Latest Applications of Semantic Web Technologies for Service Industry

Ekuobase, Godspower O.; Ebietomere, Esingbemi Princewill

doi:10.1201/9781003309420-4

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Characteristics Of Big Data1

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2024

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Cited by 2 publications

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“…If the velocity of data is high, then such data is termed "big data". With the advent of several disruptive technologies like the internet of things (IOTs), and cyber-physical systems as evident in industry 4.0-an advanced manufacturing model used to refer to modifications linked to automation fields fused with information technology, and driven by knowledge, experimentation, and innovation 8,22 -this characteristic of big data has become more ubiquitous 8,23 . Additionally, this feature of big data imposes a strain on computational resources as a result of the fast pace at which the data is generated, thus requiring real time analysis of data for veritable decision making.…”

Section: Characteristics Of Big Datamentioning

confidence: 99%

Semantic Web Technologies and Machine Learning: A Conjoined Silver Bullet for Big Data

Ebietomere,

Ajisegiri

2024

JAIMLD

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Section: Characteristics Of Big Datamentioning

confidence: 99%

Semantic Web Technologies and Machine Learning: A Conjoined Silver Bullet for Big Data

Ebietomere,

Ajisegiri

2024

JAIMLD

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LLM-Powered Natural Language Text Processing for Ontology Enrichment

Mukanova,

Milosz,

Dauletkaliyeva

et al. 2024

Applied Sciences

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This paper describes a method and technology for processing natural language texts and extracting data from the text that correspond to the semantics of an ontological model. The proposed method is distinguished by the use of a Large Language Model algorithm for text analysis. The extracted data are stored in an intermediate format, after which individuals and properties that reflect the specified semantics are programmatically created in the ontology. The proposed technology is implemented using the example of an ontological model that describes the geographical configuration and administrative–territorial division of Kazakhstan. The proposed method and technology can be applied in any subject areas for which ontological models have been developed. The results of the study can significantly improve the efficiency of using knowledge bases based on semantic networks by converting texts in natural languages into semantically linked data.

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Latest Applications of Semantic Web Technologies for Service Industry

Cited by 2 publications

References 0 publications

Semantic Web Technologies and Machine Learning: A Conjoined Silver Bullet for Big Data

Semantic Web Technologies and Machine Learning: A Conjoined Silver Bullet for Big Data

LLM-Powered Natural Language Text Processing for Ontology Enrichment

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