Search (3 results, page 1 of 1)

  • × author_ss:"Cortez, E.M."
  • × year_i:[1990 TO 2000}
  1. Cortez, E.M.: Use of metadata vocabularies in data retrieval (1999) 0.00 
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    Abstract
    Researchers have developed a prototype of a system that uses metadata to guide their user in selecting document databases of interest. A standardized vocabulary is used to index the document sets and is also used to locate databases. Once a database is located, then freeform searches are aided by the metadata vocabulary to locate specific documents. This system, the Research, Education, Economic Information System (REEIS), is being developed fot the USDA to provide a way to locate programs, projects, and research that focus on food, agriculture, natural resources and rural development
    Type
    a
  2. Cortez, E.M.; Park, S.C.; Kim, S.: ¬The hybrid application of an inductive learning method and a neural network (1995) 0.00 
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    Abstract
    Traditional information retrieval systems based on Boolean logic suffer from 2 inherent problems: (1) inaccurate or incomplete query representation, and (2) inconsistent indexing. While many researchers have demonstrated that neural networks can solve the incomplete query problems for information retrieval, the inconsistent indexing problem still remains unsolved. In this paper, we present a hybrid methodology of integrating an indictive learning technique with a neural network (connectionist model) in order to solve both inconsistent indexing and incomplete query problems. Since an inductive learning technique has the ability to identify the most significant document index terms with various levels of relationship to their semantic significance, it provides a possible solution to the problem on inconsitent indexing. This paper reports the first phase of research that demonstrates how a neural network augmented by an inductive learning technique results in effective information retrieval performance in the ares that demand flexible inferencing and reasoning when incomplete queries and inconsistent problems are present
    Type
    a
  3. Cortez, E.M.: Planning and implementing a high performance knowledge base (1999) 0.00 
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    Abstract
    This paper discusses the conceptual framework for developing a rapid-prototype high-performance knowledge base for the four mission agencies of the U.S. Department of Agriculture and their university partners. These agencies include the Cooperative State Research, Education, Economic Service (CSREES), the Agricultural Research Service (ARS), the Economic Research Service (ERS), and the National Agriculture Statistical Service (NASS). The knowledge base, known as REEIS (Research, Education, Economic Information System), is a data mining application, where data are extracted from text, moved into different formats, allowing then the data mining features to run inferences, visualize connections, etc. -- all generated automatically. Described are alternative data mining models along with the generalized approach to building a Warehouse architecture. Also described is the methodology used for translating system requirements into specifications and for building the REEIS prototype. The method, known as the "Rational Unified Process", is one of iteration, quality assessment, and visual modeling. The two major obstacles in the project were the normalization of disparate data repositories, and the ability to achieve an acceptable level of semantic interoperability. A metadata vocabulary model is presented to address these obstacles
    Type
    a

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