NSF Award: III: Small: Enhancing Ontology Matching with Visual Analytics

PI: Maria Isabel Cruz

Award Number: 1618126


An ontology is a representation of a domain, be it biomedical, business, environmental, or others. In a world where data are predominantly heterogeneous, ontology matching establishes correspondences between the concepts of two ontologies, thus effectively bridging across two distinct domains or two different representations of the same domain. Ontology matching is therefore a fundamental tool for data integration, that is, for the creation of a homogeneous gateway to disparate data. Ontology matching systems are made up of several matching algorithms, called matchers. Different matching tasks require different matchers, thus there are various configuration and parameter choices to be made, resulting in a multi-dimensional problem whose tuning requires considerable effort and expertise. However, most ontology matching systems operate as a black box offering no insight as to how the output---a set of mappings among ontology concepts, called alignment---is generated. These systems do not usually offer the opportunity to the domain experts to validate automatically generated mappings, so as to gain control over the matching process. In this project, we use visual analytics, a combination of visualization and analytics to facilitate ontology matching. Users interact with a visual representation of the matching process and validate mappings that are ranked by underlying analytical methods. This award investigates visual analytics methods and studies their potential benefits. Our collaboration with partners in the biological domain will ensure the practical relevance of our research. From an educational viewpoint, the PI is spearheading a new Data Science curriculum in Computer Science, which can incorporate the main aspects of the proposed research and will train a graduate student and postdoc in this multidisciplinary field.

Driven by data integration needs in a wide range of domains, the field of ontology matching has been prospering. However, the use of visual analytics remains largely unexplored. The proposed research will combine the power of visual analytics with ontology matching to: (1) open up the ontology matching process so as to facilitate its configuration by domain experts; (2) reduce the number of mappings to be validated by the experts so as to achieve high quality results with minimum effort; and (3) investigate a methodology to evaluate the benefits of combining ontology matching with visual analytics. For the visualization design, a principled approach will be followed that provides prescriptive guidance for determining appropriate evaluation approaches, while for the manipulation of visualized data a taxonomy of interactive operations will be used. Further, the design and analysis of the workflow that describes the interactive nature of the overall process will facilitate the study of the complex interdependencies between the data manipulation and the visualization components. The web site of this project is available at https://www.cs.uic.edu/Cruz/OntologyMatchingVisualAnalytics.

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Topic revision: r3 - 2016-08-30 - 14:20:31 - Main.ifcruz
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