EAGER: Visual Analytics for Ontology Matching

本体的开发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，由于独立的组开发本体，因此需要联合收割机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，这是因为需要涉及几种类型的匹配算法，这些算法考虑到本体的语法、词汇、结构、实例和逻辑特征。目前的支持提供给用户理解和评价本体匹配系统提供的结果是非常有限的，因此本体匹配是一个艰巨和耗时的任务。这个探索性项目的重点是开发一种新的本体匹配方法，该方法采用可视化分析来指导用户进行匹配。它有望提高生成的本体的质量，同时减少参与本体匹配的专家的时间和精力。可视化分析是信息可视化、数据分析和数据转换的融合。该项目探讨了可视化分析的潜力，以有效地帮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为可以有效操作的丰富可视化。特别是，可视化应该很好地适用于复杂的转换，以促进趋势或模式的识别。(2)架构：自动匹配和可视化分析模块之间的交互是所提出的方法的核心。设想的架构将支持一个质量控制的反馈回路，其中用户将进行干预，以改变系统的分析和视觉参数。(3)业绩评价：将制定性能指标，以客观地确定所获得的收益，即用户节省的工作量和所提出的本体匹配可视化分析方法所实现的匹配结果的质量。如果成功，这一概念验证项目预计将在有效的本体匹配方面做出重大贡献，从而使语义丰富的访问复杂，异构，并将数据分发给各种领域中越来越多的用户。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，研究结果将纳入计算机科学课程。

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.