Ontology Mapping Framework with Feature Extraction and Semantic Embeddings

Mayanka Chandrashekar; Rohithkumar Nagulapati; Yugyung Lee

doi:10.1109/ICHI-W.2018.00012

Ontology Mapping Framework with Feature Extraction and Semantic Embeddings

Mayanka Chandrashekar, Rohithkumar Nagulapati, Yugyung Lee

Biostatistics and Biomedical Informatics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

Abstract

During the last decade, we have seen an increasing interest in biomedical ontologies. In this paper, we proposed a semantic framework for an automatic ontology mapping through ontology search, feature extraction, and word embeddings. This is a new way to discover semantic mapping between the concepts across multiple ontologies. The ontologies were mapped to semantic features extracted from multiple ontologies selected from the NCBO BioPortal. We confirmed that the OMF framework is effective in the enhancement of the existing ontologies mapping and the discovery of new relations across ontologies beyond the boundary of ontologies.

Original language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	34-42
Number of pages	9
ISBN (Electronic)	9781538667774
DOIs	https://doi.org/10.1109/ICHI-W.2018.00012
State	Published - Jul 16 2018
Event	6th IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018 - New York, United States Duration: Jun 4 2018 → Jun 7 2018

Publication series

Name	Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018

Conference

Conference	6th IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018
Country/Territory	United States
City	New York
Period	06/4/18 → 06/7/18

Keywords

Ontology Connectivity
ontology mapping
Term Frequency and Inverse Document Frequency
Word Embeddings

Access to Document

10.1109/ICHI-W.2018.00012

Cite this

Chandrashekar, M., Nagulapati, R., & Lee, Y. (2018). Ontology Mapping Framework with Feature Extraction and Semantic Embeddings. In Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018 (pp. 34-42). Article 8411678 (Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICHI-W.2018.00012

Chandrashekar, Mayanka ; Nagulapati, Rohithkumar ; Lee, Yugyung. / Ontology Mapping Framework with Feature Extraction and Semantic Embeddings. Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 34-42 (Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018).

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abstract = "During the last decade, we have seen an increasing interest in biomedical ontologies. In this paper, we proposed a semantic framework for an automatic ontology mapping through ontology search, feature extraction, and word embeddings. This is a new way to discover semantic mapping between the concepts across multiple ontologies. The ontologies were mapped to semantic features extracted from multiple ontologies selected from the NCBO BioPortal. We confirmed that the OMF framework is effective in the enhancement of the existing ontologies mapping and the discovery of new relations across ontologies beyond the boundary of ontologies.",

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author = "Mayanka Chandrashekar and Rohithkumar Nagulapati and Yugyung Lee",

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Chandrashekar, M, Nagulapati, R & Lee, Y 2018, Ontology Mapping Framework with Feature Extraction and Semantic Embeddings. in Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018., 8411678, Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018, Institute of Electrical and Electronics Engineers Inc., pp. 34-42, 6th IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018, New York, United States, 06/4/18. https://doi.org/10.1109/ICHI-W.2018.00012

Ontology Mapping Framework with Feature Extraction and Semantic Embeddings. / Chandrashekar, Mayanka; Nagulapati, Rohithkumar; Lee, Yugyung.
Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 34-42 8411678 (Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - During the last decade, we have seen an increasing interest in biomedical ontologies. In this paper, we proposed a semantic framework for an automatic ontology mapping through ontology search, feature extraction, and word embeddings. This is a new way to discover semantic mapping between the concepts across multiple ontologies. The ontologies were mapped to semantic features extracted from multiple ontologies selected from the NCBO BioPortal. We confirmed that the OMF framework is effective in the enhancement of the existing ontologies mapping and the discovery of new relations across ontologies beyond the boundary of ontologies.

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KW - ontology mapping

KW - Term Frequency and Inverse Document Frequency

KW - Word Embeddings

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M3 - Conference contribution

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BT - Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018

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Chandrashekar M, Nagulapati R, Lee Y. Ontology Mapping Framework with Feature Extraction and Semantic Embeddings. In Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 34-42. 8411678. (Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018). doi: 10.1109/ICHI-W.2018.00012

Ontology Mapping Framework with Feature Extraction and Semantic Embeddings

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Keywords

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