Authors:
Xavier Quevedo
and
Janneth Chicaiza
Affiliation:
Departamento de Ciencias de la Computación, Universidad Técnica Particular de Loja, Loja, Ecuador
Keyword(s):
DBPedia, NLP, Metadata, Scientific Knowledge Graphs, RDF, Semantic Similarity.
Abstract:
In recent years, Knowledge Graphs have become increasingly popular thanks to the potential of Semantic Web technologies and the development of NoSQL graph-based. A knowledge graph that describes scholarly production makes the literature metadata legible for machines. Making the paper’s text legible for machines enables them to discover and leverage relevant information for the scientific community beyond searching based on metadata fields. Thus, scientific knowledge graphs can become catalysts to drive research. In this research, we reuse an existing scientific knowledge graph and enrich it with new facts to demonstrate how this information can be used to improve tasks like finding similar documents. To identify new entities and relationships we combine two different approaches: (1) an RDF scheme-based approach to recognize named entities, and (2) a sequence labeler based on spaCy to recognize entities and relationships on papers’ abstracts. Then, we compute the semantic similarity a
mong papers considering the original graph and the enriched one to state what is the graph that returns the closest similarity. Finally, we conduct an experiment to verify the value or contribution of the additional information, i.e. new triples, obtained by analyzing the content of the abstracts of the papers.
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