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MedGraph: A semantic biomedical information retrieval framework using knowledge graph embedding for PubMed
Here we study the semantic search and retrieval problem in biomedical digital libraries. First, we introduce MedGraph, a knowledge graph embedding-based method that provides semantic relevance retrieval and ranking for the biomedical literature indexed in PubMed. Second, we evaluate our approach usi...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9627348/ https://www.ncbi.nlm.nih.gov/pubmed/36338335 http://dx.doi.org/10.3389/fdata.2022.965619 |
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author | Ebeid, Islam Akef |
author_facet | Ebeid, Islam Akef |
author_sort | Ebeid, Islam Akef |
collection | PubMed |
description | Here we study the semantic search and retrieval problem in biomedical digital libraries. First, we introduce MedGraph, a knowledge graph embedding-based method that provides semantic relevance retrieval and ranking for the biomedical literature indexed in PubMed. Second, we evaluate our approach using PubMed's Best Match algorithm. Moreover, we compare our method MedGraph to a traditional TF-IDF-based algorithm. Third, we use a dataset extracted from PubMed, including 30 million articles' metadata such as abstracts, author information, citation information, and extracted biological entity mentions. We pull a subset of the dataset to evaluate MedGraph using predefined queries with ground truth ranked results. To our knowledge, this technique has not been explored before in biomedical information retrieval. In addition, our results provide some evidence that semantic approaches to search and relevance in biomedical digital libraries that rely on knowledge graph modeling offer better search relevance results when compared with traditional methods in terms of objective metrics. |
format | Online Article Text |
id | pubmed-9627348 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-96273482022-11-03 MedGraph: A semantic biomedical information retrieval framework using knowledge graph embedding for PubMed Ebeid, Islam Akef Front Big Data Big Data Here we study the semantic search and retrieval problem in biomedical digital libraries. First, we introduce MedGraph, a knowledge graph embedding-based method that provides semantic relevance retrieval and ranking for the biomedical literature indexed in PubMed. Second, we evaluate our approach using PubMed's Best Match algorithm. Moreover, we compare our method MedGraph to a traditional TF-IDF-based algorithm. Third, we use a dataset extracted from PubMed, including 30 million articles' metadata such as abstracts, author information, citation information, and extracted biological entity mentions. We pull a subset of the dataset to evaluate MedGraph using predefined queries with ground truth ranked results. To our knowledge, this technique has not been explored before in biomedical information retrieval. In addition, our results provide some evidence that semantic approaches to search and relevance in biomedical digital libraries that rely on knowledge graph modeling offer better search relevance results when compared with traditional methods in terms of objective metrics. Frontiers Media S.A. 2022-10-19 /pmc/articles/PMC9627348/ /pubmed/36338335 http://dx.doi.org/10.3389/fdata.2022.965619 Text en Copyright © 2022 Ebeid. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Big Data Ebeid, Islam Akef MedGraph: A semantic biomedical information retrieval framework using knowledge graph embedding for PubMed |
title | MedGraph: A semantic biomedical information retrieval framework using knowledge graph embedding for PubMed |
title_full | MedGraph: A semantic biomedical information retrieval framework using knowledge graph embedding for PubMed |
title_fullStr | MedGraph: A semantic biomedical information retrieval framework using knowledge graph embedding for PubMed |
title_full_unstemmed | MedGraph: A semantic biomedical information retrieval framework using knowledge graph embedding for PubMed |
title_short | MedGraph: A semantic biomedical information retrieval framework using knowledge graph embedding for PubMed |
title_sort | medgraph: a semantic biomedical information retrieval framework using knowledge graph embedding for pubmed |
topic | Big Data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9627348/ https://www.ncbi.nlm.nih.gov/pubmed/36338335 http://dx.doi.org/10.3389/fdata.2022.965619 |
work_keys_str_mv | AT ebeidislamakef medgraphasemanticbiomedicalinformationretrievalframeworkusingknowledgegraphembeddingforpubmed |