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MKEM: a Multi-level Knowledge Emergence Model for mining undiscovered public knowledge
BACKGROUND: Since Swanson proposed the Undiscovered Public Knowledge (UPK) model, there have been many approaches to uncover UPK by mining the biomedical literature. These earlier works, however, required substantial manual intervention to reduce the number of possible connections and are mainly app...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3165192/ https://www.ncbi.nlm.nih.gov/pubmed/20406501 http://dx.doi.org/10.1186/1471-2105-11-S2-S3 |
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author | Ijaz, Ali Z Song, Min Lee, Doheon |
author_facet | Ijaz, Ali Z Song, Min Lee, Doheon |
author_sort | Ijaz, Ali Z |
collection | PubMed |
description | BACKGROUND: Since Swanson proposed the Undiscovered Public Knowledge (UPK) model, there have been many approaches to uncover UPK by mining the biomedical literature. These earlier works, however, required substantial manual intervention to reduce the number of possible connections and are mainly applied to disease-effect relation. With the advancement in biomedical science, it has become imperative to extract and combine information from multiple disjoint researches, studies and articles to infer new hypotheses and expand knowledge. METHODS: We propose MKEM, a Multi-level Knowledge Emergence Model, to discover implicit relationships using Natural Language Processing techniques such as Link Grammar and Ontologies such as Unified Medical Language System (UMLS) MetaMap. The contribution of MKEM is as follows: First, we propose a flexible knowledge emergence model to extract implicit relationships across different levels such as molecular level for gene and protein and Phenomic level for disease and treatment. Second, we employ MetaMap for tagging biological concepts. Third, we provide an empirical and systematic approach to discover novel relationships. RESULTS: We applied our system on 5000 abstracts downloaded from PubMed database. We performed the performance evaluation as a gold standard is not yet available. Our system performed with a good precision and recall and we generated 24 hypotheses. CONCLUSIONS: Our experiments show that MKEM is a powerful tool to discover hidden relationships residing in extracted entities that were represented by our Substance-Effect-Process-Disease-Body Part (SEPDB) model. |
format | Online Article Text |
id | pubmed-3165192 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-31651922011-09-03 MKEM: a Multi-level Knowledge Emergence Model for mining undiscovered public knowledge Ijaz, Ali Z Song, Min Lee, Doheon BMC Bioinformatics Proceedings BACKGROUND: Since Swanson proposed the Undiscovered Public Knowledge (UPK) model, there have been many approaches to uncover UPK by mining the biomedical literature. These earlier works, however, required substantial manual intervention to reduce the number of possible connections and are mainly applied to disease-effect relation. With the advancement in biomedical science, it has become imperative to extract and combine information from multiple disjoint researches, studies and articles to infer new hypotheses and expand knowledge. METHODS: We propose MKEM, a Multi-level Knowledge Emergence Model, to discover implicit relationships using Natural Language Processing techniques such as Link Grammar and Ontologies such as Unified Medical Language System (UMLS) MetaMap. The contribution of MKEM is as follows: First, we propose a flexible knowledge emergence model to extract implicit relationships across different levels such as molecular level for gene and protein and Phenomic level for disease and treatment. Second, we employ MetaMap for tagging biological concepts. Third, we provide an empirical and systematic approach to discover novel relationships. RESULTS: We applied our system on 5000 abstracts downloaded from PubMed database. We performed the performance evaluation as a gold standard is not yet available. Our system performed with a good precision and recall and we generated 24 hypotheses. CONCLUSIONS: Our experiments show that MKEM is a powerful tool to discover hidden relationships residing in extracted entities that were represented by our Substance-Effect-Process-Disease-Body Part (SEPDB) model. BioMed Central 2010-04-16 /pmc/articles/PMC3165192/ /pubmed/20406501 http://dx.doi.org/10.1186/1471-2105-11-S2-S3 Text en Copyright ©2010 Song and Lee; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Proceedings Ijaz, Ali Z Song, Min Lee, Doheon MKEM: a Multi-level Knowledge Emergence Model for mining undiscovered public knowledge |
title | MKEM: a Multi-level Knowledge Emergence Model for mining undiscovered public knowledge |
title_full | MKEM: a Multi-level Knowledge Emergence Model for mining undiscovered public knowledge |
title_fullStr | MKEM: a Multi-level Knowledge Emergence Model for mining undiscovered public knowledge |
title_full_unstemmed | MKEM: a Multi-level Knowledge Emergence Model for mining undiscovered public knowledge |
title_short | MKEM: a Multi-level Knowledge Emergence Model for mining undiscovered public knowledge |
title_sort | mkem: a multi-level knowledge emergence model for mining undiscovered public knowledge |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3165192/ https://www.ncbi.nlm.nih.gov/pubmed/20406501 http://dx.doi.org/10.1186/1471-2105-11-S2-S3 |
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