CAREER: Web Information Extraction: Integration and Scaling

职业：Web 信息提取：集成和扩展

• 批准号: 1351029
• 负责人: Douglas Downey
• 金额: $ 55万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1351029&HistoricalAwards=false
• 关键词: CAREER; Web; Information; Extraction; Integration

This project studies Web Information Extraction (WIE), the task of automatically extracting computer-understandable knowledge bases (KBs) from the World Wide Web. The project addresses two key challenges in WIE. First, many different teams in academia and industry are pursuing WIE, but they lack methods for combining their KBs into a more powerful whole. This project explores how to integrate knowledge automatically across WIE systems and approaches. Secondly, a long-standing goal for WIE is to construct systems that can scale to billions of facts, by continually improving themselves over time. This project is investigating new methods that continually optimize a WIE system with limited human intervention. The project's goal of scaling and integrating WIE systems promises to address needs in the research community, the computing industry, and the public. Methods that allow different WIE systems to seamlessly exchange knowledge could dramatically hasten the progress of Web extraction efforts currently underway in academia and industry. For the public, advances in Web extraction promise to enable improved search engines that can assist users with tasks and answer complex questions. Further, through application prototypes, the project will provide public-facing information retrieval tools that promise to help users retrieve, understand, and analyze the Web's knowledge more rapidly. The project's research is also integrated with an education plan that includes outreach to underrepresented groups.The technical solutions pursued in the project utilize probability distributions over natural language. For the integration challenge, the project is developing new Application Programming Interfaces (APIs) that leverage the expressiveness of natural language to automatically integrate current and future WIE systems, even when the systems extract from different types of corpora and represent knowledge in different ways. For the scaling challenge, the project is developing ways to continually optimize new Statistical Language Models (SLMs) over text on the Web. The project investigates the SLM approach for WIE theoretically, asking what types of knowledge different SLMs can encode, and how much text is required to obtain the knowledge. Further, the project introduces new SLM capabilities, including methods for scaling to larger corpora and more semantic classes, and novel models that incorporate collocations, quantitative attributes, sense disambiguation, and actively-selected human input. The project web site (http://websail.eecs.northwestern.edu/wie/) provides additional information and access to results, including software, corpora, and evaluation data sets.

本项目研究Web信息抽取(WIE)，即从万维网中自动抽取计算机可理解的知识库(KBS)的任务。该项目解决了WIE面临的两个关键挑战。首先，学术界和工业界的许多不同团队都在追求WIE，但他们缺乏将他们的KBS组合成一个更强大的整体的方法。该项目探索如何跨WIE系统和方法自动集成知识。其次，WIE的一个长期目标是通过随着时间的推移不断改进自己，构建能够扩展到数十亿事实的系统。该项目正在研究在有限人工干预的情况下持续优化WIE系统的新方法。该项目的目标是扩展和集成WIE系统，承诺满足研究社区、计算行业和公众的需求。允许不同WIE系统无缝交换知识的方法可以极大地加快目前学术界和工业界正在进行的Web提取工作的进展。对于公众来说，网络提取的进步有望实现更好的搜索引擎，帮助用户完成任务并回答复杂的问题。此外，通过应用程序原型，该项目将提供面向公众的信息检索工具，承诺帮助用户更快地检索、理解和分析Web知识。该项目的研究还与一项教育计划相结合，该计划包括对代表人数不足的群体进行推广。该项目所追求的技术解决方案利用自然语言的概率分布。对于集成挑战，该项目正在开发新的应用程序编程接口(API)，该接口利用自然语言的表达能力来自动集成当前和未来的WIE系统，即使这些系统从不同类型的语料库中提取并以不同的方式表示知识。对于可伸缩性的挑战，该项目正在开发方法，以不断优化新的统计语言模型(SLM)，而不是Web上的文本。该项目从理论上研究了WIE的SLM方法，询问不同的SLM可以编码哪些类型的知识，以及需要多少文本才能获得这些知识。此外，该项目引入了新的SLM能力，包括扩展到更大语料库和更多语义类别的方法，以及纳入搭配、量化属性、意义消除歧义和主动选择的人类输入的新模型。项目网站(http://websail.eecs.northwestern.edu/wie/))提供更多信息，并提供对结果的访问，包括软件、语料库和评价数据集。