# Inductive Logic Programming in Databases: From Datalog to $\mathcal{DL}+log}^{\neg\vee}$

@article{Lisi2010InductiveLP,
title={Inductive Logic Programming in Databases: From Datalog to \$\mathcal\{DL\}+log\}^\{\neg\vee\}\$},
author={Francesca Alessandra Lisi},
journal={Theory and Practice of Logic Programming},
year={2010},
volume={10},
pages={331 - 359}
}
• F. Lisi
• Published 12 March 2010
• Computer Science
• Theory and Practice of Logic Programming
Abstract In 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…
• F. Lisi
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• F. Lisi
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This work presents a method for effective revision of learned Horn rules by adding exceptions (i.e., negated atoms) into their bodies and demonstrates the effectiveness of the developed method and the improvements in accuracy for KG completion by rule-based fact prediction.
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• 2011
The system -QuIn supports a variant of the frequent pattern discovery task by following the Onto-Relational Learning approach, which takes taxonomic ontologies into account during the discovery process and produces descriptions of a given relational database at multiple granularity levels.
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## References

SHOWING 1-10 OF 111 REFERENCES

This paper provides a general framework for learning in ${\mathcal AL}$-log, a hybrid language that integrates the description logic ${\ mathcal ALC}$ and the function-free Horn clausal language Datalog, thus turning out to be a small yet sufficiently expressive subset of SWRL.
• Computer Science
Machine Learning
• 2004
This paper presents a novel approach to association rule mining which deals with multiple levels of description granularity and relies on the hybrid language A -log which allows a unified treatment of both the relational and structural features of data.
DL +log is defined, a general framework for the integration of Description Logics and disjunctive Datalog rules that allows for a tighter form of integration between DL-KBs and Datalogs rules which overcomes the main representational limits of the approaches based on the safety condition.
• Computer Science
Journal of Automated Reasoning
• 2007
It is shown that, for the DLs of the DL-Lite family, the usual DL reasoning tasks are polynomial in the size of the TBox, and query answering is LogSpace in thesize of the ABox, which is the first result ofPolynomial-time data complexity for query answering over DL knowledge bases.
• Computer Science
ILP
• 2008
This paper considers the problem of learning rules from observations that combine relational data and ontologies, and identifies the ingredients of an ILP solution to it, and adopts an instantiation of the KR framework which integrates the DL $\mathcal{SHIQ}$ and positive Datalog.
• Computer Science
AI*IA
• 2003
A relation of subsumption is defined, called $$\mathcal{B}$$-subsumption, inspired by Buntine’s generalized subsumption, and it is shown that it induces a quasi-order over the space of constrained DATALOG clauses and provides a procedure for checking $$\succeq_{\mathcal_{B}}$$ under the object identity bias.
• Computer Science
Journal of Intelligent Information Systems
• 2004
A method for query answering in AL-log based on constrained resolution, where the usual deduction procedure defined for Datalog is integrated with a method for reasoning on the structural knowledge.
• Peter A. Flach
• Computer Science
Transactions and Change in Logic Databases
• 1998
This chapter aims at demonstrating that inductive logic programming (ILP), a recently established subfield of machine learning that induces first-order clausal theories from examples, combines very
• Computer Science
SWAP
• 2005
This paper provides a framework for learning Semantic Web rules which adopts Inductive Logic Programming (ILP) as methodological apparatus and AL-log as KR&R setting and inductive hypotheses are represented as constrained Datalog clauses and evaluated against observations by means of coverage relations.
• Computer Science
Data Mining and Knowledge Discovery
• 2004
This paper relates the setting to propositional data mining and to the classical ILP setting, and shows that learning from interpretations corresponds to learning from multiple relations and thus extends the expressiveness of propositional learning, while maintaining its efficiency to a large extent.