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命名法（SNOMED CT）是一个临床本体，正在英国卫生服务的国家信息技术计划（NPfIT）中使用。其他的例子包括GALEN，基础解剖学模型（FMA），国家癌症研究所（NCI）词库，以及OBO铸造厂——一个包含大约80个生物医学本体的存储库。这些本体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读的可能性，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（dl）密切相关，描述逻辑为许多本体语言提供了形式化基础，最著名的是W3C标准化的Web本体语言（OWL）。所有上面提到的本体现在都可以在OWL中使用，因此也可以在描述逻辑中使用。医学本体的开发人员已经认识到使用dl的许多好处，例如不同建模构造的清晰和明确的语义，表达性和计算复杂性之间的良好权衡，以及可证明正确的推理器和工具的可用性。本体的发展和应用至关重要地依赖于推理。本体分类，即将类组织成专门化/泛化层次结构，是一项推理任务，在本体开发过程中起着重要作用：它提供了检测潜在的建模错误，如不一致的类描述和缺失的子类关系。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。查询回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，查询回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先进技术，现代医学本体对基于dl的语言的理论和实践都提出了重大挑战。现有的推理器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可用工具的范围。例如，现有的推理器都不能成功地对GALEN或FMA进行分类。应用程序目前需要绕过这些限制，例如，通过使用可以成功处理的本体子集。例如，实践中通常使用的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，从而使本体具有可处理性。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，并且可能阻止建模错误的检测。此外，与本体一起使用的数据量可能比本体本身大几个数量级。例如，对单个医院的患者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，而在国家一级进行汇总可能会产生数十亿个事实。现有的推理器无法处理这样的数据量，特别是当GALEN和FMA等本体被用作模式时。该项目的目标是开发可扩展的推理算法和原型实现，可以有效地处理大型复杂的本体和大型数据集。开发这样一个推理器对于许多基于本体的应用程序的成功至关重要。

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.