首页> 外文会议>International conference on knowledge engineering and knowledge management >Learning with Partial Data for Semantic Table Interpretation
【24h】

Learning with Partial Data for Semantic Table Interpretation

机译:学习部分数据以进行语义表解释

获取原文

摘要

This work studies methods of annotating Web tables for semantic indexing and search - labeling table columns with semantic type information and linking content cells with named entities. Built on a state-of-the-art method, the focus is placed on developing and evaluating methods able to achieve the goals with partial content sampled from the table as opposed to using the entire table content as typical state-of-the-art methods would otherwise do. The method starts by annotating table columns using a sample automatically selected based on the data in the table, then using the type information to guide content cell disambiguation. Different methods of sample selection are introduced, and experiments show that they contribute to higher accuracy in cell disambiguation, comparable accuracy in column type annotation but with reduced computational overhead.
机译:这项工作研究为语义索引和搜索注释Web表的方法-使用语义类型信息标记表列以及将内容单元格与命名实体链接。建立在最新方法的基础上,重点放在开发和评估能够通过从表中抽取部分内容来实现目标的方法,而不是将整个表内容用作典型的最新技术方法否则会做。该方法首先使用基于表中数据自动选择的样本为表列添加注释,然后使用类型信息指导内容单元消除歧义。介绍了不同的样本选择方法,实验表明它们有助于提高单元格消除歧义的准确性,在列类型注释中具有可比的准确性,但减少了计算开销。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号