A Knowledge Graph (KG) is a form of structured human knowledge depicting relations between entities, destined to reflect cognition and human-level intelligence. Large and openly available knowledge graphs (KGs) like DBpedia, YAGO, WikiData are universal cross-domain knowledge bases and are also accessible within the Linked Open Data (LOD) cloud, according to the FAIR principles that make data findable, accessible, interoperable and reusable. This work aims at proposing a methodological approach to construct domain-oriented knowledge graphs by parsing natural language content to extract simple triple-based sentences that summarize the analyzed text. The triples coded in RDF are in the form of subject, predicate, and object. The goal is to generate a KG that, through the main identified concepts, can be navigable and linked to the existing KGs to be automatically found and usable on the Web LOD cloud. © 2022 Copyright for this paper by its authors.

FAIR Knowledge Graph construction from text, an approach applied to fictional novels

Sabrina Senatore
2022-01-01

Abstract

A Knowledge Graph (KG) is a form of structured human knowledge depicting relations between entities, destined to reflect cognition and human-level intelligence. Large and openly available knowledge graphs (KGs) like DBpedia, YAGO, WikiData are universal cross-domain knowledge bases and are also accessible within the Linked Open Data (LOD) cloud, according to the FAIR principles that make data findable, accessible, interoperable and reusable. This work aims at proposing a methodological approach to construct domain-oriented knowledge graphs by parsing natural language content to extract simple triple-based sentences that summarize the analyzed text. The triples coded in RDF are in the form of subject, predicate, and object. The goal is to generate a KG that, through the main identified concepts, can be navigable and linked to the existing KGs to be automatically found and usable on the Web LOD cloud. © 2022 Copyright for this paper by its authors.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4866193
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