20,875 research outputs found
Using fuzzy logic to handle the users' semantic descriptions in a music retrieval system
This paper provides an investigation of the potential application of fuzzy logic to semantic music recommendation. We show that a set of affective/emotive, structural and kinaesthetic descriptors can be used to formulate a query which allows the retrieval of intended music. A semantic music recommendation system was built, based on an elaborate study of potential users of music information retrieval systems. In this study analysis was made of the descriptors that best characterize the user's understanding of music. Significant relationships between expressive and structural descriptions of music were found. A straightforward fuzzy logic methodology was then applied to handle the quality ratings associated with the descriptions. Rigorous real-world testing of the semantic music recommendation system revealed high user satisfaction
Automatic domain ontology extraction for context-sensitive opinion mining
Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizationsâ business strategy development and individual consumersâ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline
Probabilistic latent semantic analysis as a potential method for integrating spatial data concepts
In this paper we explore the use of Probabilistic Latent Semantic Analysis (PLSA) as a method for quantifying semantic differences between land cover classes. The results are promising, revealing âhiddenâ or not easily discernible data concepts. PLSA provides a âbottom upâ approach to interoperability problems for users in the face of âtop downâ solutions provided by formal ontologies. We note the potential for a meta-problem of how to interpret the concepts and the need for further research to reconcile the top-down and bottom-up approaches
Semantic distillation: a method for clustering objects by their contextual specificity
Techniques for data-mining, latent semantic analysis, contextual search of
databases, etc. have long ago been developed by computer scientists working on
information retrieval (IR). Experimental scientists, from all disciplines,
having to analyse large collections of raw experimental data (astronomical,
physical, biological, etc.) have developed powerful methods for their
statistical analysis and for clustering, categorising, and classifying objects.
Finally, physicists have developed a theory of quantum measurement, unifying
the logical, algebraic, and probabilistic aspects of queries into a single
formalism. The purpose of this paper is twofold: first to show that when
formulated at an abstract level, problems from IR, from statistical data
analysis, and from physical measurement theories are very similar and hence can
profitably be cross-fertilised, and, secondly, to propose a novel method of
fuzzy hierarchical clustering, termed \textit{semantic distillation} --
strongly inspired from the theory of quantum measurement --, we developed to
analyse raw data coming from various types of experiments on DNA arrays. We
illustrate the method by analysing DNA arrays experiments and clustering the
genes of the array according to their specificity.Comment: Accepted for publication in Studies in Computational Intelligence,
Springer-Verla
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