From Symptoms to Diseases - Creating the Missing Link

A wealth of biomedical datasets is meanwhile published as Linked Open Data. Each of these datasets has a particular focus, such as providing information on diseases or symptoms of a certain kind. Hence, a comprehensive view can only be provided by integrating information from various datasets. Although, links between diseases and symptoms can be found, these links are far too sparse to enable practical applications such as a disease-centric access to clinical reports that are annotated with symptom information. For this purpose, we build a model of disease-symptom relations. Utilizing existing ontology mappings, we propagate semantic type information for disease and symptom across ontologies. Then entities of the same semantic type from different ontologies are clustered and object properties between entities are mapped to cluster-level relations. The effectiveness of our approach is demonstrated by integrating all available disease-symptom relations from different biomedical ontologies resulting in a significantly increased linkage between datasets.

[1]  Saikat Mukherjee,et al.  Semantic annotation of medical images , 2010, Medical Imaging.

[2]  D. Lindberg,et al.  The Unified Medical Language System , 1993, Methods of Information in Medicine.

[3]  Amit P. Sheth,et al.  Linked Data Is Merely More Data , 2010, AAAI Spring Symposium: Linked Data Meets Artificial Intelligence.

[4]  M. Ashburner,et al.  The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration , 2007, Nature Biotechnology.

[5]  V. Pattabiraman,et al.  Ontology Based Disease Information System , 2012 .

[6]  Michel Dumontier,et al.  Ontology-Based Querying with Bio2RDF’s Linked Open Data , 2013, Journal of Biomedical Semantics.

[7]  Jérôme Euzenat,et al.  Ontology Matching: State of the Art and Future Challenges , 2013, IEEE Transactions on Knowledge and Data Engineering.

[8]  Mark A. Musen,et al.  Creating Mappings For Ontologies in Biomedicine: Simple Methods Work , 2009, AMIA.

[9]  Ryutaro Ichise,et al.  Ontology Integration for Linked Data , 2014, Journal on Data Semantics.

[10]  Kent A. Spackman,et al.  Review: Representing Thoughts, Words, and Things in the UMLS , 1998, J. Am. Medical Informatics Assoc..

[11]  Sonja Zillner,et al.  Towards a Ranking of Likely Diseases in Terms of Precision and Recall , 2012 .

[12]  Michel Dumontier,et al.  Bio2RDF Release 2: Improved Coverage, Interoperability and Provenance of Life Science Linked Data , 2013, ESWC.

[13]  Craig A. Knoblock,et al.  Discovering Concept Coverings in Ontologies of Linked Data Sources , 2012, International Semantic Web Conference.

[14]  Amit P. Sheth,et al.  Contextual Ontology Alignment of LOD with an Upper Ontology: A Case Study with Proton , 2011, ESWC.

[15]  Gang Feng,et al.  Disease Ontology: a backbone for disease semantic integration , 2011, Nucleic Acids Res..

[16]  Mark A. Musen,et al.  BioPortal as a dataset of linked biomedical ontologies and terminologies in RDF , 2013, Semantic Web.

[17]  Mark A. Musen,et al.  Using SPARQL to Query BioPortal Ontologies and Metadata , 2012, SEMWEB.

[18]  Simon Fong,et al.  Building a diseases symptoms ontology for medical diagnosis: An integrative approach , 2012, The First International Conference on Future Generation Communication Technologies.

[19]  Csongor Nyulas,et al.  BioPortal: enhanced functionality via new Web services from the National Center for Biomedical Ontology to access and use ontologies in software applications , 2011, Nucleic Acids Res..

[20]  Sonja Zillner,et al.  Interpreting Patient Data using Medical Background Knowledge , 2012, ICBO.

[21]  Elizabeth Chang,et al.  Ontology-based Multi-agent Systems Support Human Disease Study and Control , 2005, SOAS.