A graph-based approach for integrating biological heterogeneous data based on connecting ontology
Date
2021
Authors
Zhang, S.
Tang, Y.
Yan, J.
Li, L.
Li, T.
Li, J.
Xie, P.
Gu, Y.
Xu, J.
Feng, Z.
Editors
Huang, Y.
Kurgan, L.
Luo, F.
Hu, X.T.
Chen, Y.
Dougherty, E.
Kloczkowski, A.
Li, Y.
Kurgan, L.
Luo, F.
Hu, X.T.
Chen, Y.
Dougherty, E.
Kloczkowski, A.
Li, Y.
Advisors
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Conference paper
Citation
Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021, 2021 / Huang, Y., Kurgan, L., Luo, F., Hu, X.T., Chen, Y., Dougherty, E., Kloczkowski, A., Li, Y. (ed./s), pp.600-607
Statement of Responsibility
Conference Name
2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 (9 Dec 2021 - 12 Dec 2021 : virtual)
Abstract
Linked Open Data (LOD) is an ongoing effort in the Semantic Web community to build a massive public knowledge graph. The goal is to extend the Web by publishing various open datasets as RDF on the Web and then linking data items to other useful information from different data sources. With linked data, starting from a certain point in the graph, a person or machine can explore the graph to find other related data. In this paper, we develop a novel pipeline for graph-based biological data integration.
By using our pipeline, users can easily glue heterogeneous biological ontologies, annotate sources with multiple join tables effectively, obtain a high-quality biological knowledge graph automatically, and enrich the knowledge graph with public biological ontologies finally. We implement a platform that realizes the proposed approach and conduct two case studies to evaluate the effectiveness and efficiency of our approach.
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Copyright 2021 IEEE