Querying knowledge graphs with extended property paths

Fionda, Valeria; Pirrò, Giuseppe; Consens, Mariano P.

doi:10.3233/sw-190365

Cited by 6 publications

(2 citation statements)

References 55 publications

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“…73 The remarkable thing is that the technology of semantic web wherein the machine and human-readable knowledge and linked data will reside for using them in Industry 4.0 will rely on the knowledge graph-based data-format (ie, concept mapping). 74,75 On the other hand, there are some disputed grounds. For example, consider the role of logical operations in knowledge formation.…”

Section: Revised Definition Of Knowledgementioning

confidence: 99%

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What is knowledge in Industry 4.0?

Ullah

2020

Engineering Reports

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Industry 4.0 relevant systems (eg, cyber‐physical systems, digital twins, and alike) need digitized knowledge to function. Before digitizing knowledge, a fundamental question arises: What is knowledge? In order to answer this question, this study first reviews the definitions of knowledge found in the extant literature of epistemology, engineering design, manufacturing, organization science, information science, and education science. Since the definitions reported so far are not succinct and suffer circularity, this study overcomes this by introducing a three‐element‐based definition of knowledge—a piece knowledge consists of three elements defined as claim, provenance, and inference. This results in four types of knowledge defined as definitional, deductive, inductive, and creative knowledge, and each type of knowledge is again divided into some categories. Some real‐life scenarios relevant to engineering design and manufacturing are used to clarify the proposed knowledge types/categories; the relevant pieces of knowledge are represented by knowledge graphs (concept maps) for the sake of digitization. The myriad proximal and distal relationships between knowledge and other relevant entities (human/machine learning, logical inferences, experimental data, analytical results, creative thinking, and cognitive reflections) become succinct and transparent due to the proposed definition of knowledge. Consequently, this study establishes the fundamentals of developing sophisticated methods and tools for the advancement of Industry 4.0.

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Section: Revised Definition Of Knowledgementioning

confidence: 99%

“…This is a drawback of the existing knowledge and data representation using the semantic web. 74,75 Based on the abovementioned considerations, this section presents an Industry 4.0-and semantic web-friendly definition of knowledge, which is free from circularity and ambiguity.…”

Section: Revised Definition Of Knowledgementioning

confidence: 99%

What is knowledge in Industry 4.0?

Ullah

2020

Engineering Reports

View full text Add to dashboard Cite

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A survey of RDF stores & SPARQL engines for querying knowledge graphs

et al. 2021

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Recent years have seen the growing adoption of non-relational data models for representing diverse, incomplete data. Among these, the RDF graphbased data model has seen ever-broadening adoption, particularly on the Web. This adoption has prompted the standardization of the SPARQL query language for RDF, as well as the development of a variety of local and distributed engines for processing queries over RDF graphs. These engines implement a diverse range of specialized techniques for storage, indexing, and query processing. A number of benchmarks, based on both synthetic and real-world data, have also emerged to allow for contrasting the performance of different query engines, often at large scale. This survey paper draws together these developments, providing a comprehensive review of the techniques, engines and benchmarks for querying RDF knowledge graphs.

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