Inductive Logic Programming in Databases: From Datalog to

AbstractIn this paper we address an issue that has been brought to the attention of the database community with the advent of the Semantic Web, i.e., the issue of how ontologies (and semantics conveyed by them) can help solving typical database problems, through a better understanding of Knowledge Representation (KR) aspects related to databases. In particular, we investigate this issue from the ILP perspective by considering two database problems, (i) the definition of views and (ii) the definition of constraints, for a database whose schema is represented also by means of an ontology. Both can be reformulated as ILP problems and can benefit from the expressive and deductive power of the KR framework $\mathcal{DL}+log}^{\neg\vee}$. We illustrate the application scenarios by means of examples.

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Building Rules on Top of Ontologies for the Semantic Web with Inductive Logic Programming

Theory and Practice of Logic Programming ◽

10.1017/s1471068407003195 ◽

2008 ◽

Vol 8 (3) ◽

pp. 271-300 ◽

Cited By ~ 26

Author(s):

FRANCESCA A. LISI

Keyword(s):

Semantic Web ◽

Logic Programming ◽

Inductive Logic Programming ◽

Description Logic ◽

Ad Hoc ◽

Inductive Logic ◽

Knowledge Representation And Reasoning ◽

Reasoning Systems ◽

Hybrid Knowledge ◽

Deductive Power

AbstractBuilding rules on top of ontologies is the ultimate goal of the logical layer of the Semantic Web. To this aim, an ad-hoc markup language for this layer is currently under discussion. It is intended to follow the tradition of hybrid knowledge representation and reasoning systems, such as$\mathcal{AL}$-log that integrates the description logic$\mathcal{ALC}$and the function-free Horn clausal languageDatalog. In this paper, we consider the problem of automating the acquisition of these rules for the Semantic Web. We propose a general framework for rule induction that adopts the methodological apparatus of Inductive Logic Programming and relies on the expressive and deductive power of$\mathcal{AL}$-log. The framework is valid whatever the scope of induction (description versus prediction) is. Yet, for illustrative purposes, we also discuss an instantiation of the framework which aims at description and turns out to be useful in Ontology Refinement.

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Inductive logic programming applied for knowledge representation in computer music/ Programação lógica indutiva aplicada para representação do conhecimento em música computacional

Brazilian Applied Science Review ◽

10.34115/basrv5n4-009 ◽

2021 ◽

Vol 5 (4) ◽

pp. 1840-1857

Author(s):

Clenio B. Gonçalves Junior ◽

Murillo Rodrigo Petrucelli Homem

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Knowledge Representation ◽

Logic Programming ◽

Inductive Logic Programming ◽

Computer Music ◽

Inductive Logic ◽

Essential Element ◽

Research Field ◽

Musical Knowledge

In Computer Music, the knowledge representation process is an essential element for the development of systems. Methods have been applied to provide the computer with the ability to generate conclusions based on previously established experience and definitions. In this sense, Inductive Logic Programming presents itself as a research field that incorporates concepts of Logic Programming and Machine Learning, its declarative character allows musical knowledge to be presented to non-specialist users in a naturally understandable way. The present work performs a systematic review based on approaches that use Inductive Logic Programming in the representation of musical knowledge. Questions that these studies seek to address were raised, as well as identifying characteristic aspects related to their application.

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