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Thesaurus alignment for Linked Data publishing








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    Thesaurus alignment for Linked Data publishing 2011
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    As part of the publication of the AGROVOC thesaurus as Linked Data (LD), AGROVOC is now mapped with six well-known thesauri in the agricultural domain, i.e., EUROVOC, NALT, GEMET, STW, TheSoz, RAMAEU. To find matching candidates, known matching algorithms discussed in the literature and available from public API were used. Results were evaluated by a domain expert, and almost total precision obtained. The candidate matches that were confirmed have already been added to the LD version of AGROVOC. Moreover, the owners of two of the thesauri mapped with AGROVOC have included in their data the mapping we identified. From this work, we conclude that we achieved our goal to enhance the Linked Data version of AGROVOC with reliable links to other thesauri, following a procedure that is fully replicable.
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    A Collaborative Framework for Managing and Publishing KOS 2011
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    In the Food and Agriculture Organization of the United Nations (FAO), the need to revamp its popular agriculture vocabulary AGROVOC using Semantic Web knowledge representation standards combined with the need to provide a collaborative environment for development and maintenance purposes, pushed forward the realization of a dedicated AGROVOC thesaurus maintenance tool. With the progressive standardization of the AGROVOC knowledge model, following recent Simple Knowledge Organization System (SKOS ) recommendations by the World Wide Web Consortium (W3C) and with the addition of more FAO-maintained vocabularies, the former “AGROVOC Concept Server Workbench” has become a general-purpose framework for thesauri and vocabulary development and is now reborn as “ VocBench”. In this paper, we describe the path which led to its realization and its main features
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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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