The Core Method: Connectionist Model Generation for First-Order Logic Programs

TitleThe Core Method: Connectionist Model Generation for First-Order Logic Programs
Publication TypeBook Chapter
Year of Publication2007
AuthorsSebastian Bader, Steffen Holldobler, Andreas Witzel, Pascal Hitzler
KeywordsArtificial Intelligence
Abstract

In Artificial Intelligence, knowledge representation studies the formalisation of knowledge and its processing within machines. Techniques of automated reasoning allow a computer system to draw conclusions from knowledge represented in a machine-interpretable form. Recently, ontologies have evolved in computer science as computational artefacts to provide computer systems with a conceptual yet computational model of a particular domain of interest. In this way, computer systems can base decisions on reasoning about domain knowledge, similar to humans. This chapter gives an overview on basic knowledge representation aspects and on ontologies as used within computer systems. After introducing ontologies in terms of their appearance, usage and classification, it addresses concrete ontology languages that are particularly important in the context of the Semantic Web. The most recent and predominant ontology languages and formalisms are presented in relation to each other and a selection of them is discussed in more detail.

Full Text

Sebastian Bader, Pascal Hitzler, Steffen Hölldobler and Andreas Witzel, 'The Core Method: Connectionist Model Generation for First-Order Logic Programs,' In: Barbara Hammer, Pascal Hitzler (Eds.), Perspectives of Neural-Symbolic Integration: Studies in Computational Intelligence Vol. 77. Springer, 2007, ISBN 978-3-540-73952-1, pp. 205-232.
pages: 205-232
publisher: Springer
year: 2007
hasEditor: Pascal Hitzler
hasURL: http://knoesis.wright.edu/faculty/pascal/resources/publications/c09-bade...
hasBookTitle: Perspectives of Neural-Symbolic Integration. Studies in Computational Intelligence Vol. 77