Web-based interactive mapping from data dictionaries to ontologies, with an application to cancer registry
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RESEARCH
Web‑based interactive mapping from data dictionaries to ontologies, with an application to cancer registry Shiqiang Tao1†, Ningzhou Zeng2†, Isaac Hands3, Joseph Hurt‑Mueller3, Eric B. Durbin3,4, Licong Cui1 and Guo‑Qiang Zhang1*
Abstract Background: The Kentucky Cancer Registry (KCR) is a central cancer registry for the state of Kentucky that receives data about incident cancer cases from all healthcare facilities in the state within 6 months of diagnosis. Similar to all other U.S. and Canadian cancer registries, KCR uses a data dictionary provided by the North American Association of Central Cancer Registries (NAACCR) for standardized data entry. The NAACCR data dictionary is not an ontological system. Mapping between the NAACCR data dictionary and the National Cancer Institute (NCI) Thesaurus (NCIt) will facilitate the enrichment, dissemination and utilization of cancer registry data. We introduce a web-based system, called Interactive Mapping Interface (IMI), for creating mappings from data dictionaries to ontologies, in particular from NAACCR to NCIt. Method: IMI has been designed as a general approach with three components: (1) ontology library; (2) mapping interface; and (3) recommendation engine. The ontology library provides a list of ontologies as targets for building mappings. The mapping interface consists of six modules: project management, mapping dashboard, access control, logs and comments, hierarchical visualization, and result review and export. The built-in recommendation engine automatically identifies a list of candidate concepts to facilitate the mapping process. Results: We report the architecture design and interface features of IMI. To validate our approach, we implemented an IMI prototype and pilot-tested features using the IMI interface to map a sample set of NAACCR data elements to NCIt concepts. 47 out of 301 NAACCR data elements have been mapped to NCIt concepts. Five branches of hierarchi‑ cal tree have been identified from these mapped concepts for visual inspection. Conclusions: IMI provides an interactive, web-based interface for building mappings from data dictionaries to ontologies. Although our pilot-testing scope is limited, our results demonstrate feasibility using IMI for semantic enrichment of cancer registry data by mapping NAACCR data elements to NCIt concepts. Keywords: Data dictionary, Ontology, Concept mapping
*Correspondence: guo‑[email protected] † Shiqiang Tao and Ningzhou Zeng have contributed equally 1 The University of Texas Health Science Center at Houston, Houston, TX, USA Full list of author information is available at the end of the article
Background Ontologies have been commonly used to facilitate data management, data sharing, and information retrieval in biomedicine. To enhance semantic interoperability among ontologies, significant effort has been spent to study algorithms mapping concepts and relations between different ontologies [1]. Several ontology mapping systems and
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