The KNOWITALL system aims to automate the tedious process of extracting large collections of facts (e.g., names of scientists or politicians) from the Web in an unsupervised, domain-independent, and scalable manner. The paper presents an overview of KNOW-ITALL’s novel architecture and design principles, emphasizing its distinctive ability to extract information without any hand-labeled training examples. In its first major run, KNOW-ITALL extracted over 50,000 facts, but suggested a challenge: How can we improve KNOW-ITALL’s recall and extraction rate without sacrificing precision? This paper presents three distinct ways to address this challenge and evaluates their performance. Pattern Learning learns domain-specific extraction rules, which enable additional extractions. Subclass Extraction automatically identifies sub-classes in order to boost recall. List Extraction locates lists of class instances, learns a “wrapper ” for each list, and extracts elements of each list. Since each method bootstraps from KNOWITALL’s domainindependent methods, the methods also obviate hand-labeled training examples. The paper reports on experiments, focused on named-entity extraction, that measure the relative efficacy of each method and demonstrate their synergy. In concert, our methods gave KNOW-ITALL a 4-fold to 8-fold increase in recall, while maintaining high precision, and discovered over 10,000 cities missing from the Tipster Gazetteer.

The World-Wide Web consists of a huge number of unstructured documents, but it also contains structured data in the form of HTML tables. We extracted 14.1 billion HTML tables from Google’s general-purpose web crawl, and used statistical classification techniques to find the estimated 154M that contain high-quality relational data. Because each relational table has its own “schema ” of labeled and typed columns, each such table can be considered a small structured database. The resulting corpus of databases is larger than any other corpus we are aware of, by at least five orders of magnitude. We describe the WebTables system to explore two fundamental questions about this collection of databases. First, what are effective techniques for searching for structured data at search-engine scales? Second, what additional power

The World-Wide Web consists of a huge number of unstructured hypertext documents, but it also contains structured data in the form of HTML tables. Many of these tables contain both relational-style data and a small “schema ” of labeled and typed columns, making each such table a small structured database. The WebTables project is an effort to extract and make use of the huge number of these structured tables on the Web. A clean collection of relational-style tables could be useful for improving web search, schema design, and many other applications. This paper describes the first stage of the WebTables project. First, we give an in-depth study of the Web’s HTML table corpus. For example, we extracted 14.1 billion HTML tables from a several-billion-page portion of Google’s generalpurpose web crawl, and estimate that 154 million of these tables contain high-quality relational-style data. We also describe the crawl’s distribution of table sizes and data types. Second, we describe a system for performing relation recovery. The Web mixes relational and non-relational tables indiscriminately (often on the same page), so there is no simple way to distinguish the 1.1 % of good relations from the remainder, nor to recover column label and type information. Our mix of hand-written detectors and statistical classifiers takes a raw Web crawl as input, and generates a collection of databases that is five orders of magnitude larger than any other collection we are aware of. Relation recovery achieves precision and recall that are comparable to other domain-independent information extraction systems. 1.

There are major trends to advance the functionality of search engines to a more expressive semantic level. This is enabled by the advent of knowledge-sharing communities such as Wikipedia and the progress in automatically extracting entities and relationships from semistructured as well as natural-language Web sources. Recent endeavors of this kind include DBpedia, EntityCube, KnowItAll, ReadTheWeb, and our own YAGO-NAGA project (and others). The goal is to automatically construct and maintain a comprehensive knowledge base of facts about named entities, their semantic classes, and their mutual relations as well as temporal contexts, with high precision and high recall. This tutorial discusses state-ofthe-art methods, research opportunities, and open challenges along this avenue of knowledge harvesting.

Web Information Extraction (WIE) systems have recently been able to extract massive quantities of relational data from online text. This has opened the possibility of achieving an elusive goal in Artificial Intelligence (AI): broad-coverage domain knowledge. AI systems depend to a great extent on having knowledge about the domains in which they operate, and such knowledge is typically expensive to enter into the system. Furthermore, the knowledge must be entered for every different domain in which an application is to operate. The Web contains knowledge about all kinds of different domains, but in a format that is not readily usable by AI systems. WIE promises to bridge the gap between the Web and AI. Natural Language Processing is an example of an area in AI in which knowledge can make a dramatic difference in the performance of an application. Understanding or interpreting language depends on the ability to understand the words used in a domain. The meanings, usages, and syntactic properties of words, and the relative frequency with which certain words are used, are necessary pieces of information for effective language processing, and much of this information can be extracted from text. In one case study, this thesis examines methods for using extracted information in improving a particular kind of language processing tool, a parser. Before information extraction can become broadly useful, however, more research must be done to improve the quality of the extracted information. A number of factors affect the quality, including correctness, importance or relevance, and the sophistication of meaning representation. The second case study in this thesis investigates a method for resolving synonyms in extracted information. This technique changes the meaning representation of extractions from one that relates words or names to one that relates entities to one another.

A long-standing goal of Web research has been to construct a unified Web knowledge base. Information extraction techniques have shown good results on Web inputs, but even most domain-independent ones are not appropriate for Web-scale operation. In this paper we describe three recent extraction systems that can be operated on the entire Web (two of which come from Google Research). The TextRunner system focuses on raw natural language text, the WebTables system focuses on HTML-embedded tables, and the deep-web surfacing system focuses on “hidden ” databases. The domain, expressiveness, and accuracy of extracted data can depend strongly on its source extractor; we describe differences in the characteristics of data produced by the three extractors. Finally, we discuss a series of unique data applications (some of which have already been prototyped) that are enabled by aggregating extracted Web information. 1.

Over the past few years, we have been trying to build an end-to-end system at Wisconsin to manage unstructured data, using extraction, integration, and user interaction. This paper describes the key information extraction (IE) challenges that we have run into, and sketches our solutions. We discuss in particular developing a declarative IE language, optimizing for this language, generating IE provenance, incorporating user feedback into the IE process, developing a novel wikibased user interface for feedback, best-effort IE, pushing IE into RDBMSs, and more. Our work suggests that IE in managing unstructured data can open up many interesting research challenges, and that these challenges can greatly benefit from the wealth of work on managing structured data that has been carried out by the database community. 1.

Sanitization of a document involves removing sensitive information from the document, so that it may be distributed to a broader audience. Such sanitization is needed while declassifying documents involving sensitive or confidential information such as corporate emails, intelligence reports, medical records, etc. In this paper, we present the ERASE framework for performing document sanitization in an automated manner. ERASE can be used to sanitize a document dynamically, so that different users get different views of the same document based on what they are authorized to know. We formalize the problem and present algorithms used in ERASE for finding the appropriate terms to remove from the document. Our preliminary experimental study demonstrates the efficiency and efficacy of the proposed algorithms. disclosure of proprietary information while sharing data with outsourced operations. Example. Figure 1 shows an example U.S. government document that has been sanitized prior to release [16]. This sanitized document gives limited information (such as the purpose and the funding amount) on an erstwhile secret medical research project, while hiding the names of the funding sources, principal investigators and their affiliation.

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Information Extraction Improving the ability of computer systems to process text is a significant research challenge. Many applications are based on partially structured databases, where structured data conforming to a schema is combined with free text. Information is stored as text in these applications because the queries required are not all known in advance – allowing for text is an attempt to capture information that could be relevant in the future but cannot be anticipated when the database schema is being designed. Text is also used due to the limitations of conventional databases, where the schema cannot easily be extended as new entity types and relationships arise in the future. Information Extraction (IE) is the process of finding instances of pre-defined entity types within text, while Data Integration systems build a virtual global schema from available structured data sources. We argue that combining techniques from IE and data integration is a promising approach for supporting applications that access partially structured data: the virtual global schema and associated metadata can be used to partially configure an IE process, and the information extracted by the IE process can then be integrated into the virtual global database, supporting queries which could not otherwise be answered. In this thesis we describe the design and implementation of the Experimental System To Extract Structure from Text (ESTEST) that investigates this approach. We 3 give examples of its use and experimental results from a number of application domains.