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Ontologies and folksonomies are currently the most prominent web content classification schemes. While their roles are similar, their engineering is different. In an attempt to combine and harness their distinct powers, web and information scientists are attempting to integrate them, merging the flexibility, collaboration and information aggregation of folksonomies with the standardisation, automated validation and interoperability of ontologies. This paper explores the basics of web information classification engineering, identifies the strengths and weaknesses of the existing methodologies, assesses their effectiveness and investigates a number of key quality issues. It then investigates the existing methods for integrating ontologies and folksonomies and examines the integration requirements. It finally proposes a common framework for reconciliation of the two classification approaches and quality assurance. 相似文献
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Web information retrieval and knowledge discovery are undergoing changes. The size of the Web and the heterogeneity of web pages generate new challenges in meeting user needs. This paper investigates the different methods deployed that add semantics to web content: semantic tagging and semantic APIs. The research carried out investigates existing systems in each category, outlining their primary features and functionality. It then proposes a framework for the evaluation of semantic tagging based on the main requirements for information discovery and recommends a number of comparative assessments, ranging from basic product information and requirements’ analysis to the evaluation of the APIs information modelling functionality. 相似文献
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