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Condensed representations for inductive logic programming
 In Proc. of the 9th Intl. Conf. on Principles of Knowledge Representation and Reasoning
, 2004
"... When mining frequent Datalog queries, many queries will cover the same examples; i.e., they will be equivalent and hence, redundant. The equivalences can be due to the data set or to the regularities specified in the background theory. To avoid the generation of redundant clauses, we introduce vario ..."
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When mining frequent Datalog queries, many queries will cover the same examples; i.e., they will be equivalent and hence, redundant. The equivalences can be due to the data set or to the regularities specified in the background theory. To avoid the generation of redundant clauses, we introduce various types of condensed representations. More specifically, we introduce δfree and closed clauses, that are defined w.r.t. the data set, and semantically free and closed clauses, that take into account a logical background theory. A novel algorithm that employs these representations is also presented and experimentally evaluated on a number of benchmark problems in inductive logic programming.
Query optimization: Combining query packs and the oncetranformation
 IN INDUCTIVE LOGIC PROGRAMMING, 13TH INTERNATIONAL CONFERENCE, ILP 2003, SZEGED, HUNGARY, SHORT PRESENTATIONS
, 2003
"... In Inductive Logic Programming (ILP), several techniques have been introduced to improve the efficiency of query execution. One such technique is query pack execution. A set of queries with a common prefix, as it is generated by the refinement operator of a typical ILP system, can be executed faster ..."
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Cited by 4 (3 self)
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In Inductive Logic Programming (ILP), several techniques have been introduced to improve the efficiency of query execution. One such technique is query pack execution. A set of queries with a common prefix, as it is generated by the refinement operator of a typical ILP system, can be executed faster after it is converted into a tree structure called a query pack. Query transformations, on the other hand, improve the efficiency of executing a single query by transforming it into a different form that is more efficient to execute. Combining query packs with query transformations is difficult because a transformation may have a negative effect on the structure of the pack. The oncetransformation is one of the most important query transformations and can improve the efficiency of query execution by several orders of magnitude. In this work, we extend query pack execution in such a way that it is able to handle queries produced by the oncetransformation. We do this in the context of ilProlog, a high performance Prolog system with specific extensions for supporting ILP systems. We evaluate our approach on both artificial domains and real world ILP applications.
Sampling for Probabilistic Logical Models by Means of Program Specialization
, 2010
"... There is currently a large interest in probabilistic logical models. A popular algorithm for approximate probabilistic inference with such models is Gibbs sampling. From a computational perspective, Gibbs sampling boils down to repeatedly executing certain queries on a knowledge base composed of a s ..."
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There is currently a large interest in probabilistic logical models. A popular algorithm for approximate probabilistic inference with such models is Gibbs sampling. From a computational perspective, Gibbs sampling boils down to repeatedly executing certain queries on a knowledge base composed of a static part and a dynamic part. The larger the static part, the more redundancy there is in these repeated calls. This is problematic since inefficient Gibbs sampling yields poor approximations. We show how to apply program specialization to make Gibbs sampling more efficient. Concretely, we develop an algorithm that specializes the definitions of the querypredicates with respect to the static part of the knowledge base. In experiments on realworld benchmarks we obtain speedups of up to an order of magnitude.
A Framework for SetOriented Computation in Inductive Logic Programming and its Application in Generalizing Inverse Entailment ⋆
"... Abstract. We propose a new approach to Inductive Logic Programming that systematically exploits caching and offers a number of advantages over current systems. It avoids redundant computation, is more amenable to the use of setoriented generation and evaluation of hypotheses, and allows relational ..."
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Abstract. We propose a new approach to Inductive Logic Programming that systematically exploits caching and offers a number of advantages over current systems. It avoids redundant computation, is more amenable to the use of setoriented generation and evaluation of hypotheses, and allows relational DBMS technology to be more easily applied to ILP systems. Further, our approach opens up new avenues such as probabilistically scoring rules during search and the generation of probabilistic rules. As a first example of the benefits of our ILP framework, we propose a scheme for defining the hypothesis search space through Inverse Entailment using multiple example seeds. 1
Induction as a Search Procedure
"... 1 Induction as a Search Procedure This chapter introduces Inductive Logic Programming from the perspective of search algorithms in Computer Science. It first briefly considers the Version Spaces approach to induction, and then focuses on Inductive Logic Programming: from its formal definition and ..."
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1 Induction as a Search Procedure This chapter introduces Inductive Logic Programming from the perspective of search algorithms in Computer Science. It first briefly considers the Version Spaces approach to induction, and then focuses on Inductive Logic Programming: from its formal definition and main techniques and strategies, to priors used to restrict the search space and optimized sequential, parallel, and stochastic algorithms. The authors hope that this presentation of the theory and applications of Inductive Logic Programming will help the reader understand the theoretical underpinnings of inductive reasoning, and also provide a helpful overview of the StateoftheArt in the domain. 1
Refinement ActionBased Framework For Utilization Of Softcomputing In Inductive Learning
, 2013
"... In this thesis we describe a novel approach to application of Evolutionary Algorithms (EAs) into the field of Inductive Logic Programming (ILP). One of the main issues of ILP is the time complexity that comes out of the large size of ILP search space. Improving the search efficiency is therefore the ..."
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In this thesis we describe a novel approach to application of Evolutionary Algorithms (EAs) into the field of Inductive Logic Programming (ILP). One of the main issues of ILP is the time complexity that comes out of the large size of ILP search space. Improving the search efficiency is therefore the main objective of this thesis. To reach this, EAs were used in this work as they have proven to be efficient solvers for many optimization problems in many applications before. The target of the thesis is to design a system that would use EAs to speed up the search process in ILP while also enabling to use full potential of ILP including the possibilities of first order logic as well as the refinement operators. In the same time we aim at developing a system that would not be too problemspecific and would be both user and implementation friendly. Unlike the traditional approaches that focus on evolving populations of logical clauses, our refinementbased approach uses the evolutionary optimization process to search for sequences of refinement actions that iteratively adapt the initial working clause so that it changes into a well classifying clause. Utilization of contextsensitive refinements (adaptations) helps the search operations to produce mostly syntactically correct concepts and enables using available background