LogMap: Logic-based Methods for Ontology Mapping

在计算机科学中，本体是以计算机可以处理的格式对世界的某些方面进行的正式描述。例如，生物医学本体可能包含这样的信息：多关节关节炎是一种影响至少五个关节的关节炎，青少年关节炎是一种影响到13岁以下儿童的关节炎，多关节青少年关节炎是一种同时具有多关节和青少年的关节炎。本体在生物学和医学中被广泛使用。生物医学本体论的一个突出例子是SNOMED CT，它是NHS患者记录服务的核心组件。其他例子包括解剖学基础模型(FMA)和国家癌症研究所主题词库(NCI)。SNOMED CT、FMA和NCI等本体正在逐渐取代现有的医学分类，并正在成为访问、收集和共享医学知识和数据的核心平台。例如，本体可以用于以更智能的方式处理数据(例如，在医学应用程序的情况下，电子患者记录)：如果Johnsmith的医疗记录表明他是一个患有关节炎的10岁患者，并且他的膝盖、脚踝、手腕、肘部和髋关节受损，为了在基于本体的应用程序之间交换或迁移数据，在它们的本体之间建立对应(或映射)是至关重要的。例如，NCI和FMA之间的映射应该建立FMA术语心肌组织和NCI术语心肌组织是同义词。例如，使用该映射，计算机程序将能够将Paul Williams遭受的影响心肌梗死的数据存储从基于NCI的应用迁移到基于FMA的应用。由于现代本体的大小和复杂性，手动创建这种映射通常是不可行的。因此，自动生成本体之间的映射的问题(通常被称为本体匹配、本体对齐或本体映射问题)近年来被广泛研究。尽管本领域已经成熟，生物医学本体仍然是对现有技术的挑战，我们的最终目标是迎接这些挑战，为新一代生物医学信息系统的开发奠定基础。我们的主要研究假设是基于观察到现有的本体映射技术往往忽略了输入本体的基于逻辑的语义。其结果是，它们无法利用可用的语义，以及现代非语义学语言的高效推理服务。我们建议通过在本体映射过程的每个步骤中结合逻辑推理来重新思考该领域当前最先进技术的基础。我们还打算走得更远，使我们的技术切实可行，并准备在应用中使用。这项研究基于我们的初步经验证据，这些证据表明，在分析现实世界本体之间的现有映射时，基于逻辑的推理具有潜在的好处。我们预计，我们的结果将直接与生物医学领域基于本体的系统的用户相关，在生物医学领域，知识和数据集成是一个主要关注的问题。

In computer science, an ontology is a formal description of some aspect ofthe world in a format that a computer can process. For example, a bio-medical ontologymay contain information such as polyarticular arthritis is a kind of arthritis that affects at least five joints'', juvenile arthritis is a kind of arthritis that affects children up to the age of 13'', and polyarticular juvenile arthritis is the kind of arthritis that is both polyarticular and juvenile''.Ontologies are extensively used in biology and medicine. Aprominent example of a bio-medical ontology is SNOMED CT, which is a core component of the NHS patient recordservice. Other examples include the Foundational Model of Anatomy(FMA) and the National Cancer Institute Thesaurus (NCI).Ontologies such as SNOMED CT, FMA, and NCI are gradually superseding the existing medical classificationsand are becoming core platforms for accessing, gathering, and sharing medical knowledge and data.For example, ontologies can be used to process data (e.g., electronic patient records in the case of a medical application) in a more intelligent way: if JohnSmith's medical record states that he is a 10 years old patient suffering from arthritis, and who has damage in hisknee, ankle, wrist, elbow, and hip joints, then an ontology can be used to conclude that hesuffers from a kind of polyarticular juvenile arthritis.To exchange or migrate data between ontology-based applications,it is crucial to establish correspondences (or mappings) between their ontologies.For example, a mapping between NCI and FMA should establish that the FMA term Cardiac Muscle Tissue'' and the NCI term Myocardium'' are synonyms. Usingthis mapping, a computer program would then be able, for example, to migrate the datastatement Paul Williams has suffered from an infarction affecting the Myocardium'' from an NCI-based application to an FMA-based application.Creating such mappings manually is often unfeasible due to the size and complexity of modern ontologies.Therefore, the problem of automatically generating mappings between ontologies (often referred to as the ontology matching, ontology alignment, or ontology mappingproblem) has been investigated extensively in recent years.Despite the already mature state of the art, bio-medical ontologies still poseserious challenges to existing techniques.Our ultimate goal in this project is to meet these challenges and lay thefoundations for the development of new generation bio-medical informationsystems.Our main research hypothesis is based on the observation that existing techniques for ontology mapping oftendisregard the logic-based semantics of the input ontologies. As a result, they fail to take advantage ofthe available semantics, and of the highly effective reasoning services for modernontology languages. We are proposing to rethink the foundations underlying the current state-of-the art in the field by incorporating logical reasoning in each of the steps of the ontology mapping process. We also intend to go even further and make our techniquespractical and ready to be used in applications.The research is based on our preliminary empirical evidence which suggests the potential benefitsof logic-based reasoning when analysing existing mappings between real-world ontologies.We expect that our results will be directly relevant to the users of ontology-based systems inthe bio-medical domain, where knowledge and data integration is a matter of major concern.