Authors: Elena Simperl
Tags: 2009, conceptual modeling
Technologies for the efficient and effective reuse of ontological knowledge are one of the key success factors for the Semantic Web. Putting aside matters of cost or quality, being reusable is an intrinsic property of ontologies, originally conceived of as a means to enable and enhance the interoperability between computing applications. This article gives an account, based on empirical evidence and real-world findings, of the methodologies, methods and tools currently used to perform ontology-reuse processes. We study the most prominent case studies on ontology reuse, published in the knowledge-/ontology-engineering literature from the early nineties. This overview is complemented by two self-conducted case studies in the areas of eHealth and eRecruitment in which we developed Semantic Web ontologies for different scopes and purposes by resorting to existing ontological knowledge on the Web. Based on the analysis of the case studies, we are able to identify a series of research and development challenges which should be addressed to ensure reuse becomes a feasible alternative to other ontology-engineering strategies such as development from scratch. In particular, we emphasize the need for a context- and tasksensitive treatment of ontologies, both from an engineering and a usage perspective, and identify the typical phases of reuse processes which could profit considerably from such an approach. Further on, we argue for the need for ontology-reuse methodologies which optimally exploit human and computational intelligence to effectively operationalize reuse processes
Read the full paper here: https://pdf.sciencedirectassets.com/271546/1-s2.0-S0169023X09X00091/1-s2.0-S0169023X0900007X/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEC8aCXVzLWVhc3QtMSJHMEUCIQDVrdcHIjM%2BwVJL6r5bqw%2FKw%2BWqEKkfuw%2F6CJWPa%2BqdrwIgGkrmMgjXFrpmduh8mXqMjOFJW6baV3qk3jruOdsB9%2FAqtAMINxADGgwwNTkwMDM1NDY4NjUiDLf15R%2FdvKsv%2FajcQyqRA3FbN2aG%2FN4G%2B2fSlAaDMNbtgjmDXAmNZ6K%2FUV%2B%2B0RiiPUbqukG5bxUx30Zzt9TkXE51q2oGvZHfGBWnNwhe3jDLVdk7gMuJeR8EcdVDksL7O%2BQoCUfyqiOqOJAoH6cEOf1ewe9Z6Q0nXLLLG5DI%2BGsbzmxtlX%2Fch4sn9hnhIOPIpLeWBMcwbLBqqtYx79%2BfyQ3tF5RGmQ9UoT7DfftdwBksZmFcpj5LxlfeZ%2Bbgacm4ICRxsLLYmuwTBaF4RguRf2NnvCRoev09NlfC13vXXQu%2Fm0ZJFZzUQli2j6huuKRjJjy6S2TF8iaFyVL6X%2BLuxnVc9yq%2BfRl8lEjyTCaYnN7DIuIFtFxi%2BAj9sESXFs8OAVh236TDOu18fDzmZlPbviKohKJ4oifTwwE%2BAQL02Zo85TA1zDpttl51hvvSOZvsJQFwbqzWVk4fx%2BYPr%2Bkc8NoaMo1LixJ58Id%2FBxSYnvTz6gidE%2Fdy6Nq0tnAIYdOQ3Omq4HFcwHEx6ZUEP2jNE75%2FSbl2NZRyB1w0117Q1nPcMPOA4PoFOusBV3dqacQFpeq%2BLdPD6Zpn78y055HFWZ4qzmCMsEnH3id%2FPFZs9yOcUxzKQqhm9nW8pP7Kmy0qHXGU24WN3a5KRSwQctivXQt5WilHB5UUvI%2FRtuOv4sbNIPKJNC6VtqJdH7%2FQPfwgVi8Ym8ODNRL1rUubgSuOhe8adfmu8%2Fa5o6I86kB%2B8FsS32KNGzHX8pIjH0Lcuk93N8YHSSg5vezbjr%2BlyvUEKzryXA5jTQGpRn8f2Zj9Qle9Q4j8HVeYXA1Fj2ih7IqsjdJPQ5zy06%2FJv3%2BvHjXW22oCClQ%2F8tsSQi%2Byt1iqlkgy%2BznPEg%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20200908T234859Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTY2XBJVBDL%2F20200908%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=580177125bd7e78d8177e286f1760c387614c5156174a6b7bf8d737659c30ee8&hash=c54e1b40b7cb81b5a1d30a824c49e01357c22c3680a6b9c633c388c776e1938e&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0169023X0900007X&tid=spdf-264609bd-8b7d-4b9d-94cb-accff645e694&sid=006e6d4c3109c14eb9383ad2b0357d9edc3agxrqa&type=client
