用于对齐知识和文本集合的开放和封闭模式

Matthew Kelcey
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引用次数: 1

摘要

说到知识库,大多数人首先想到的是结构化资源,如Freebase/Wikidata,以及它们与类似结构化网络资源(如Wikipedia)的关系。然而,许多额外的和有趣的“知识”是在使用开放信息提取技术以较少监督的方式构建的非结构化数据库中捕获的。在这次演讲中,我们将讨论开放/封闭模式知识库之间的一些差异,包括客观内容与主观内容以及新鲜度和信任度的概念。我们将概述对齐这些数据源的方法,以便将它们的相对优势结合起来,并完成此类对齐的应用程序;特别是围绕开放式问答系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Open and Closed Schema for Aligning Knowledge and Text Collections
When it comes to knowledge bases most people's first thought are structured sources such as Freebase/Wikidata and their relationship to similarly structured web sources such as Wikipedia. A lot of additional and interesting "knowledge" though is captured in unstructured databases constructed in a less supervised manner using open information extraction techniques. In this talk we'll discuss some of the differences between open/closed schema knowledge bases including the ideas of objective vs subjective content as well as freshness and trust. We'll give an overview on approaches to aligning such data sources in a way that their relative strengths can be combined and finish with applications of such alignments; particularly around open question and answer systems.
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