LogMap: Logic-based Methods for Ontology Mapping

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.

We expect that our results will be directly relevant to the users of ontology-based systems, especially in industry and government organisations, where knowledge and data integration is a major concern. These include the NHS, Ordnance Survey, Siemens, IBM, Software AG, Alcatel-Lucent, and Oracle, all of which are already using ontologies. Our results will be especially relevant to the bio-medical community. For example, we intend to develop technologies that could revolutionise the way in which UMLS-Meta and similar data and knowledge integration resources are designed and maintained. Given the importance of UMLS-Meta in applications in bio-informatics (e.g., PubMed), we expect those who use it in their applications will directly benefit from our results. In the long term, beneficiaries could include anyone who uses or depends on an ontology-based information system, from the medical doctor who uses the NHS patient record service to the researcher who uses PubMed to access scientific knowledge. Concerning dissemination and engagement, we will disseminate the results of this research mainly through the following channels: - publications in leading conferences and journals in the fields of artificial intelligence, Semantic Web, and bio-informatics; - contacts with users from the bio-medical community through organisations such as the OBO consortium and the World Wide Web Consortium (W3C) Health Care and Life Sciences Special Interest Group; - participation in relevant coordination and standardisation efforts within groups and organisations such as the (W3C) and the OWL Experiences and Directions Group (OWLED); - distribution of software via the Web; - industry liaisons activities via the Host Institution, which includes talks, industry showcases, periodic newsletters, and an industry liaison website; and - industry liaisons via our proposed collaboration with the European Bio-informatics Institute (EBI). Concerning exploitation, the commercialisation of IP resulting from the project will be managed by Isis Innovation, a subsidiary of the University of Oxford, founded to exploit the outcomes of Oxford's research activities. The Host Institution is very proactive in communicating funding opportunities to its academics for exploiting research output via Isis and therefore extensive departmental support and assistance during a potential exploitation process is to be expected. Possible ways of commercially exploiting our expected results through Isis include: - licensing innovative software; - creating a new start-up company; and - developing new specialised application-software in areas such as bio-medicine. Finally, the PI and named RA have already participated in several research projects, many of which involved close interaction and collaboration with users and industrial partners. The PI has also extensive experience in standardisation efforts as well as in the organisation of workshops and other events which have gathered a significant number of users and practitioners from both academia and industry.