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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(More)
We describe the ucpop partial order planning algorithm which handles a subset of Pednault's ADL action representation. In particular, ucpop operates with actions that have conditional eeects, universally quan-tiied preconditions and eeects, and with universally quantiied goals. We prove ucpop is both sound and complete for this representation and describe a(More)
Information extraction (IE) holds the promise of generating a large-scale knowledge base from the Web's natural language text. Knowledge-based weak supervision, using structured data to heuristically label a training corpus, works towards this goal by enabling the automated learning of a potentially unbounded number of relation extractors. Recently,(More)
Many Internet information resources present relational data|telephone directories, product catalogs, etc. Because these sites are formatted for people, mechanically extracting their content is dicult. Systems using such resources typically use hand-coded wrappers, procedures to extract data from information resources. We introduce wrapper induction, a(More)
Although most people believe that planners that delay step-ordering decisions as long as possible are more ecient than those that manipulate totally ordered sequences of actions, this intuition has received little formal justi-cation or empirical validation. In this paper we do both, characterizing the types of domains that oer performance dierentiation and(More)
If an agent does not have complete information about the world-state, it must reason about alternative possible states of the world and consider whether any of its actions can reduce the uncertainty. Agents controlled by a contingent planner seek to generate a robust plan, that accounts for and handles all eventualities, in advance of execution. Thus a(More)
Information-extraction (IE) systems seek to distill semantic relations from natural-language text, but most systems use supervised learning of relation-specific examples and are thus limited by the availability of training data. Open IE systems such as TextRunner, on the other hand, aim to handle the unbounded number of relations found on the Web. But how(More)
Entity Recognition (ER) is a key component of relation extraction systems and many other natural-language processing applications. Unfortunately, most ER systems are restricted to produce labels from to a small set of entity classes, e.g., person, organization, location or miscellaneous. In order to intelligently understand text and extract a wide range of(More)