. They find applications in search engines, chatbots, and semantic data mining systems. In this paper, we first discuss the knowledge representation that has emerged as a pragmatic consensus in the research community of entity-centric knowledge bases. Then, we describe how these knowledge bases can be mined for logical rules. Finally, we discuss how entities can be represented alternatively as vectors in a vector space, by help of neural networks.

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Annotated RDF

Cited by 19 publications

References 12 publications

Labeled RDF

Labeled RDF

Mapping fuzzy RDF(S) into fuzzy object‐oriented databases

Knowledge Representation and Rule Mining in Entity-Centric Knowledge Bases

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