Publishing a Disease Ontologies as Linked Data

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8388)


Publishing open data as linked data is a significant trend in not only the Semantic Web community but also other domains such as life science, government, media, geographic research and publication. One feature of linked data is the instance-centric approach, which assumes that considerable linked instances can result in valuable knowledge. In the context of linked data, ontologies offer a common vocabulary and schema for RDF graphs. However, from an ontological engineering viewpoint, some ontologies offer systematized knowledge, developed under close cooperation between domain experts and ontology engineers. Such ontologies could be a valuable knowledge base for advanced information systems. Although ontologies in RDF formats using OWL or RDF(S) can be published as linked data, it is not always convenient to use other applications because of the complicated graph structures. Consequently, this paper discusses RDF data models for publishing ontologies as linked data. As a case study, we focus on a disease ontology in which diseases are defined as causal chains.


Ontology Linked data Disease ontology Ontological engineering 



A part of this research is supported by the Japan Society for the Promotion of Science (JSPS) through its “FIRST Program" and the Ministry of Health, Labour and Welfare, Japan. The authors are deeply grateful to medical doctors (Natsuko Ohtomo, Aki Hayashi, Takayoshi Matsumura, Ryota Sakurai, Satomi Terada, Kayo Waki,, of The University of Tokyo Hospital for describing disease ontology and providing us with broad clinical knowledge. The authors also would like to thank Hiroko Kou for describing the primary version of disease ontology, Nobutake Kato for implementation the proposed systems and Enago ( for the English language review.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.The Institute of Scientific and Industrial ResearchOsaka UniversityIbarakiJapan
  2. 2.Department of Planning Information and ManagementThe University of Tokyo HospitalBunkyo-kuJapan
  3. 3.Department of Medical Informatics Graduate School of MedicineThe University of TokyoBunkyo-kuJapan
  4. 4.Japan Advanced Institute of Science and TechnologyNomiJapan

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