用于数据驱动的本体填充和维护方法的集成语言

LDV Forum Pub Date : 2007-07-01 DOI:10.21248/jlcl.22.2007.94
Eduardo Torres Schumann, Uwe Mönnich, K. Schulz
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引用次数: 0

摘要

用大量数据填充本体和通过人工监督确保集成过程的质量似乎是相互排斥的目标,然而在构建实际应用程序时却作为需求出现。在我们的案例中,我们面临着填充EFGT网络的实际问题,这是一个大规模的本体,可以在不同的NLP应用程序中使用已经存在的和部分非常大的数据源进行主题推理,但前提是不危及资源的质量。我们在这里提出了我们对这个问题的特殊解决方案,它在一个工具中,一方面结合了能够从结构化数据中为本体生成新条目的集成语言,另一方面结合了具有在线本体编辑功能的冲突生成条目的可视化。这种方法似乎能够以一种互动的方式对人口过程进行有效的人类监督,并且对维护任务也很有用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integration Languages for Data-Driven Approaches to Ontology Population and Maintenance
Populating an ontology with a vast amount of data and ensuring the quality of the integration process by means of human supervision seem to be mutually exclusive goals that nevertheless arise as requirements when building practical applications. In our case, we were confronted with the practical problem of populating the EFGT Net, a large-scale ontology that enables thematic reasoning in dierent NLP applications, out of already existing and partly very large data sources, but on condition of not putting the quality of the resource at risk. We present here our particular solution to this problem, which combines, in a single tool, on one hand an integration language capable of generating new entries for the ontology out of structured data with, on the other hand, a visualization of conflicting generated entries with online ontology editing facilities. This approach appears to enable ecient human supervision of the population process in an interactive way and to be also useful for maintenance tasks.
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