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A Logic Framework for the Incremental Inductive Synthesis of Datalog
Theories
Giovanni Semeraro,
Floriana Esposito,
Donato Malerba,
Nicola
Fanizzi,
and Stefano Ferilli.
In Norbert E. Fuchs, editor, Proceedings of the 7th International Workshop
on Logic Programming Synthesis and Transformation, volume 1463 of
Lecture Notes in Computer Science, pages 300--321. Springer-Verlag,
August 1998. More behind this link.
Abstract
This paper presents a logic framework for the incremental inductive synthesis
of Datalog theories. It allows us to cast the problem as a process of
abstract diagnosis and debugging of an incorrect theory. This process
involves a search in a space, whose algebraic structure (conferred by the
notion of object identity) makes easy the definition of algorithms that meet
several properties which are deemed as desirable from the point of view of
the theoretical computer science. Such algorithms embody two ideal refinement
operators, one for generalizing incomplete clauses, and the other one for
specializing inconsistent clauses. These algorithms have been implemented in
INCR/H, an incremental learning system whose main characteristic consists of
the capability of extending autonomously the search to the space of clauses,
when no correct theories exist in the space of Datalog clauses. Experimental
results show that INCR/H is able to cope effectively and efficiently with the
real-world task of document understanding.
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