Score!: Scalable and Complete Reasoning with Incomplete Ontology Reasoners

Score!：使用不完整本体推理器进行可扩展且完整的推理

• 批准号: EP/J020214/1
• 负责人: Bernardo Cuenca Grau
• 金额: $ 70.81万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FJ020214%2F1
• 关键词: Score; Scalable; Complete; Reasoning; Incomplete

Decisions in industry, government and health care increasingly depend on improved access to and processing of digital information. This has led to a pressing demand for more powerful and flexible information systems. New generation information systems will need to efficiently process large data sets, exploit machine-readable domain knowledge, and answer queries by taking into account both knowledge and data.Ontology-based information systems (OISs) constitute a rapidly maturing technology with the potential to meet these requirements. An ontology provides a vocabulary of terms that are familiar to the user, together with axioms describing the meaning of those terms. OISs can exploit the rich domain knowledge in an ontology to provide a unified view of the data and enrich query answers with implicit information using an automated reasoner.Several standards for ontology and query languages have been developed, including RDF, OWL, OWL 2, and SPARQL. OWL and its revision OWL 2 provide a powerful and flexible ontology modelling language that can capture features such as class hierarchies, incomplete information, negative information, and so on. OWL ontologies are being used in an increasing range of applications, and are becoming a core technology for accessing, gathering, and sharing knowledge and data.Applications involving large amounts of data, however, still pose serious challenges to the applicability of OISs. Problems in the applicability of OISs typically originate from conflicting application requirements.- Modelling complex application domains requires rich ontology languages.- Fine-grained access to information requires powerful query languages.- Answering queries over large data sets requires scalable reasoners.- Critical decisions that depend on access to information require query answers that are either complete, or where the incompleteness is well-understood.Due to high worst-case complexity of the relevant reasoning problems, scalability is usually in conflict with the use of powerful ontology and query languages, and many applications give up completeness to achieve the desired scalability. As a result, existing OISs fail to meet one or more of these requirements: they support only weak ontology or query languages, they do not scale to the required volumes of data, or they do not provide guarantees as to the completeness of query answers.Our goal in this project is to lay the foundations for a new generation of OISs that meet all the aforerementioned requirements, thus providing the ideal combination of expressive power, scalability and completeness.To accomplish such an ambitious goal, we observe that the limitations imposed by the trade-offs between expressivity, scalability and completeness apply at the language level: that is, they involve worst-case complexity bounds for every ontology, query, and data set expressed in given ontology, query and data modeling languages. The class of ontologies, queries and data sets that are relevant to a particular application is, however, much more restricted. For example, although application data is often unknown or frequently changing, the ontology itself is fixed at design time, or changes infrequently. As a result, a reasoner known to be incomplete in general for given query and ontology languages might yield the same results as a complete reasoner for the application at hand. Identifying such cases is challenging, but it would have tremendous added value: applications could exploit scalable incomplete reasoners while still enjoying completeness guarantees, thus achieving 'the best of both worlds'.We believe that our main goal can be accomplished by designing OISs that are optimised for the ontologies, queries and data sets relevant to the application at hand. Such OISs would maximise scalability while ensuring completeness of query answers, even for rich ontologies, large-scale data sets, and complex user queries.

工业、政府和医疗保健的决策越来越依赖于改善数字信息的获取和处理。这导致了对更强大、更灵活的信息系统的迫切需求。新一代信息系统将需要高效地处理大数据集，开发机器可读的领域知识，并同时考虑知识和数据来回答查询。基于本体的信息系统(OIS)是一种迅速成熟的技术，具有满足这些需求的潜力。本体提供了用户熟悉的术语词汇表，以及描述这些术语含义的公理。OIS可以利用本体中丰富的领域知识来提供统一的数据视图，并使用自动推理器用隐含信息丰富查询结果。目前已经开发了几种本体和查询语言标准，包括RDF、OWL、OWL 2和SPARQL。OWL及其修订版OWL 2提供了一种强大而灵活的本体建模语言，可以捕获类层次结构、不完全信息、否定信息等特征。OWL本体在越来越多的应用中得到应用，并正在成为访问、收集和共享知识和数据的核心技术。然而，涉及大量数据的应用仍然对OIS的适用性构成了严峻的挑战。OIS的适用性问题通常源于相互冲突的应用需求。-为复杂的应用领域建模需要丰富的本体论语言。-细粒度的信息访问需要强大的查询语言。-回答大数据集上的查询需要可伸缩的推理器。-依赖于对信息的访问的关键决策要求查询答案要么是完整的，要么是不完全的。由于相关推理问题的最坏情况下的高度复杂性，可伸缩性通常与使用强大的本体和查询语言相冲突，并且许多应用放弃完备性以实现所需的可伸缩性。因此，现有的OIS无法满足这些要求中的一个或多个：它们只支持弱本体或查询语言，它们不能扩展到所需的数据量，或者它们不能保证查询答案的完备性。我们在这个项目中的目标是为满足所有上述要求的新一代OIS奠定基础，从而提供表达能力、可扩展性和完备性的理想组合。为了实现这样一个雄心勃勃的目标，我们观察到在表现性、可伸缩性和完备性之间的权衡所施加的限制适用于语言级别：即，它们涉及到每个本体、查询、以及以给定的本体、查询和数据建模语言表示的数据集。然而，与特定应用相关的本体、查询和数据集的类别要受限得多。例如，尽管应用程序数据通常是未知的或频繁变化的，但本体本身在设计时是固定的，或者不经常变化。因此，对于给定的查询和本体语言，已知的推理机通常是不完全的，可能会产生与手头应用程序的完全推理机相同的结果。识别这类情况具有挑战性，但它将具有巨大的附加值：应用程序可以利用可扩展的不完全推理机，同时仍享有完整性保证，从而实现“两全其美”。我们相信，通过设计针对与手头应用程序相关的本体、查询和数据集进行优化的OIS，可以实现我们的主要目标。这样的OIS将最大化可伸缩性，同时确保查询答案的完整性，即使对于丰富的本体、大规模数据集和复杂的用户查询也是如此。

项目成果

Is Your Ontology as Hard as You Think? Rewriting Ontologies into Simpler DLs

你的本体论像你想象的那么难吗？

• 发表时间: 2014
• 期刊: Proceedings of the 27th International Workshop on Description Logics (DL)
• 作者: Carral D

Acyclicity Notions for Existential Rules and Their Application to Query Answering in Ontologies

• DOI: 10.1613/jair.3949
• 发表时间: 2013-05
• 期刊: J. Artif. Intell. Res.
• 作者: B. C. Grau;Ian Horrocks;M. Krötzsch;C. Kupke;Despoina Magka;B. Motik;Zhe Wang

Faceted search over RDF-based knowledge graphs

• DOI: 10.1016/j.websem.2015.12.002
• 发表时间: 2016-03-01
• 期刊: JOURNAL OF WEB SEMANTICS
• 影响因子: 2.5
• 作者: Arenas, Marcelo;Grau, Bernardo Cuenca;Zheleznyakov, Dmitriy
• 通讯作者: Zheleznyakov, Dmitriy

Automated Reasoning

自动推理

• DOI: 10.1007/978-3-319-08587-6_36
• 发表时间: 2014
• 作者: Carral D

Ontology Module Extraction via Datalog Reasoning

• DOI: 10.1609/aaai.v29i1.9418
• 发表时间: 2014-11
• 期刊: ArXiv
• 作者: A. A. Romero-A.;M. Kaminski;B. C. Grau;Ian Horrocks