18,717 research outputs found

    Ontology construction from online ontologies

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    One of the main hurdles towards a wide endorsement of ontologies is the high cost of constructing them. Reuse of existing ontologies offers a much cheaper alternative than building new ones from scratch, yet tools to support such reuse are still in their infancy. However, more ontologies are becoming available on the web, and online libraries for storing and indexing ontologies are increasing in number and demand. Search engines have also started to appear, to facilitate search and retrieval of online ontologies. This paper presents a fresh view on constructing ontologies automatically, by identifying, ranking, and merging fragments of online ontologies

    A NASA-wide approach toward cost-effective, high-quality software through reuse

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    NASA Langley Research Center sponsored the second Workshop on NASA Research in Software Reuse on May 5-6, 1992 at the Research Triangle Park, North Carolina. The workshop was hosted by the Research Triangle Institute. Participants came from the three NASA centers, four NASA contractor companies, two research institutes and the Air Force's Rome Laboratory. The purpose of the workshop was to exchange information on software reuse tool development, particularly with respect to tool needs, requirements, and effectiveness. The participants presented the software reuse activities and tools being developed and used by their individual centers and programs. These programs address a wide range of reuse issues. The group also developed a mission and goals for software reuse within NASA. This publication summarizes the presentations and the issues discussed during the workshop

    Building communities for the exchange of learning objects: theoretical foundations and requirements

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    In order to reduce overall costs of developing high-quality digital courses (including both the content, and the learning and teaching activities), the exchange of learning objects has been recognized as a promising solution. This article makes an inventory of the issues involved in the exchange of learning objects within a community. It explores some basic theories, models and specifications and provides a theoretical framework containing the functional and non-functional requirements to establish an exchange system in the educational field. Three levels of requirements are discussed. First, the non-functional requirements that deal with the technical conditions to make learning objects interoperable. Second, some basic use cases (activities) are identified that must be facilitated to enable the technical exchange of learning objects, e.g. searching and adapting the objects. Third, some basic use cases are identified that are required to establish the exchange of learning objects in a community, e.g. policy management, information and training. The implications of this framework are then discussed, including recommendations concerning the identification of reward systems, role changes and evaluation instruments

    Urban Agriculture and Community Food Security in the United States: Farming from the City Center To the Urban Fringe

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    Urban Agriculture and Community Food Security in the United States: Farming from the City Center To the Urban Fringe is prepared by the Urban Agriculture Committee of the Community Food Security Coalition to raise awareness of the ways that urban agriculture can respond to food insecurity. The document advocates for policies that promote small-scale urban and peri-urban farming, and thereby prepare the next generation of urban farming leaders

    Reuse of Neural Modules for General Video Game Playing

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    A general approach to knowledge transfer is introduced in which an agent controlled by a neural network adapts how it reuses existing networks as it learns in a new domain. Networks trained for a new domain can improve their performance by routing activation selectively through previously learned neural structure, regardless of how or for what it was learned. A neuroevolution implementation of this approach is presented with application to high-dimensional sequential decision-making domains. This approach is more general than previous approaches to neural transfer for reinforcement learning. It is domain-agnostic and requires no prior assumptions about the nature of task relatedness or mappings. The method is analyzed in a stochastic version of the Arcade Learning Environment, demonstrating that it improves performance in some of the more complex Atari 2600 games, and that the success of transfer can be predicted based on a high-level characterization of game dynamics.Comment: Accepted at AAAI 1
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