ExODA: Integrating Description Logics and Database Technologies for Expressive Ontology-Based Data Access

Web上的半结构化、重叠和语义相关的数据源目前正以惊人的速度激增，这就产生了对更强大、更灵活的信息系统（IS）的需求。这新一代的信息系统将需要集成不完整的和半结构化的信息，从异构的来源，采用丰富和灵活的模式，并回答查询，同时考虑到知识和data. Ontology为基础的数据访问最近被提出作为这样的系统的架构原则。其主要思想是通过在本体中描述相关领域来开发数据的统一视图，然后提供用于询问查询的词汇表。IS可以使用本体论语句，如概念层次结构，以获得新的事实，从而丰富查询答案与隐含的知识。这种思想已经被结合到诸如QuOnto、Owlgres、ROWLKit和REQUIEM的系统中，以及诸如RACER、FaCT++、Pellet和HermiT的本体推理器中。首先，本体语言的建模能力通常不足以满足实际用例。为了实现良好的计算性能，本体语言通常只能描述树形关系;此外，（除了一些值得注意的例外）它们通常只支持一元和二元谓词。最后，本体语言通常采用开放世界的假设，然而，当回答查询大量的数据，封闭世界的假设（CWA）往往是更appropriate.Second，查询回答设施在现有的基于本体的IS通常不规模的数据集在实践中遇到的。到目前为止，解决这个问题的方法都集中在减少本体语言的表达能力，甚至进一步，以获得正式的易处理性保证。这显然加剧了第一个问题（受限的建模能力），而不一定在实践中提供强大的可扩展性。数据库理论和实践可以提供这些问题的部分解决方案。在数据库中，复杂的域可以使用依赖关系来描述。依赖关系有多种不同的使用方式：它们通常用作完整性约束--检查数据库实例是否包含域描述中指定的所有数据;然而，依赖关系也可以类似于本体来导出隐式知识。在CWA框架下，将依赖关系视为完整性约束并回答查询，使得实用的关系数据库管理系统（RDBMS）能够扩展到非常大的数据集，然而，单靠数据库技术并不能满足基于本体的信息系统的所有要求。特别是，依赖关系通常无法对任意大的结构进行建模，因此无法覆盖所有实际建模用例。此外，将在实际关系数据库管理系统中使用的查询应答技术推广到必须考虑信息派生依赖关系的情况仍然是一个悬而未决的问题。因此，我们认为，下一代基于本体的信息系统应该基于本体和数据库系统和技术的综合和扩展，提供类似于当前关系数据库管理系统的数据处理能力，但具有丰富、灵活的模式，并与数据紧密结合。然而，为了实现这一宏伟目标，必须解决一些具有挑战性的基本问题。首先，本体和依赖语言需要统一在一个连贯的理论框架中。第二，有必要确定框架的片段，这些片段可能表现出强大的可伸缩性，但仍然可以支持现实的用例。第三，有必要设计有效的算法技术，以形成实用IS的基础。

Sources of semi-structured, overlapping, and semantically-related data on the Web are currently proliferating at a phenomenal rate, which has created a demand for more powerful and flexible information systems (ISs). This new generation of ISs will need to integrate incomplete and semi-structured information from heterogeneous sources, employ rich and flexible schemas, and answer queries by taking into account both knowledge and data.Ontology-based data access has recently been proposed as an architectural principle for such systems. The main idea is to develop a unified view of the data by describing the relevant domain in an ontology, which then provides the vocabulary used to ask queries. The IS can use ontological statements, such as the concept hierarchy, to derive new facts and thus enrich query answers with implicit knowledge. This idea has been incorporated into systems such as QuOnto, Owlgres, ROWLKit, and REQUIEM, and ontology reasoners such as RACER, FaCT++, Pellet, and HermiT.Such systems suffer from two main problems. First, the modelling capabilities of ontology languages are often insufficient for practical use cases. In order to achieve favourable computational properties, ontology languages are usually capable of describing only tree-shaped relationships; furthermore, (with some notable exceptions) they usually support only unary and binary predicates. Finally, ontology languages typically employ the open world assumption; however, when answering queries over large amounts of data, the closed world assumption (CWA) is often more appropriate.Second, query answering facilities in existing ontology-based ISs typically do not scale to data sets commonly encountered in practice. Up to now, approaches to addressing this problem have focused on reducing the expressivity of the ontology language even further in order to obtain formal tractability guarantees. This obviously exacerbates the first problem (restricted modelling capabilities), while not necessarily delivering robust scalability in practice.Database theory and practice can provide partial solutions to these problems. In databases, complex domains can be described using dependencies. Dependencies are used in a number of different ways: they are often used as integrity constraints--checks that verify whether a database instance includes all data specified in the domain description; however, dependencies can also be used similarly to ontologies to derive implicit knowledge. Treating dependencies as integrity constraints and answering queries under CWA has allowed practical relational database management systems (RDBMSs) to scale to very large data sets.Database techniques alone do not, however, satisfy all the requirements for an ontology-based IS. In particular, dependencies often cannot model arbitrarily large structures and thus do not cover all practical modelling use cases. Furthermore, generalising the query answering techniques used in practical RDBMSs to the case where information deriving dependencies must be taken into account is still an open problem.We therefore believe that the next generation of ontology-based ISs should be based on a synthesis and an extension of ontology and database systems and techniques, providing data handling capabilities similar to current RDBMSs, but with schemas that are rich, flexible, and tightly integrated with the data. In order to achieve this ambitions goal, however, a number of challenging fundamental problems must be solved. First, ontology and dependency languages need to be unified in a coherent theoretical framework. Second, it will be necessary to identify fragments of the framework that are likely to exhibit robust scalability but can still support realistic use cases. Third, it will be necessary to devise effective algorithmic techniques that can form the basis of practical ISs.