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A Comprehensive Framework for Building Multilingual Domain Ontologies: Creating a Prototype Biosecurity Ontology








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    Book (stand-alone)
    Automatic Term Relationship Cleaning and Refinement for AGROVOC 2005
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    AGROVOC is a multilingual thesaurus developed and maintained by the Food and Agricultural Organization of the United Nations. Like all thesauri, it contains some explicit semantics, which allow it to be transformed into an ontology or used as a resource for ontology construction. However, most thesauri, AGROVOC included, give very broad relationships that lack the semantic precision needed in an ontology. Many relationships in a thesaurus are incorrectly applied or defined too broadly. According ly, extracting ontological relationships from a thesaurus requires data cleaning and refinement of semantic relationships. This paper presents a hybrid approach for (semi-)automatically detecting these problematic relationships and for suggesting more precisely defined ones. The system consists of three main modules: Rule Acquisition, Detection and Suggestion, and Verification. The Refinement Rule Acquisition module is used to acquire rules specified by experts and through machine learning. The Detection and Suggestion module uses noun phrase analysis and WordNet alignment to detect incorrect relationships and to suggest more appropriate ones based on the application of the acquired rules. The Verification module is a tool for confirming the proposed relationships. We are currently trying to apply the learning system with some semantic relationships to test our method.
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    7.3.1: Results from experiments in ontology learning including evaluation and recommendation
    NeOn: Lifecycle Support for Networked Ontologies, Integrated Project (IST-2005-027595). Priority: IST-2004-2.4.7,
    2007
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    This document describes the Ontology Learning experiments we performed in order to recommend a set of ontology learning techniques to enhance the ontology engineering process in the fisheries domain in place at FAO. Experiments have been conceived in the wider context of the WorkPackage 7 of the NeOn Project. The main contribution of this document to WP7 is a set of recommendations and best practices to exploit semi-automatic technique to acquire knowledge either from domain specific documents a nd existing ontologies. The basic criterion for success indication adopted is the reduction of the development time required for the Ontology Engineering process actually in place at FAO. After a brief overview of WP7, the document addresses the following issues: to describe the state of the art in ontology learning and; to set up an evaluation case study in the fisheries domain; to identify suitable techniques which can be profitably applied to fit the user’s requirements; to evaluate such tech niques in the use case scenario, and, finally, to provide a set of recommendations indicating the most reliable techniques to be included in the ontology engineering lifecycle.
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    Requirements for the treatment of multilinguality in ontologies within FAO 2007
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    International organizations like FAO are intrinsically multilingual. FAO is currently experimenting with semantic-oriented technologies based on ontologies, with the purpose of integrating data across various information systems and providing better services to end users. However, in order for these technologies to be used in real-life scenarios, models and tools for accommodating and managing multilingual data are needed. This paper analyzes the requirements for the treatment of multilinguality as resulting from the experience we gained at FAO.

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