Novácek Vít, Laera Loredana, Handschuh Siegfried, Davis Brian
Digital Enterprise Research Institute, National University of Ireland, Galway, IDA Business Park, Lower Dangan, Galway, Co. Galway, Ireland.
J Biomed Inform. 2008 Oct;41(5):816-28. doi: 10.1016/j.jbi.2008.06.003. Epub 2008 Jul 24.
We present a novel ontology integration technique that explicitly takes the dynamics and data-intensiveness of e-health and biomedicine application domains into account. Changing and growing knowledge, possibly contained in unstructured natural language resources, is handled by application of cutting-edge Semantic Web technologies. In particular, semi-automatic integration of ontology learning results into a manually developed ontology is employed. This integration bases on automatic negotiation of agreed alignments, inconsistency resolution and natural language generation methods. Their novel combination alleviates the end-user effort in the incorporation of new knowledge to large extent. This allows for efficient application in many practical use cases, as we show in the paper.
我们提出了一种新颖的本体集成技术，该技术明确考虑了电子健康和生物医学应用领域的动态性和数据密集性。可能包含在非结构化自然语言资源中的不断变化和增长的知识，通过应用前沿的语义网技术来处理。特别是，采用了将本体学习结果半自动集成到手动开发的本体中的方法。这种集成基于对商定对齐的自动协商、不一致性解决和自然语言生成方法。它们的新颖组合在很大程度上减轻了最终用户纳入新知识的工作量。正如我们在本文中所展示的，这使得该技术能够在许多实际用例中高效应用。
