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

该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。 Etzioni教授领导的先前工作开发了KnowItAll（http：//www.cs.washington.edu/research/knowitall），这是一个无监督的，独立于领域的，可扩展的系统，以开放的方式从Web学习。 另一个由Domingos教授领导的项目正式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。 该项目集成了KnowItAll和MLN，从文本语料库中构建大规模本体：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，Google只提供与查询中的关键字匹配的文档。 KnowItAll只能识别那些被明确标识为诺贝尔奖获得者和欧洲人的人。 该项目研究了一个系统，该系统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及其正确的可能性。 作为一个简单的例子，系统基于句子“Einstein was born in乌尔姆，德国”得出爱因斯坦出生在欧洲的结论。询问“哪些食物有助于预防骨质疏松症？“是使用关于食物成分及其预防疾病能力的多步推理链来回答的。这项研究的更广泛影响包括自动构建知识库的新方法。这样的知识库（也许在一些手动调整之后）可以用于支持从问答系统到用于医学应用的基于知识的系统，再到支持文本的机器阅读的背景知识的广泛应用。 这个项目所建立的知识库将作为一个网站免费提供给研究界，并通过项目网站（http：//www.cs.washington.edu/research/knowitall/ReadingTheWeb/）作为一个基于网络的API提供给研究界。

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/).