III: Small: Enhancing Ontology Matching with Visual Analytics

III：小：通过可视化分析增强本体匹配

• 批准号: 1618126
• 负责人: Abolfazl Asudeh
• 金额: $ 50万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1618126&HistoricalAwards=false
• 关键词: III; Small; Enhancing; Ontology; Matching

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 where parameter 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, in order to gain control over the matching process. In this project, visual analytics -- a combination of visualization and analytics to facilitate ontology matching -- is used to aid in the process of ontology matching. Users interact with a visual representation of the matching process and validate mappings that are ranked by underlying analytical methods. This project explores novel visual analytics methods and studies their potential benefits. The research team is collaborating with partners in the biological domain in order to ensure the practical relevance of the 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 project web site (https://www.cs.uic.edu/Cruz/OntologyMatchingVisualAnalytics) will provide access to research results and software will be disseminated via GitHub.

本体论是一个领域的代表，无论是生物医学，商业，环境还是其他人。在数据主要是异质性的世界中，本体匹配建立了两个本体论的概念之间的对应关系，因此有效地跨越了两个不同的域或同一域的两个不同表示。因此，本体匹配是数据集成的基本工具，也就是说，为创建均匀的网关而言。 本体匹配系统由几种匹配算法（称为匹配器）组成。不同的匹配任务需要不同的匹配器，因此要做出各种配置和参数选择，从而导致了一个多维问题，其中参数调整需要大量的努力和专业知识。但是，大多数本体匹配系统是一个黑匣子，对如何生成输出（称为对齐）中的一组映射的黑匣子。这些系统通常不会为域专家提供验证自动生成映射的机会，以便获得对匹配过程的控制。在该项目中，视觉分析 - 可视化和分析以促进本体匹配的组合 - 用于帮助进行本体匹配过程。用户与匹配过程的视觉表示相互作用，并验证通过潜在的分析方法排名的映射。该项目探讨了新颖的视觉分析方法，并研究了其潜在的好处。 研究团队正在与生物领域的合作伙伴合作，以确保研究的实际相关性。从教育的角度来看，PI负责计算机科学领域的新数据科学课程，该课程可以纳入拟议的研究的主要方面，并将在这个多学科领域中培训研究生和博士后。在广泛领域的数据集成需求的驱动下，本体匹配的领域一直在繁荣。但是，视觉分析的使用仍然在很大程度上尚未开发。拟议的研究将将视觉分析的力量与本体论匹配相结合：（1）打开本体论匹配过程，以促进域专家的配置； （2）减少专家验证的映射数量，以便以最小的努力获得高质量的结果； （3）研究一种方法，以评估将本体匹配与视觉分析相匹配的好处。对于可视化设计，将遵循一种原则性的方法，为确定适当的评估方法提供规范性指南，而对于可视化数据的操纵将使用交互式操作的分类法。此外，描述整个过程的互动性质的工作流程的设计和分析将有助于研究数据操作和可视化组件之间的复杂相互依存关系。项目网站（https://www.cs.uic.edu/cruz/ontology-matchingvisualanalytics）将提供对研究结果的访问，并且软件将通过github传播。

项目成果

Facet Annotation Using Reference Knowledge Bases

• DOI: 10.1145/3178876.3186020
• 发表时间: 2018-04
• 期刊: Proceedings of the 2018 World Wide Web Conference
• 作者: R. Porrini;M. Palmonari;I. Cruz

GUIDES: Geospatial Urban Infrastructure Data Engineering Solutions

指南：地理空间城市基础设施数据工程解决方案

• DOI: 10.1145/3139958.3139968
• 发表时间: 2017
• 期刊: 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
• 作者: Balasubramani, B. S.;Belingheri, O.;Boria, E. S.;Cruz, I. F.;Derrible, S.;Siciliano, M. D.
• 通讯作者: Siciliano, M. D.

A distantly supervised method for extracting spatio-temporal information from text

• DOI: 10.1145/2996913.2996967
• 发表时间: 2016-10
• 期刊: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
• 作者: Seyed Iman Mirrezaei;Bruno Martins;I. Cruz

OAEI 2016 results of AML

• 发表时间: 2016
• 作者: Daniel Faria;Catia Pesquita;B. Balasubramani;Catarina Martins;João Cardoso;H. Curado;Francisco M. Couto;I. Cruz

AML and AMLC Results for OAEI 2019

2019 年 OAEI AML 和 AMLC 结果

• 发表时间: 2019
• 期刊: Proceedings of the 14th International Workshop on Ontology Matching co-located with the 18th International Semantic Web Conference (ISWC
• 作者: Faria, Daniel;Pesquita, Catia;Tervo, Teemu;Couto, Francisco M;Cruz, Isabel F
• 通讯作者: Cruz, Isabel F