Generation of Ontologies from Linked Data

从关联数据生成本体

• 批准号: 233626375
• 负责人: Professor Dr. Jens Lehmann
• 金额: --
• 依托单位: Lehrstuhl für Künstliche Intelligenz
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2013
• 资助国家: 德国
• 起止时间: 2012-12-31 至 2015-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/233626375?language=en
• 关键词: Generation; Ontologies; Linked; Data

In 2001, Tim Berners-Lee introduced the term Semantic Web in order to refer to what can be perceived as the future of the internet: a web of machine-interpretable content that can be processed by automatic agents in a meaningful way. Since achieving this ambitious goal requires both an explication and formalization of relevant domain knowledge, ontology languages such as RDFS and OWL have emerged as a means for unambiguous knowledge specification. However, the realization of the semantic web as envisioned by Tim Berners-Lee and even more the wide-spread use of intelligent, reasoning-based applications is still hampered by the lack of ontological resources. The vast amount of linked data in the form of RDF triples which is out there on the Web can be considered an important step forward on the way to the semantic web, but it lacks the necessary degree of expressivity as well as the semantic and syntactic accuracy which is an indispensable requirement for logical inference that yields non-obvious conclusions. In this project, we will develop new methods for the acquisition of structured knowledge representations which leverage existing contents on the Web of data. By applying logical and statistical relational learning to the vast amounts of linked data while reusing manually engineered formal ontologies whenever possible we hope to provide means for bootstrapping the realization of the semantic web. A significant contribution of the project will be the development of novel, integrated learning approaches, which can handle the particular requirements of Linked Open Data in terms of scalability and robustness. Experiments will demonstrate the efficiency and effectiveness of our methods when it comes to different domains and application scenarios the most central one being the task of automatic ontology engineering support and debugging of knowledge bases.

2001 年，蒂姆·伯纳斯·李 (Tim Berners-Lee) 引入了“语义网”一词，以指代互联网的未来：由机器可解释的内容组成的网络，可以由自动代理以有意义的方式进行处理。由于实现这一雄心勃勃的目标需要对相关领域知识进行解释和形式化，因此 RDFS 和 OWL 等本体语言已经作为明确知识规范的手段而出现。然而，蒂姆·伯纳斯·李所设想的语义网的实现，乃至基于推理的智能应用的广泛使用，仍然因本体资源的缺乏而受到阻碍。 Web 上以 RDF 三元组形式存在的大量链接数据可以被视为迈向语义 Web 的重要一步，但它缺乏必要的表达程度以及语义和句法准确性。这是产生非显而易见结论的逻辑推理不可或缺的要求。在这个项目中，我们将开发利用数据网络上现有内容获取结构化知识表示的新方法。通过将逻辑和统计关系学习应用于大量链接数据，同时尽可能重用手动设计的形式本体，我们希望提供引导语义网实现的方法。该项目的一个重大贡献将是开发新颖的集成学习方法，该方法可以满足链接开放数据在可扩展性和稳健性方面的特殊要求。实验将证明我们的方法在不同领域和应用场景中的效率和有效性，其中最核心的是自动本体工程支持和知识库调试的任务。

项目成果

Inductive Lexical Learning of Class Expressions

类表达式的归纳词汇学习

• DOI: 10.1007/978-3-319-13704-9_4
• 发表时间: 2014
• 作者: Lorenz Bühmann;Daniel Fleischhacker;Jens Lehmann;André Melo;Johanna Völker
• 通讯作者: Johanna Völker

Detecting Errors in Numerical Linked Data Using Cross-Checked Outlier Detection

使用交叉检查异常值检测来检测数字关联数据中的错误

• DOI: 10.1007/978-3-319-11964-9_23
• 发表时间: 2014
• 作者: Daniel Fleischhacker;Heiko Paulheim;Volha Bryl;Johanna Völker;Christian Bizer
• 通讯作者: Christian Bizer