Conceptual Indexing: A Better Way to Organize Knowledge
Conceptual Indexing: A Better Way to Organize Knowledge
Dictionary-based techniques for cross-language information retrieval
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Searching and exploring controlled vocabularies
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Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Connecting the universal to the specific: towards the global grid
IWIC'07 Proceedings of the 1st international conference on Intercultural collaboration
LKR'08 Proceedings of the 3rd international conference on Large-scale knowledge resources: construction and application
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This article describes our approach to accessing Knowledge Organization Systems expressed using the Simple Knowledge Organization System (SKOS) data model. We share the view that the Web is becoming a multilingual lexical resource and a distribution infrastructure for knowledge resources. We aim to tap into this for the particular use case of Cross-Language Information Retrieval systems. The SKOS framework allows the description of monolingual or multilingual thesauri, controlled vocabularies and other classification systems in a simple machine-understandable representation. It has support for decentralized distribution on the Web of any resource described with it and includes mechanisms to interconnect different concept schemes. Yet, when building our prototype CLIR system different processes require more than the existing content of a SKOS resource: concept descriptions, labels and basic inter-concept relations. For example the SKOS concept indexing phase entails identifying potential occurrences of a SKOS concept in a text and to disambiguate based on the semantics referenced to in the overall SKOS scheme. By design, the SKOS data model does not formally define semantics of its concepts thus we have built a set of three algorithms that help generate a multilingual dataset linking to the original SKOS dataset and providing more details about the lexical entities that describe concepts. This new dataset contains specific RDF triples that aid concept identification, disambiguation and translation in CLIR.