LivingKnowledge goal is to bring a new quality into search and knowledge management technology for more concise, complete and contextualised search results.
The paper “LivingKnowledge : Kernel Methods for Relational Learning and Semantic Modeling” written by A. Moschitti has been presented at ISoLA2010 in Amirandes, Heraclion, Crete on 18th - 20th October 2010.
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.