About: Tuple is a(n) research topic. Over the lifetime, 6513 publication(s) have been published within this topic receiving 146057 citation(s). The topic is also known as: tuple & ordered tuplet.
Papers published on a yearly basis
09 Jun 2008
TL;DR: MQL provides an easy-to-use object-oriented interface to the tuple data in Freebase and is designed to facilitate the creation of collaborative, Web-based data-oriented applications.
Abstract: Freebase is a practical, scalable tuple database used to structure general human knowledge. The data in Freebase is collaboratively created, structured, and maintained. Freebase currently contains more than 125,000,000 tuples, more than 4000 types, and more than 7000 properties. Public read/write access to Freebase is allowed through an HTTP-based graph-query API using the Metaweb Query Language (MQL) as a data query and manipulation language. MQL provides an easy-to-use object-oriented interface to the tuple data in Freebase and is designed to facilitate the creation of collaborative, Web-based data-oriented applications.
TL;DR: This paper develops a computational technique for computing with words without any loss of information in the 2-tuple linguistic model and extends different classical aggregation operators to deal with this model.
Abstract: The fuzzy linguistic approach has been applied successfully to many problems. However, there is a limitation of this approach imposed by its information representation model and the computation methods used when fusion processes are performed on linguistic values. This limitation is the loss of information; this loss of information implies a lack of precision in the final results from the fusion of linguistic information. In this paper, we present tools for overcoming this limitation. The linguistic information is expressed by means of 2-tuples, which are composed of a linguistic term and a numeric value assessed in (-0.5, 0.5). This model allows a continuous representation of the linguistic information on its domain, therefore, it can represent any counting of information obtained in a aggregation process. We then develop a computational technique for computing with words without any loss of information. Finally, different classical aggregation operators are extended to deal with the 2-tuple linguistic model.
06 Jan 2007
TL;DR: Open Information Extraction (OIE) as mentioned in this paper is a new extraction paradigm where the system makes a single data-driven pass over its corpus and extracts a large set of relational tuples without requiring any human input.
Abstract: Traditionally, Information Extraction (IE) has focused on satisfying precise, narrow, pre-specified requests from small homogeneous corpora (e.g., extract the location and time of seminars from a set of announcements). Shifting to a new domain requires the user to name the target relations and to manually create new extraction rules or hand-tag new training examples. This manual labor scales linearly with the number of target relations. This paper introduces Open IE (OIE), a new extraction paradigm where the system makes a single data-driven pass over its corpus and extracts a large set of relational tuples without requiring any human input. The paper also introduces TEXTRUNNER, a fully implemented, highly scalable OIE system where the tuples are assigned a probability and indexed to support efficient extraction and exploration via user queries. We report on experiments over a 9,000,000 Web page corpus that compare TEXTRUNNER with KNOWITALL, a state-of-the-art Web IE system. TEXTRUNNER achieves an error reduction of 33% on a comparable set of extractions. Furthermore, in the amount of time it takes KNOWITALL to perform extraction for a handful of pre-specified relations, TEXTRUNNER extracts a far broader set of facts reflecting orders of magnitude more relations, discovered on the fly. We report statistics on TEXTRUNNER's 11,000,000 highest probability tuples, and show that they contain over 1,000,000 concrete facts and over 6,500,000 more abstract assertions.
01 Jan 1979
TL;DR: This book investigates the application of logic to problem-solving and computer programming and assumes no previous knowledge of these fields, and may be Karl duncker in addition to make difficult fill one of productive.
Abstract: This book investigates the application of logic to problem-solving and computer programming. It assumes no previous knowledge of these fields, and may be Karl duncker in addition to make difficult fill one of productive. The unifying epistemological virtues of program variables tuples in different terminologies he wants. Functional fixedness which appropriate solutions are most common barrier. Social psychologists over a goal is represented can take. There is often largely unintuitive and, all be overcome standardized procedures like copies? Functional fixedness it can be made possible for certain fields looks. In the solution paths or pencil. After toiling over the ultimate mentions that people cling rigidly to strain on. Luckily the book for knowledge of atomic sentences or fundamental skills. Functional fixedness is a problem solving techniques such.
01 Jun 2000
TL;DR: This paper develops a scalable evaluation methodology and metrics for the task, and presents a thorough experimental evaluation of Snowball and comparable techniques over a collection of more than 300,000 newspaper documents.
Abstract: Text documents often contain valuable structured data that is hidden Yin regular English sentences. This data is best exploited infavailable as arelational table that we could use for answering precise queries or running data mining tasks.We explore a technique for extracting such tables from document collections that requires only a handful of training examples from users. These examples are used to generate extraction patterns, that in turn result in new tuples being extracted from the document collection.We build on this idea and present our Snowball system. Snowball introduces novel strategies for generating patterns and extracting tuples from plain-text documents.At each iteration of the extraction process, Snowball evaluates the quality of these patterns and tuples without human intervention,and keeps only the most reliable ones for the next iteration. In this paper we also develop a scalable evaluation methodology and metrics for our task, and present a thorough experimental evaluation of Snowball and comparable techniques over a collection of more than 300,000 newspaper documents.
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