Método de enriquecimiento de grafos de conocimiento basado en la inferencia de entidades semánticas equivalentes desde fuentes de datos abiertos (Enriching a knowledge graph from open knowledge datasources)

Abstract

Abstract: The present study addresses the enrichment of knowledge graphs, specifically in RDF. Knowledge Graphs have become an increasingly crucial component in artificial intelligence systems, empowering digital assistants and inspiring several large-scale digital transformation projects. As a first phase, this research performs the systematic review of the state of the art in scientific databases, focusing the results on current graph enrichment techniques or methods. In the second phase, the work focuses on investigating fundamental concepts related to the Semantic Web and knowledge graphs. In the third phase, the research details the problem, objectives and proposal. It describes a model to enrich graphs using natural language processing taking into account the use of open data sources. Finally, the study implements the proposed model consisting of five phases, which are developed on the Python programming language using libraries and tools for its construction.