KGen: a knowledge graph generator from biomedical scientific literature
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RESEARCH
KGen: a knowledge graph generator from biomedical scientific literature Anderson Rossanez1* , Julio Cesar dos Reis1, Ricardo da Silva Torres2 and Hélène de Ribaupierre3
From The 4th International Workshop on Semantics-Powered Data Analytics Auckland, New Zealand. 27 October 2019
Abstract Background: Knowledge is often produced from data generated in scientific investigations. An ever-growing number of scientific studies in several domains result into a massive amount of data, from which obtaining new knowledge requires computational help. For example, Alzheimer’s Disease, a life-threatening degenerative disease that is not yet curable. As the scientific community strives to better understand it and find a cure, great amounts of data have been generated, and new knowledge can be produced. A proper representation of such knowledge brings great benefits to researchers, to the scientific community, and consequently, to society. Methods: In this article, we study and evaluate a semi-automatic method that generates knowledge graphs (KGs) from biomedical texts in the scientific literature. Our solution explores natural language processing techniques with the aim of extracting and representing scientific literature knowledge encoded in KGs. Our method links entities and relations represented in KGs to concepts from existing biomedical ontologies available on the Web. We demonstrate the effectiveness of our method by generating KGs from unstructured texts obtained from a set of abstracts taken from scientific papers on the Alzheimer’s Disease. We involve physicians to compare our extracted triples from their manual extraction via their analysis of the abstracts. The evaluation further concerned a qualitative analysis by the physicians of the generated KGs with our software tool. Results: The experimental results indicate the quality of the generated KGs. The proposed method extracts a great amount of triples, showing the effectiveness of our rule-based method employed in the identification of relations in texts. In addition, ontology links are successfully obtained, which demonstrates the effectiveness of the ontology linking method proposed in this investigation. Conclusions: We demonstrate that our proposal is effective on building ontology-linked KGs representing the knowledge obtained from biomedical scientific texts. Such representation can add value to the research in various domains, enabling researchers to compare the occurrence of concepts from different studies. The KGs generated may pave the way to potential proposal of new theories based on data analysis to advance the state of the art in their research domains. Keywords: Knowledge Graphs, RDF Triples, Ontologies, Information Extraction
*Correspondence: [email protected] 1 Institute of Computing, University of Campinas, Campinas, SP, Brazil Full list of author information is available at the end of the article
Background Knowledge evolves over time, often fostered by new findings or by changes in adopted reasoning me
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