III-Medium: Reading the Web: Utilizing Markov Logic in Open Information Extraction

III-中：阅读网络：在开放信息提取中利用马尔可夫逻辑

• 批准号: 0803481
• 负责人: Oren Etzioni
• 金额: $ 89.97万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0803481&HistoricalAwards=false
• 关键词: III; Medium; Reading; Web; Utilizing

This project adresses the challenge of automatically extracting high-quality knowledge bases from text corpora. Previous work, led by Prof. Etzioni, developed KnowItAll (http://www.cs.washington.edu/research/knowitall), an unsupervised, domain-independent, scalable system that learns from the Web in an open-ended fashion. Another project, led by Prof. Domingos, has formalized and fully implemented a powerful framework called Markov Logic Networks (MLNs) (see http://www.cs.washington.edu/ai/srl.html) that enable inference and learning in large, first-order models. This project integrates KnowItAll and MLNs to build large-scale ontologies from text corpora: extracting relational tuples, using joint inference to merge and validate the tuples, mapping extracted phrases to a taxonomy, and using probabilistic inference rules to answer queries about the ontology.Consider, for example, the query "how many Nobel Laureates where born in Europe?" In response, Google merely provides documents matching the keywords in the query. KnowItAll can only identify people who are explicitly identified as Nobel Laureates and Europeans. This project investigates a system that utilizes both information extraction and probabilistic reasoning to identify candidate answers, not explicitly stated in the text, and their likelihood of being correct. As a simple example, the system concludes that Einstein was born in Europe based on the sentence "Einstein was born in Ulm, Germany". The query "what foods help prevent osteoporosis?" is answered using a multi-step reasoning chain regarding the ingredients of the food and their ability to prevent the disease.The broader impact of this research includes novel methods of building knowledge bases automatically. Such knowledge bases (after some manual tuning, perhaps) could be used to support a wide range of applications from question-answering systems, to knowledge-based systems for medical applications, to background knowledge in support of machine reading of text. The knowledge bases created by this project will be made freely available to the research community as a Web-site and also as a Web-based API via the project Web site (http://www.cs.washington.edu/research/knowitall/ReadingTheWeb/).

这个项目解决了从文本语料库中自动提取高质量知识库的挑战。由埃齐奥尼教授领导的之前的工作开发了Knowwith All(http://www.cs.washington.edu/research/knowitall)，，这是一个无监督、独立于领域、可扩展的系统，它以一种开放的方式从网络中学习。由多明戈斯教授领导的另一个项目已经形式化并完全实现了一个名为马尔可夫逻辑网络(MLN)的强大框架(见http://www.cs.washington.edu/ai/srl.html))，该框架能够在大型一阶模型中进行推理和学习。这个项目集成了Knowwitall和MLN来从文本语料库中构建大规模的本体：提取关系元组，使用联合推理来合并和验证元组，将提取的短语映射到分类，并使用概率推理规则来回答关于本体的查询。作为回应，谷歌只提供与查询中的关键字匹配的文档。Knowwitall只能识别那些明确被确认为诺贝尔奖获得者和欧洲人的人。这个项目调查了一个系统，它利用信息提取和概率推理来识别文本中没有明确说明的候选答案，以及它们正确的可能性。作为一个简单的例子，该系统根据一句话得出结论：爱因斯坦出生在欧洲，德国乌尔姆。询问“哪些食物有助于预防骨质疏松症？”这项研究的更广泛的影响包括自动建立知识库的新方法。这种知识库(也许经过一些人工调整之后)可用于支持广泛的应用，从问答系统到医疗应用的基于知识的系统，再到支持文本机器阅读的背景知识。本项目创建的知识库将作为网站免费提供给研究界，也将通过项目网站(http://www.cs.washington.edu/research/knowitall/ReadingTheWeb/).作为基于网络的API免费提供