Part4 chapter3

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Inductive Logic Programming for Ontology Learning
Authors Farid CERBAH, Nadira LAMMARI
Part Learning from Web Data
Topics Ontology Learning from Databases
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Ontology Learning from Databases: Some Efficient Methods to Discover Semantic Patterns in Data

The chapter "Inductive Logic Programming for Ontology Learning" was written by Farid CERBAH, Nadira LAMMARI.

Abstract

Databases are often considered as the most reliable sources for knowledge extraction. Methods and tools have been proposed to build ontologies from databases, mainly by exploiting relational schemas. However, the resulting ontologies are often far below expectations, especially in terms of expressivity. A significant part of the domain-specific semantics needed to build more expressive ontologies can only be recovered from the stored data. This chapter describes some semantic patterns that are frequently encountered in databases and provides a systematic description of data-driven methods to exploit these patterns for ontology learning.

Topics / Key Words

Ontology Learning from Databases


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AbstractDatabases are often considered as the most Databases are often considered as the most reliable sources for knowledge extraction. Methods and tools have been proposed to build ontologies from

databases, mainly by exploiting relational schemas. However, the resulting ontologies are often far below expectations, especially in terms of expressivity. A significant part of the domain-specific semantics needed to build more expressive ontologies can only be recovered from the stored data. This chapter describes some semantic patterns that are frequently encountered in databases and provides a systematic description of data-driven methods to exploit these patterns for ontology learning. loit these patterns for ontology

learning.
AuthorFarid CERBAH  +, and Nadira LAMMARI  +
Book part titleLearning from Web Data  +
Chapter titleInductive Logic Programming for Ontology Learning  +
TopicsOntology Learning from Databases  +
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