Titlebook: Computational Intelligence for Semantic Knowledge Management; New Perspectives for Giovanni Acampora,Witold Pedrycz,Autilia Vitiello Book 2

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Combining Collaborative Filtering and Semantic-Based Techniques to Recommend Components for Mashup aches, by using collaborative filtering techniques and semantic tagging, in order to rank mashups based on user goals. We have proven the validity of the proposed approach through experimental sessions based on data from the ProgrammableWeb repository.

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A Study on Local Search Meta-heuristics for Ontology Alignment,n local search optimization have been applied until now. This paper bridges this gap by performing a comparison among some popular local search-based algorithms for generating an alignment between two ontologies. As shown by the results involving well-known benchmarks, Tabu search results to be the

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1860-949X ss and uncertainty of information. This book reveals how computational intelligence methodologies, due to their natural inclination to deal with imprecision and partial truth, are opening new positive sc978-3-030-23760-8Series ISSN 1860-949X Series E-ISSN 1860-9503

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Natural Noise Management in Recommender Systems Using Fuzzy Tools,ral domains such as e-commerce, e-learning, and social networks. These systems require the elicitation of user preferences, which are not always precise because there are external factors such as human errors, or the inherent vagueness associated to human beings; which are usually related to user be

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Combining Collaborative Filtering and Semantic-Based Techniques to Recommend Components for Mashup nt of a new mashup application s/he has to cope with the abundance of services and information sources available on the Web, and with complex operations like filtering and joining. Thus, even a simple to use mashup editor is not capable of providing adequate support, unless it embeds intelligent met

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