Search Engine: Approaches and Performance

Search Engine: Approaches and Performance

Eliana Campi (University of Salento, Italy) and Gianluca Lorenzo (University of Salento, Italy)
Copyright: © 2009 |Pages: 27
DOI: 10.4018/978-1-60566-034-9.ch004


This chapter presents technologies and approaches for information retrieval in a knowledge base. We intend to show that the use of ontology for domain representation and knowledge search offers a more efficient approach for knowledge management. This approach focuses on the meaning of the word, thus becoming an important element in the building of the Semantic Web. The search based on both keywords and ontology allows more effective information retrieval exploiting the Semantic of the information in a variety of data. We present a method for taxonomy building, annotating, and searching documents with taxonomy concepts. We also describe our experience in the creation of an informal taxonomy, the automatic classification, and the validation of search results with traditional measures, such as precision, recall and f-measure.
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Knowledge Management In The Kiwi Project

The KIWI project aims at representing and managing organizational knowledge using technologies and methodologies based on Semantic technologies. Taxonomy and/or more complex structures, like ontologies, are key elements to structure the knowledge base of an organization. The KIWI project develops a set of open source tools, which allow for a formal domain representation and enable creative cooperation among users and interaction with the knowledge base:

  • OntoMaker, an ontology editor;

  • OntoMeter, a tool to evaluate the quality of a formal representation;

  • OntoAssistant, a tool for semiautomatic document annotations;

  • Semantic Navigator, a tool to browse ontology and knowledge base.

We have proposed a methodology to provide a more effective automatic document classification process. In particular, the methodology comprises four steps:

  • 1.

    Developing a conceptual structure (an informal taxonomy) that presents the knowledge domain of the eBMS-ISUFI;

  • 2.

    Defining rules for each category through positive and negative training set;

  • 3.

    Filling the hierarchical structure with documents through semi-automatic classification;

  • 4.

    Testing the quality of the classification rules, through precision, recall and F-measure, refining of the classification rules and periodic taxonomy maintenance.

This methodology has been applied to the Virtual eBMS knowledge management system.

This technological platform, developed by ISUFI-eBMS University of Lecce, provides numerous tools for organizational knowledge management.

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