2023
DOI: 10.1007/978-3-031-36819-6_26
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Towards Knowledge Graph Creation from Greek Governmental Documents

Abstract: Documents contain textual information, which is of the utmost importance for all the organizations. Document management systems have been used to store vast amounts of unstructured textual data described with minimal metadata, a method that has several limitations. In order to convert hidden knowledge into machine-readable data with rich connections, this paper presents work in progress on the development of the first end-to-end guided approach to construct a Knowledge Graph from Greek government documents fro… Show more

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Cited by 3 publications
(2 citation statements)
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“…The current effort could also be significantly promoted if automatic extraction of knowledge, in the form of RDF triples [9] [14], is made possible via exploitation, for instance, of Natural Language Processing and Machine Learning techniques [6] so that it can feed-in with a sufficient amount of data the developed ontology. The latter could additionally enable a validation process of the data integrity in the sense of a "Proof of Concept" procedure for the imported data.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…The current effort could also be significantly promoted if automatic extraction of knowledge, in the form of RDF triples [9] [14], is made possible via exploitation, for instance, of Natural Language Processing and Machine Learning techniques [6] so that it can feed-in with a sufficient amount of data the developed ontology. The latter could additionally enable a validation process of the data integrity in the sense of a "Proof of Concept" procedure for the imported data.…”
Section: Discussionmentioning
confidence: 99%
“…-The Spanish Public organizations ontology 9 representing the hierarchical structure and competences of public organizations in Spain; -The UK Parliament organisation ontology 10 for the internal hierarchical structure of the UK Parliament in terms of positions and roles.…”
Section: Governmental-public Organizations Ontologies/vocabulariesmentioning
confidence: 99%