Abstract
Latest results of statistical learning theory have provided techniques such us pattern analysis and relational learning, which help in modeling system behavior, e.g. the semantics expressed in text, images, speech for information search applications (e.g. as carried out by Google, Yahoo,..) or the semantics encoded in DNA sequences studied in Bioinformatics. These represent distinguished cases of successful use of statistical machine learning. The reason of this success relies on the ability of the latter to overcome the critical limitations of logic/rule-based approaches to semantic modeling: although, from a knowledge engineer perspective, hand-crafted rules are natural methods to encode system semantics, noise, ambiguity and errors, affecting dynamic systems, prevent them from being effective.
One drawback of statistical approaches relates to the complexity of modeling world objects in terms of simple parameters. In this paper, we describe kernel methods (KM), which are one of the most interesting results of statistical learning theory capable to abstract system design and make it simpler. We provide an example of effective use of KM for the design of a natural language application required in the European Project LivingKnowledge.
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Moschitti, A. (2010). LivingKnowledge: Kernel Methods for Relational Learning and Semantic Modeling. In: Margaria, T., Steffen, B. (eds) Leveraging Applications of Formal Methods, Verification, and Validation. ISoLA 2010. Lecture Notes in Computer Science, vol 6416. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16561-0_5
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DOI: https://doi.org/10.1007/978-3-642-16561-0_5
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