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，Tim Berners-Lee引入了语义网这个术语，以指代可以被视为互联网未来的东西：一个机器可解释内容的网络，可以由自动代理以有意义的方式处理。由于实现这一雄心勃勃的目标需要相关领域知识的解释和形式化，本体语言，如RDFS和OWL已经成为一种明确的知识规范的手段。然而，由Tim Berners-Lee所设想的语义网的实现，以及智能的、基于推理的应用程序的广泛使用，仍然受到本体资源缺乏的阻碍。Web上大量的RDF三元组形式的链接数据可以被认为是向语义Web迈出的重要一步，但它缺乏必要的表达能力以及语义和语法准确性，而这是逻辑推理不可或缺的要求，产生不明显的结论。在这个项目中，我们将开发新的方法来获取结构化的知识表示，利用现有的内容在Web上的数据。通过将逻辑和统计关系学习应用于大量的关联数据，同时尽可能地重用手动设计的正式本体，我们希望提供自举实现语义Web的方法。该项目的一个重大贡献将是开发新颖的综合学习方法，这些方法可以处理关联开放数据在可扩展性和鲁棒性方面的特殊要求。实验将证明我们的方法的效率和有效性，当涉及到不同的领域和应用场景，最核心的任务是自动本体工程支持和调试的知识库。

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