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MedGraph: A semantic biomedical information retrieval framework using knowledge graph embedding for PubMed.

Islam Akef Ebeid1

  • 1Department of Information Science, University of Arkansas at Little Rock, Little Rock, AR, United States.

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This study introduces MedGraph, a novel semantic search method for biomedical literature. MedGraph enhances search relevance in digital libraries compared to traditional algorithms.

Keywords:
biomedical digital librariesgraph embeddinginformation retrievalknowledge graphnatural language processing

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Area of Science:

  • Biomedical Informatics
  • Information Retrieval
  • Knowledge Representation

Background:

  • Biomedical digital libraries face challenges in semantic search and retrieval.
  • Existing methods like TF-IDF may not fully capture the nuances of biomedical literature.
  • PubMed's Best Match algorithm serves as a benchmark for relevance ranking.

Purpose of the Study:

  • To introduce MedGraph, a knowledge graph embedding-based method for semantic search and retrieval in biomedical literature.
  • To evaluate MedGraph's performance against PubMed's Best Match and traditional TF-IDF algorithms.
  • To demonstrate the effectiveness of knowledge graph modeling in improving biomedical information retrieval.

Main Methods:

  • Developed MedGraph, a novel approach utilizing knowledge graph embeddings for semantic relevance.
  • Evaluated MedGraph using a large dataset of 30 million PubMed articles, including metadata and biological entity mentions.
  • Compared MedGraph's ranking results against PubMed's Best Match and TF-IDF algorithms using predefined queries and ground truth data.

Main Results:

  • MedGraph demonstrates superior search relevance compared to traditional TF-IDF methods.
  • The knowledge graph modeling approach shows promise in enhancing biomedical information retrieval.
  • Objective metrics indicate improved performance of semantic search over conventional techniques.

Conclusions:

  • Semantic search approaches, particularly those leveraging knowledge graph modeling, offer enhanced relevance in biomedical digital libraries.
  • MedGraph represents a significant advancement in semantic search for biomedical information retrieval.
  • Further exploration of knowledge graph embeddings is warranted for optimizing biomedical literature search.