Automated interpretability of linked data ontologies: : an evaluation within the cultural heritage domain

Freire, Nuno; Valk, Sjors de

doi:10.1109/bigdata47090.2019.9005491

Cited by 8 publications

(9 citation statements)

References 7 publications

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“…Semantically enriched representation of data or KGs adds meaning and context to data through ontologies and vocabularies that make it more easily understood and interpreted by humans and machines [25]. This leads to several benefits including improved data integration, data understanding, data interoperability, and faster discovery of knowledge via more powerful data querying and analysis [7,10,19]. We present the new release of the I4.0 KG dataset, comprising comprehensive semantic descriptions and values of the machine's processes involved in a single football production line.…”

Section: An Approach To Integrate the I40 Production Line Datamentioning

confidence: 99%

A benchmark dataset with Knowledge Graph generation for Industry 4.0 production lines

Yahya

Ali²,

Mehmood

et al. 2024

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Industry 4.0 (I4.0) is a new era in the industrial revolution that emphasizes machine connectivity, automation, and data analytics. The I4.0 pillars such as autonomous robots, cloud computing, horizontal and vertical system integration, and the industrial internet of things have increased the performance and efficiency of production lines in the manufacturing industry. Over the past years, efforts have been made to propose semantic models to represent the manufacturing domain knowledge, one such model is Reference Generalized Ontological Model (RGOM).11 https://w3id.org/rgom However, its adaptability like other models is not ensured due to the lack of manufacturing data. In this paper, we aim to develop a benchmark dataset for knowledge graph generation in Industry 4.0 production lines and to show the benefits of using ontologies and semantic annotations of data to showcase how the I4.0 industry can benefit from KGs and semantic datasets. This work is the result of collaboration with the production line managers, supervisors, and engineers in the football industry to acquire realistic production line data22 https://github.com/MuhammadYahta/ManufacturingProductionLineDataSetGeneration-Football,.33 https://zenodo.org/record/7779522 Knowledge Graphs (KGs) or Knowledge Graph (KG) have emerged as a significant technology to store the semantics of the domain entities. KGs have been used in a variety of industries, including banking, the automobile industry, oil and gas, pharmaceutical and health care, publishing, media, etc. The data is mapped and populated to the RGOM classes and relationships using an automated solution based on JenaAPI, producing an I4.0 KG. It contains more than 2.5 million axioms and about 1 million instances. This KG enables us to demonstrate the adaptability and usefulness of the RGOM. Our research helps the production line staff to take timely decisions by exploiting the information embedded in the KG. In relation to this, the RGOM adaptability is demonstrated with the help of a use case scenario to discover required information such as current temperature at a particular time, the status of the motor, tools deployed on the machine, etc.

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Section: An Approach To Integrate the I40 Production Line Datamentioning

confidence: 99%

A benchmark dataset with Knowledge Graph generation for Industry 4.0 production lines

Yahya

Ali²,

Mehmood

et al. 2024

View full text Add to dashboard Cite

show abstract

“…Afterward, a set of qualitative criteria usually considered to evaluate ontologies are described and five previous studies (from 1995 to 2007) proposing metrics to evaluate ontologies are compared in terms of this set of criteria. Concerning the cultural heritage domain, we found some studies that evaluate the usability of ontologies in this domain (Freire and de Valk, 2019;Freire and Proença, 2020). We also found a study to evaluate bibliographic ontologies analyzing their classes and properties (Biagetti, 2018).…”

Section: Related Workmentioning

confidence: 99%

Application of a methodological approach to compare ontologies

Cardinale

Cornejo-Lupa

Gala

et al. 2021

IJWIS

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Purpose This study aims to the OQuaRE quality model to the developed methodology. Design/methodology/approach Ontologies are formal, well-defined and flexible representations of knowledge related to a specific domain. They provide the base to develop efficient and interoperable solutions. Hence, a proliferation of ontologies in many domains is unleashed. Then, it is necessary to define how to compare such ontologies to decide which one is the most suitable for the specific needs of users/developers. As the emerging development of ontologies, several studies have proposed criteria to evaluate them. Findings In a previous study, the authors propose a methodological process to qualitatively and quantitatively compare ontologies at Lexical, Structural and Domain Knowledge levels, considering correctness and quality perspectives. As the evaluation methods of the proposal are based on a golden-standard, it can be customized to compare ontologies in any domain. Practical implications To show the suitability of the proposal, the authors apply the methodological approach to conduct comparative studies of ontologies in two different domains, one in the robotic area, in particular for the simultaneous localization and mapping (SLAM) problem; and the other one, in the cultural heritage domain. With these cases of study, the authors demonstrate that with this methodological comparative process, we are able to identify the strengths and weaknesses of ontologies, as well as the gaps still needed to fill in the target domains. Originality/value Using these metrics and the quality model from OQuaRE, the authors are incorporating a standard of software engineering at the quality validation into the Semantic Web.

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“…Semantic technologies, namely ontologies, have been gaining popularity in recent years due to their ability to specify and utilise relationships between entities and across domains and at large scales. On-tologies allow a better knowledge discovery, interpretability, transparency and traceability of data [7][8][9][10][11][12]. Moreover, semantic web technologies are based on open and interoperable standards such as RDF (Resource Description Framework) 5 for information representation, OWL (Web Ontology Language) 6 for representation of ontological modeling and SPARQL 7 for querying, and are extendable by design -making them suitable for application across use cases.…”

Section: Introductionmentioning

confidence: 99%

Consent through the lens of semantics: State of the art survey and best practices

Kurteva

Chhetri

Pandit

et al. 2024

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The acceptance of the GDPR legislation in 2018 started a new technological shift towards achieving transparency. GDPR put focus on the concept of informed consent applicable for data processing, which led to an increase of the responsibilities regarding data sharing for both end users and companies. This paper presents a literature survey of existing solutions that use semantic technology for implementing consent. The main focus is on ontologies, how they are used for consent representation and for consent management in combination with other technologies such as blockchain. We also focus on visualisation solutions aimed at improving individuals’ consent comprehension. Finally, based on the overviewed state of the art we propose best practices for consent implementation.

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Automated interpretability of linked data ontologies: : an evaluation within the cultural heritage domain

Cited by 8 publications

References 7 publications

A benchmark dataset with Knowledge Graph generation for Industry 4.0 production lines

A benchmark dataset with Knowledge Graph generation for Industry 4.0 production lines

Application of a methodological approach to compare ontologies

Consent through the lens of semantics: State of the art survey and best practices

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