HermiT: Reasoning with Large Ontologies

本体是术语的正式词汇表，通常由用户社区共享。本体论最突出的应用领域之一是医学和生命科学。例如，医学临床术语系统化术语(SNOMED CT)是英国卫生服务的国家信息技术计划(NPFIT)中使用的临床本体论。其他例子包括Galen，基础解剖学模型(FMA)，国家癌症研究所(NCI)同义词库，以及OBO Foundry-一个包含约80个生物医学本体论的知识库。这些本体论正在逐渐取代现有的医学分类，并将为收集和共享医学知识提供未来的平台。使用本体捕获医疗记录将减少数据误解的可能性，并将使不同应用程序和机构之间的信息交换成为可能。医学本体与描述逻辑(DLS)密切相关，描述逻辑为许多本体语言提供了形式基础，最著名的是W3C标准化Web本体语言(OWL)。所有上述本体现在都在OWL中可用，因此在描述逻辑中也可用。医学本体论的开发者已经认识到使用DLS的众多好处，例如不同建模结构的清晰和明确的语义，表现力和计算复杂性之间的众所周知的权衡，以及可证明正确的推理器和工具的可用性。本体论的开发和应用关键依赖于推理。本体分类，即将类组织到专门化/泛化层次结构中，是一项在本体开发过程中发挥主要作用的推理任务：它提供了对潜在建模错误的检测，例如不一致的类描述和遗漏的子类关系。例如，当使用DL推理器FACT++对NHS使用的SNOMED CT版本进行分类时，检测到大约180个缺失的子类关系。查询应答是另一种推理任务，主要用于基于本体的信息检索；例如，在临床应用中，查询应答可能用于检索所有患有坚果过敏的患者。尽管有令人印象深刻的最先进的状态，现代医学本体论对基于数字图书馆的语言的理论和实践都提出了重大挑战。现有的推理机可以有效地处理一些大型本体，如NCI，但许多重要的本体仍然超出了现有工具的能力范围。例如，现有的推理机都不能成功地对Galen或FMA进行分类。应用程序目前需要绕过这些限制，例如，通过使用可被成功处理的本体子集。例如，通常在实践中使用的Galen版本仅包含完整版本公理的20%左右；这减少了概念之间的交互，从而使本体可处理。然而，这在实践中是非常不可取的，因为它减少了覆盖范围，削弱了领域的概念化，并可能阻止对建模错误的检测。此外，本体使用的数据量可能比本体本身大几个数量级。例如，在一家医院对患者的医疗记录进行注释可以很容易地产生由数亿个事实组成的数据，而在国家层面上聚合可能会产生数十亿个事实。现有的推理机无法处理这样的数据量，特别是当使用Galen和FMA等本体作为模式时更是如此。本项目的目标是开发可扩展的推理算法和能够有效处理大型复杂本体和大数据集的原型实现。开发这样的推理机将是许多基于本体的应用成功的关键。

Ontologies are formal vocabularies of terms, often shared by a community of users. One of the most prominent application areas of ontologies is medicine and the life sciences. For example, the Systematised Nomenclature of Medicine Clinical Terms (SNOMED CT) is a clinical ontology which is being used in the UK Health Service's National Programme for Information Technology (NPfIT). Other examples include GALEN, the Foundational Model of Anatomy (FMA), the National Cancer Institute (NCI) Thesaurus, and the OBO Foundry -- a repository containing about 80 biomedical ontologies.These ontologies are gradually superseding existing medical classifications and will provide the future platforms for gathering and sharing medical knowledge. Capturing medical records using ontologies will reduce the possibility for data misinterpretation, and will enable information exchange between different applications and institutions. Medical ontologies are strongly related to description logics (DLs), which provide the formal basis for many ontology languages, most notably the W3C standardised Web Ontology Language (OWL). All the above mentioned ontologies are nowadays available in OWL and, therefore, in a description logic. The developers of medical ontologies have recognised the numerous benefits of using DLs, such as the clear and unambiguous semantics for different modelling constructs, the well-understood tradeoffs between expressivity and computational complexity, and the availability of provably correct reasoners and tools.The development and application of ontologies crucially depend on reasoning. Ontology classification, i.e., organising classes into a specialisation/generalisation hierarchy, is a reasoning task that plays a major role during ontology development: it provides for the detection of potential modelling errors such as inconsistent class descriptions and missing sub-class relationships. For example, about 180 missing sub-class relationships were detected when the version of SNOMED CT used by the NHS was classified using the DL reasoner FaCT++. Query answering is another reasoning task that is mainly used during ontology-based information retrieval; e.g., in clinical applications query answering might be used to retrieve all patients that suffer from nut allergies . Despite the impressive state-of-the-art, modern medical ontologies pose significant challenges to both the theory and practice of DL-based languages. Existing reasoners can efficiently deal with some large ontologies, such as NCI, but many important ontologies are still beyond the reach of available tools. For example, none of the existing reasoners can successfully classify either GALEN or FMA. Applications currently need to work around these limitations, e.g., by using subsets of ontologies that can be successfully processed. For example, the version of GALEN typically used in practice contains only about 20% of the axioms of the full version; this reduces the interaction between concepts and thus makes the ontology processable . This is, however, highly undesirable in practice, because it reduces coverage, weakens the conceptualisation of the domain and may prevent the detection of modelling errors.Furthermore, the amount of data used with ontologies can be orders of magnitude larger than the ontology itself. For example, the annotation of patients' medical records in a single hospital can easily produce data consisting of hundreds of millions of facts, and aggregation at a national level might produce billions of facts. Existing reasoners cannot cope with such data volumes, especially not if ontologies such as GALEN and FMA are used as schemata.The goal of this project is to develop scalable reasoning algorithms and a prototypical implementation that can efficiently deal with large and complex ontologies and large data sets. Developing such a reasoner will be critical to the success of many ontology based applications.