LogMap: Logic-based Methods for Ontology Mapping

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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.