EAGER: Visual Analytics for Ontology Matching

开发本体是为了提供特定领域的语义，并支持信息检索、推理和知识发现。然而，随着单独的组开发本体，需要组合或匹配本体以支持跨异构源的连接信息。本体匹配是一个复杂的过程，需要涉及几种类型的匹配算法，这些算法考虑了本体的句法、词汇、结构、实例和逻辑特征。目前本体匹配系统为用户提供的理解和评价结果的支持非常有限，因此本体匹配是一项艰巨且耗时的任务。这个探索性的项目专注于开发一种新的本体匹配方法，该方法使用视觉分析来指导用户进行匹配。它有望提高结果本体的质量，同时还减少专家参与本体匹配的时间和精力。视觉分析是信息可视化、数据分析和数据转换的交汇点。这个项目探索了视觉分析的潜力，以有效地辅助领域专家和本体研究人员在本体匹配过程中的实时决策。该项目围绕三个关键的研究挑战进行组织：(1)可视化：数据和分析提取的特征需要编码成可有效操作的丰富可视化。具体地说，可视化应该很好地适合于复杂的转换，从而有助于识别趋势或模式。(2)体系结构：自动匹配和可视分析模块之间的交互是提议的方法的核心。所设想的架构将支持质量受控的反馈循环，其中用户将介入以改变系统的分析和视觉参数。(3)性能评估：将开发性能测量以客观地识别在用户节省的工作和所建议的本体匹配的视觉分析方法所实现的匹配结果的质量方面所获得的收益。如果成功，这一概念验证项目有望在有效的本体匹配方面做出重大贡献，这反过来将使对复杂、异质和分布式数据的语义丰富的访问能够在不同领域中为越来越多的用户所用。研究结果，包括开发的软件，将通过项目网站(http://agreementmaker.org/wiki/index.php/Visual_Analytics).提供该项目为学生提供研究经验，研究结果将包括在计算机科学课程中。

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.