EAGER: Visual Analytics for Ontology Matching

University Of Illinois At Chicago, Chicago IL

Investigators

Abstract

Ontologies are developed to provide semantics for a particular domain and to support information retrieval, reasoning and knowledge discovery. However, as separate groups develop ontologies, there is a need to combine or match ontologies to support connecting information across heterogeneous sources. Ontology matching is a complicated process that stems from the need to involve several types of matching algorithms that take into account syntactic, lexical, structural, instance, and logic features of the ontologies. The current support provided to users to understand and evaluate the results provided by ontology matching systems is very limited; therefore ontology matching is an arduous and time consuming task. This exploratory project focuses on development of a novel approach to ontology matching that employs visual analytics to guide the users in the process. It is expected to result in increased quality of resulting ontologies while also reducing the time and effort of experts involved in ontology matching. Visual analytics is at the confluence of information visualization, data analytics, and data transformation. This project explores the potential of visual analytics to effectively assist real-time decisions by domain experts and ontology researchers alike during the ontology matching process. The project is organized around three key research challenges: (1) Visualization: Data and analytically extracted features need to be encoded into rich visualizations that can be effectively manipulated. In particular, visualizations should lend themselves well to complex transformations that facilitate the discernment of trends or patterns. (2) Architecture: The interaction between the automatic matching and the visual analytics modules is central to the proposed approach. The envisioned architecture will support a quality-controlled feedback loop in which users will intervene to change the analytic and visual parameters of the system. (3) Performance evaluation: Performance measures will be developed to objectively identify the obtained gains in terms of the effort saved by users and of the quality of the matching results as enabled by the proposed visual analytics approach to ontology matching. If successful, this proof-of-concept project is expected to make a significant contribution in effective ontology matching that in turn will enable semantically enriched access to complex, heterogeneous, and distributed data to an increasing number of users in a variety of domains. Research results, including developed software, will be made available via the project web site (http://agreementmaker.org/wiki/index.php/Visual_Analytics). The project provides research experience to students and results from this research will be included in the computer science curriculum.