Joint Information Extraction from the Web Using Linked Data
Joint Information Extraction from the Web Using Linked Data
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DOI：
10.1007/978-3-319-11915-1_32
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发表时间：
2014-10
期刊：
影响因子：
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通讯作者：
Isabelle Augenstein
中科院分区：
文献类型：
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作者：
Isabelle Augenstein
几乎所有知名网络公司目前都在致力于构建“知识图”，这些公司在改进搜索、电子邮件、日历等方面显示出显着的成果。即使是最大的开放访问公司，例如 Freebase 和 Wikidata，也远未完成，部分原因是新信息出现得如此之快。大多数缺失的信息都可以在网页上找到。为了访问这些知识并填充知识库，需要信息提取方法。信息提取系统的瓶颈是获取训练数据来学习分类器。在这项博士研究中，我们研究了如何使用知识库中的现有数据自动注释训练数据以学习分类器，进而提取更多数据来扩展知识库。我们讨论我们的假设、方法、评估方法并提出初步结果。
Almost all of the big name Web companies are currently engaged in building ‘knowledge graphs’ and these are showing significant results in improving search, email, calendaring, etc. Even the largest openly-accessible ones, such as Freebase and Wikidata, are far from complete, partly because new information is emerging so quickly. Most of the missing information is available on Web pages. To access that knowledge and populate knowledge bases, information extraction methods are necessitated. The bottleneck for information extraction systems is obtaining training data to learn classifiers. In this doctoral research, we investigate how existing data in knowledge bases can be used to automatically annotate training data to learn classifiers to in turn extract more data to expand knowledge bases. We discuss our hypotheses, approach, evaluation methods and present preliminary results.