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，生物医学本体可以包含诸如"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13岁以下儿童的关节炎“以及”多关节青少年关节炎是一种既多关节又青少年的关节炎“的信息。本体广泛地用于生物学和医学中。生物医学本体的一个突出例子是SNOMED CT，它是NHS患者记录服务的核心组件。其他示例包括解剖学基础模型（FMA）和国家癌症研究所词库（NCI）。诸如SNOMED CT、FMA和NCI的本体正逐渐取代现有的医学分类，并且正成为用于访问、收集和共享医学知识和数据的核心平台。医疗应用中的电子病历）：如果JohnSmith的医疗记录表明他是一个10岁的患有关节炎的病人，并且他的膝关节、踝关节、腕关节、肘关节和髋关节都有损伤，那么可以使用本体来推断他患有一种多关节青少年关节炎。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关系（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“是同义词。使用该映射，计算机程序然后将能够例如将数据语句"Paul威廉姆斯患有影响心肌的梗塞“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，手动创建这样的映射通常是不可行的。（通常被称为本体匹配、本体对齐或本体映射问题）近年来已被广泛研究。尽管现有技术已经成熟，生物医学本体对现有技术仍然存在严重的挑战。我们在这个项目中的最终目标是迎接这些挑战，为医学本体的发展奠定基础。新一代生物医学信息系统。我们的主要研究假设是基于现有的本体映射技术经常忽视输入本体的基于逻辑的语义的观察。因此，他们未能利用现有的语义，以及现代非本体语言的高效推理服务。我们建议重新思考的基础，目前的国家的最先进的领域，将逻辑推理的本体映射过程中的每一个步骤。我们还打算走得更远，使我们的技术实用，并准备在应用中使用。这项研究是基于我们的初步经验证据表明，基于逻辑推理的潜在好处时，分析现实世界的本体之间的现有映射。我们希望我们的结果将直接相关的用户基于本体的系统在生物医学领域，其中知识和数据集成是主要关注的问题。

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.