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Harvesting Relational and Structured Knowledge for Ontology Building in the Wpro Architecture

机译:收获WPRO架构中的本体建设的关系和结构化知识

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We present two algorithms for supporting semi-automatic ontology building, integrated in Wpro, a new architecture for ontology learning from Web documents. The first algorithm automatically extracts ontological entities from tables, by using specific heuristics and WordNet-based analysis. The second algorithm harvests semantic relations from unstructured texts using Natural Language Processing techniques. The integration in Wpro allows a friendly interaction with the user for validating and modifying the extracted knowledge, and for uploading it into an existing ontology. Both algorithms show promising performance in the extraction process, and offer a practical means to speed-up the overall ontology building process.
机译:我们展示了两个用于支持半自动本体建筑的算法,集成在WPRO中,是从Web文档中学习的新架构。第一算法通过使用特定启发式和基于Wordnet的分析,自动从表中提取来自表的本体实体。第二种算法利用自然语言处理技术从非结构化文本收获语义关系。在WPRO中的集成允许与用户进行友好的交互,以验证和修改提取的知识,并将其上传到现有本体中。这两种算法都在提取过程中显示了有希望的性能,并提供了加速整体本体建设过程的实用手段。

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