Completing Scientific Facts in Knowledge Graphs of Research Concepts

In the last few years, we have witnessed the emergence of several knowledge graphs that explicitly describe research knowledge with the aim of enabling intelligent systems for supporting and accelerating the scientific process. These resources typically characterize a set of entities in this space (...

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Detalles Bibliográficos
Autores: Borrego Díaz, Agustín, Dessì, Danilo, Hernández Salmerón, Inmaculada Concepción, Osborne, Francesco, Reforgiato Recupero, Diego, Ruiz Cortés, David, Buscaldi, Davide, Motta, Enrico
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/144473
Acceso en línea:https://hdl.handle.net/11441/144473
https://doi.org/10.1109/ACCESS.2022.3220241
Access Level:acceso abierto
Palabra clave:Knowledge graphs
science of science
knowledge graph completion
triple classification
machine learning
semantic web
Descripción
Sumario:In the last few years, we have witnessed the emergence of several knowledge graphs that explicitly describe research knowledge with the aim of enabling intelligent systems for supporting and accelerating the scientific process. These resources typically characterize a set of entities in this space (e.g., tasks, methods, evaluation techniques, proteins, chemicals), their relations, and the relevant actors (e.g., researchers, organizations) and documents (e.g., articles, books). However, they are usually very partial representations of the actual research knowledge and may miss several relevant facts. In this paper, we introduce SciCheck, a new triple classification approach for completing scientific statements in knowledge graphs. SciCheck was evaluated against other state-of-the-art approaches on seven benchmarks, yielding excellent results. Finally, we provide a real-world use case and applied SciCheck to the Artificial Intelligence Knowledge Graph (AI-KG), a large-scale automatically-generated open knowledge graph including 1.2M statements extracted from the 333K most cited articles in the field of Artificial Intelligence, and generated a new version of this knowledge graph with 300K additional triples