An ontology-guided semantic data integration framework to support integrative data analysis of cancer survival

• Published in: BMC medical informatics and decision making Vol. 18; no. Suppl 2; p. 41
• Main Authors: Zhang, Hansi, Guo, Yi, Li, Qian, George, Thomas J, Shenkman, Elizabeth, Modave, François, Bian, Jiang
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 23.07.2018; BioMed Central; BMC
• Subjects: Axioms; Cancer; Cancer survival; Cardiovascular diseases; Census of Population; Commuting; Computer simulation; Computers; Coronary artery disease; Data analysis; Data integration; Data mining; Data processing; Databases, Factual; Datasets; Electronic health records; Environmental factors; Female; Hazardous materials; Health risk assessment; Heart diseases; Humans; Information Storage and Retrieval; Integration; Integrative data analysis; Lung cancer; Male; Mapping; Medical prognosis; Medical research; Medicare; Methods; Neoplasms; Oncology; Ontology; Organic chemicals; Relational data bases; Reproducibility; Reproducibility of Results; Risk analysis; Risk factors; Semantic data integration; Semantic web; Semantics; Smoking; Surveillance; Survival; Survival Analysis; Systems Integration; Technology application; Thesauri; Vocabularies & taxonomies; Vocabulary, Controlled; Integrative data analysis; Semantic web; Semantic data integration; Ontology; Cancer survival
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-018-0636-4

Cancer is the second leading cause of death in the United States, exceeded only by heart disease. Extant cancer survival analyses have primarily focused on individual-level factors due to limited data availability from a single data source. There is a need to integrate data from different sources to simultaneously study as much risk factors as possible. Thus, we proposed an ontology-based approach to integrate heterogeneous datasets addressing key data integration challenges. Following best practices in ontology engineering, we created the Ontology for Cancer Research Variables (OCRV) adapting existing semantic resources such as the National Cancer Institute (NCI) Thesaurus. Using the global-as-view data integration approach, we created mapping axioms to link the data elements in different sources to OCRV. Implemented upon the Ontop platform, we built a data integration pipeline to query, extract, and transform data in relational databases using semantic queries into a pooled dataset according to the downstream multi-level Integrative Data Analysis (IDA) needs. Based on our use cases in the cancer survival IDA, we created tailored ontological structures in OCRV to facilitate the data integration tasks. Specifically, we created a flexible framework addressing key integration challenges: (1) using a shared, controlled vocabulary to make data understandable to both human and computers, (2) explicitly modeling the semantic relationships makes it possible to compute and reason with the data, (3) linking patients to contextual and environmental factors through geographic variables, (4) being able to document the data manipulation and integration processes clearly in the ontologies. Using an ontology-based data integration approach not only standardizes the definitions of data variables through a common, controlled vocabulary, but also makes the semantic relationships among variables from different sources explicit and clear to all users of the same datasets. Such an approach resolves the ambiguity in variable selection, extraction and integration processes and thus improve reproducibility of the IDA.