Ontology learning from biomedical natural language documents using UMLS

Ruiz-Martínez, Juana María; Valencia‐García, Rafael; Fernández-Breis, Jesualdo Tomás; García‐Sánchez, Francisco; Martı́nez-Béjar, Rodrigo

doi:10.1016/j.eswa.2011.04.016

Cited by 38 publications

(18 citation statements)

References 32 publications

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“…However, these approaches are focused on non-medical documents so it is necessary to consider how to apply professional knowledge of medical experts. Ontology-based methods for suggesting relevant documents of biomedical domain are proposed in [7][8][9]. Abacha et al [7] proposes a rule-based approach for extracting semantic relations between medical entities with RDF annotations.…”

Section: Related Workmentioning

confidence: 99%

“…Abacha et al [7] proposes a rule-based approach for extracting semantic relations between medical entities with RDF annotations. Jonquet et al [8] and Ruiz-Martínez et al [9] build biomedical ontologies for obtaining related concepts from target documents. Although ontology guarantees to be reusable and sharable information based on structured knowledge representation, it is more complex and need higher costs to construct the ontology than keyword-based approaches.…”

Section: Related Workmentioning

confidence: 99%

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Suggesting biomedical topics for unseen research articles based on MeSH descriptors

Lim

Jeong

Choi

2015

2015 International Conference on Big Data and Smart Computing (BIGCOMP)

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Due to the huge number of research articles in the biomedical domain, it becomes more and more important to develop methods to find relevant articles of our specific research interests. Keyword extraction is a useful method to find important topics from documents and summarize their major information. Unfortunately, it is hard to select appropriate keywords extracted by traditional method of keyword extraction from specific research fields such as biomedical domain. Although human experts can support to understand details of the keywords, extra time should be required to read contents of the documents. In this paper, we propose a method for suggesting keyword-based topics for unseen biomedical research articles from PubMed. Our method uses MeSH descriptors to summarize each document by obtaining frequencies of them. The list of frequencies is used to make keyword suggestions for given documents based on the MeSH. In the experiments, we evaluate the performance of the method by measuring the accuracy of keyword suggestions for a given set of unseen documents.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Suggesting biomedical topics for unseen research articles based on MeSH descriptors

Lim

Jeong

Choi

2015

2015 International Conference on Big Data and Smart Computing (BIGCOMP)

View full text Add to dashboard Cite

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“…The alignment of two ontologies, 01 and O2 amounts to determining the correspondence between the various ontological entities by category (type). All methods of matching determine the correspondences between ontological entities using measures of similarity [17]. The measures of similarity or distance (dissimilarity) allow evaluate the similarity or the distance between two elements (or individuals).…”

Section: Defmition Of Ontology Matchingmentioning

confidence: 99%

“…As a kind of semantic description model, ontology is effective and normal. Within recent years there has accumulated a considerable number of methods in the fields of ontology learning (closely related to ontology construction) [15][16][17] and ontology matching (computing the similarity between ontologies) [18][19][20], which have made the basis for measuring the semantic distance between the distributed data sources of an organization.…”

Section: Introductionmentioning

confidence: 99%

An ontology based semantic heterogeneity measurement framework for optimization in distributed data mining

Liu

Cao

et al. 2012

2012 International Conference on Machine Learning and Cybernetics

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In distributed data mining (DDM) systems, the semantic heterogeneity between data sources has not got universal attentions, which may produce the potential risks of damaging the quality of the final result. This paper presents a semantic distance measurement framework to extract the essential semantic heterogeneity between data sources. In this framework, an ontology-matching based multi-strategy voting method is utilized to comprehensively synthesize the semantic distances between two data source ontologies in element level and structure level. The output of the framework can be leveraged as the foundation to group the data sources for optimizing the DDM result. Finally, the framework is integrated into a DDM architecture we have proposed.

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“…Additionally, some ontologies for several domains have been developed, such as Snomed CT [6] and UMLS [7] for the medical field and AGROVOC [8] for the agricultural field. An ontology is a formal representation of knowledge, which plays a very important role in the semantic web because of its capability to express meanings and relationships.…”

Section: Introductionmentioning

confidence: 99%

Semantic Annotation of Unstructured Documents Using Concepts Similarity

Pech

Martínez

Estrada³

et al. 2017

Scientific Programming

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There is a large amount of information in the form of unstructured documents which pose challenges in the information storage, search, and retrieval. This situation has given rise to several information search approaches. Some proposals take into account the contextual meaning of the terms specified in the query. Semantic annotation technique can help to retrieve and extract information in unstructured documents. We propose a semantic annotation strategy for unstructured documents as part of a semantic search engine. In this proposal, ontologies are used to determine the context of the entities specified in the query. Our strategy for extracting the context is focused on concepts similarity. Each relevant term of the document is associated with an instance in the ontology. The similarity between each of the explicit relationships is measured through the combination of two types of associations: the association between each pair of concepts and the calculation of the weight of the relationships.

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Ontology learning from biomedical natural language documents using UMLS

Cited by 38 publications

References 32 publications

Suggesting biomedical topics for unseen research articles based on MeSH descriptors

Suggesting biomedical topics for unseen research articles based on MeSH descriptors

An ontology based semantic heterogeneity measurement framework for optimization in distributed data mining

Semantic Annotation of Unstructured Documents Using Concepts Similarity

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