Robust and Scalable Knowledge Extraction from the Web

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A new mode of inquiry, problem solving, and decision making has become pervasive in our society, consisting of applying computational, mathematical, and statistical models to infer actionable information from massive quantities of raw data. This paradigm, often called "Big Data Analytics", is driving our businesses, changing our educational systems, and holds the key to the next breakthroughs in science and engineering. While the hopes for this paradigm are understandably high, many challenges remain that prevent this promise to become a reality. The vast majority of information in most organizations, both public and private, comes in textual form (email, memos, reports, news articles, legislation, etc), remaining out of reach of current analytics tools which deal with structured data. As for publicly available information, which are increasingly more relevant for analytics, the situation is also problematic. Despite the existence of suitable standards for encoding and sharing knowledge online, the vast majority of content on the Web is still made available using only presentation markup (e.g. HTML). Coupled with the ever increasing volume of content (both internal to organizations and online), the need for effective and efficient tools for extracting actionable information from such sources has never been so critical. Applications of these techniques are starting to emerge, such as Google's knowledge graph which is being offered to complement the results of regular Web searches. The goal of this research is to advance the state-of-the-art in information extraction from non-traditional information resources, including both primarily textual sources and semistructured sources (online knowledge bases and databases). Our goals are: (1) to infer latent structure from these sources; (2) to find linkages within and across sources; (3) to enable data analytics over these sources as a result. We will start by leveraging our work on unsupervised extraction of relations from documents and on the systematic and large-scale evaluation of information extraction systems, which have uncovered a long list of opportunities for improvement, and then explore deeper models in AI and NLP for the purpose of extracting and linking data.