کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
562141 1451630 2012 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Induction of robust classifiers for web ontologies through kernel machines
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر سیستم های اطلاعاتی
پیش نمایش صفحه اول مقاله
Induction of robust classifiers for web ontologies through kernel machines
چکیده انگلیسی

The paper focuses on the task of approximate classification of semantically annotated individual resources in ontological knowledge bases. The method is based on classification models built through kernel methods, a well-known class of effective statistical learning algorithms. Kernel functions encode a notion of similarity among elements of some input space. The definition of a family of parametric language-independent kernel functions for individuals occurring in an ontology allows the application of these statistical learning methods on Semantic Web knowledge bases. The classification models induced by kernel methods offer an alternative way to classify individuals with respect to the typical exact and approximate deductive reasoning procedures. The proposed statistical setting enables further inductive approaches to a variety of other tasks that can better cope with the inherent incompleteness of the knowledge bases in the Semantic Web and with their potential incoherence due to their distributed nature. The effectiveness of the proposed method is empirically proved through experiments on the task of approximate classification with real ontologies collected from standard repositories.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Web Semantics: Science, Services and Agents on the World Wide Web - Volume 11, March 2012, Pages 1–13
نویسندگان
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