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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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KGen: a knowledge graph generator from biomedical scientific literature.

Anderson Rossanez1, Julio Cesar Dos Reis2, Ricardo da Silva Torres3

  • 1Institute of Computing, University of Campinas, Campinas, SP, Brazil. anderson.rossanez@ic.unicamp.br.

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|December 15, 2020
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Summary

This study presents a semi-automatic method for generating knowledge graphs from biomedical texts, aiding Alzheimer's Disease research. The approach effectively extracts and links scientific knowledge, improving data representation for researchers.

Keywords:
Information ExtractionKnowledge GraphsOntologiesRDF Triples

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Area of Science:

  • Biomedical Informatics
  • Computational Linguistics
  • Knowledge Representation

Background:

  • Scientific research generates vast amounts of data, necessitating computational tools for knowledge extraction.
  • Alzheimer's Disease research produces extensive data, highlighting the need for efficient knowledge representation to advance understanding and treatment.
  • Effective knowledge representation benefits researchers, the scientific community, and society by facilitating the discovery of new insights.

Purpose of the Study:

  • To develop and evaluate a semi-automatic method for generating knowledge graphs (KGs) from biomedical scientific literature.
  • To extract and represent knowledge from unstructured biomedical texts using natural language processing (NLP) techniques.
  • To link extracted entities and relations to existing biomedical ontologies.

Main Methods:

  • A semi-automatic method employing NLP techniques to extract knowledge from biomedical texts.
  • Generation of knowledge graphs (KGs) representing entities and their relationships.
  • Linking KG concepts to established biomedical ontologies.
  • Validation by physicians comparing extracted triples against manual extraction from Alzheimer's Disease abstracts.
  • Qualitative analysis of generated KGs using a dedicated software tool.

Main Results:

  • The method successfully generates high-quality knowledge graphs (KGs).
  • A significant number of factual triples were extracted, demonstrating the effectiveness of the rule-based relation identification.
  • Ontology linking was achieved, validating the proposed ontology linking approach.
  • Physician evaluation confirmed the quality and utility of the extracted information.

Conclusions:

  • The proposed method effectively builds ontology-linked knowledge graphs from biomedical texts.
  • This knowledge representation adds value to research by enabling concept comparison across studies.
  • Generated KGs can facilitate data-driven theory proposals and advance scientific understanding.