LivingKnowledge: Kernel methods for relational learning and semantic modeling

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages15-19
Number of pages5
Volume6416 LNCS
EditionPART 2
DOIs
Publication statusPublished - 23 Dec 2010
Externally publishedYes
Event4th International Symposium on Leveraging Applications, ISoLA 2010 - Heraklion, Crete, Greece
Duration: 18 Oct 201021 Oct 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6416 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other4th International Symposium on Leveraging Applications, ISoLA 2010
CountryGreece
CityHeraklion, Crete
Period18/10/1021/10/10

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Keywords

  • Kernel Methods
  • Natural Language Processing
  • Structural Kernels
  • Support Vector Machines

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Moschitti, A. (2010). LivingKnowledge: Kernel methods for relational learning and semantic modeling. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 2 ed., Vol. 6416 LNCS, pp. 15-19). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6416 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-16561-0_5