Score!: Scalable and Complete Reasoning with Incomplete Ontology Reasoners

工业、政府和医疗保健方面的决策越来越依赖于改进对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有效地处理大型数据集，利用机器可读的领域知识，并通过考虑知识和数据来回答查询。基于本体的信息系统（ois）是一种快速成熟的技术，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答案。已经为本体和查询语言开发了几个标准，包括RDF、OWL、owl2和SPARQL。OWL及其修订版owl2提供了一种强大而灵活的本体建模语言，可以捕获诸如类层次结构、不完整信息、负面信息等特征。OWL本体正在越来越多的应用程序中使用，并且正在成为访问、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程序仍然对ois的适用性提出了严峻的挑战。ois的适用性问题通常源于相互冲突的应用程序需求。-建模复杂的应用领域需要丰富的本体语言。-细粒度的信息访问需要强大的查询语言。-回答对大型数据集的查询需要可扩展的推理器。-依赖于信息访问的关键决策要求查询答案要么是完整的，要么是完全理解不完整的。由于相关推理问题的高最坏情况复杂性，可扩展性通常与使用强大的本体和查询语言相冲突，许多应用放弃完整性来实现期望的可扩展性。因此，现有的ois不能满足这些需求中的一个或多个：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整性。我们在这个项目中的目标是为满足上述所有需求的新一代ois奠定基础，从而提供表达能力、可伸缩性和完整性的理想组合。为了实现这样一个雄心勃勃的目标，我们观察到表达性、可扩展性和完整性之间的权衡所施加的限制适用于语言层面：也就是说，它们涉及到在给定的本体、查询和数据建模语言中表达的每个本体、查询和数据集的最坏情况复杂性界限。然而，与特定应用程序相关的本体、查询和数据集的类别受到更多的限制。例如，尽管应用程序数据通常是未知的或经常更改，但本体本身在设计时是固定的，或者很少更改。因此，对于给定的查询语言和本体语言来说，通常已知不完整的推理器可能会产生与当前应用程序的完整推理器相同的结果。识别这种情况是具有挑战性的，但它将具有巨大的附加价值：应用程序可以利用可扩展的不完整推理器，同时仍然享受完整性保证，从而实现“两全其美”。我们相信，我们的主要目标可以通过设计面向本体、查询和与手头应用程序相关的数据集进行优化的ois来实现。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。

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.