Using NLP to Enrich Scientific Knowledge Graphs: A Case Study to Find Similar Papers

Xavier Quevedo, Janneth Chicaiza

2023

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 among 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.

Paper Citation

in Harvard Style

Quevedo X. and Chicaiza J. (2023). Using NLP to Enrich Scientific Knowledge Graphs: A Case Study to Find Similar Papers. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-623-1, pages 190-198. DOI: 10.5220/0011671100003393

in Bibtex Style

@conference{icaart23,
author={Xavier Quevedo and Janneth Chicaiza},
title={Using NLP to Enrich Scientific Knowledge Graphs: A Case Study to Find Similar Papers},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2023},
pages={190-198},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011671100003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Using NLP to Enrich Scientific Knowledge Graphs: A Case Study to Find Similar Papers
SN - 978-989-758-623-1
AU - Quevedo X.
AU - Chicaiza J.
PY - 2023
SP - 190
EP - 198
DO - 10.5220/0011671100003393