A conjunctive query problem is a problem to determine whether or not a tuple belongs to the answer of a conjunctive query over a database. In this paper, a tuple, a conjunctive query and a database in relational datab...
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A conjunctive query problem is a problem to determine whether or not a tuple belongs to the answer of a conjunctive query over a database. In this paper, a tuple, a conjunctive query and a database in relational database theory are regarded as a ground atom, a nonrecursive function-free definite clause and a finite set of ground atoms, respectively, in inductive logic programming terminology. An acyclic conjunctive query problem is a conjunctive query problem with acyclicity. Concerned with the acyclic conjunctive query problem, in this paper, we present the hardness results of predicting acyclic conjunctive queries from an instance with a j-database of which predicate symbol is at most j-ary. Also we deal with two kinds of instances, a simple instance as a set of ground atoms and an extended instance as a set of pairs of a ground atom and a description. We mainly show that, from both a simple and an extended instances, acyclic conjunctive queries are not polynomial-time predictable withj-databases (j >= 3) under the cryptographic assumptions, and predicting acyclic conjunctive queries with 2-databases is as hard as predicting DNF formulas. Hence, the acyclic conjunctive queries become a natural example that the equivalence between subsumption-efficiency and efficient pac-learnability from both a simple and an extended instances collapses. (c) 2005 Elsevier B.V. All rights reserved.
inductive logic programming (ILP) algorithms are classification algorithms that construct classifiers represented as logic programs. ILP algorithms have a number of attractive features, notably the ability to make use...
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inductive logic programming (ILP) algorithms are classification algorithms that construct classifiers represented as logic programs. ILP algorithms have a number of attractive features, notably the ability to make use of declarative background (user-supplied) knowledge. However, ILP algorithms deal poorly with large data sets (> 10(4) examples) and their widespread use of the greedy set-covering algorithm renders them susceptible to local maxima in the space of logic programs. This paper presents a novel approach to address these problems based on combining the local search properties of an inductive logic programming algorithm with the global search properties of an evolutionary algorithm. The proposed algorithm may be viewed as an evolutionary wrapper around a population of ILP algorithms. The evolutionary wrapper approach is evaluated on two domains. The chess-endgame (KRK) problem is an artificial domain that is a widely used benchmark in inductive logic programming, and Part-of-Speech Tagging is a real-world problem from the field of Natural Language Processing. In the latter domain, data originates from excerpts of the Wall Street Journal. Results indicate that significant improvements in predictive accuracy can be achieved over a conventional ILP approach when data is plentiful and noisy.
This article presents a combination of unsupervised and supervised learning techniques for the generation of word segmentation rules from a raw list of words. First, a language bias for word se mentation is introduced...
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This article presents a combination of unsupervised and supervised learning techniques for the generation of word segmentation rules from a raw list of words. First, a language bias for word se mentation is introduced and a simple genetic algorithm is used in the search for a segmentation that corresponds to the best bias value. In the second phase, the words segmented by the genetic algorithm are used as an input for the first order decision list learner CLOG. The result is a set of first order rules which can be used for segmentation of unseen words. When applied on either the training data or unseen data, these rules produce segmentations which are linguistically meaningful, and to a large degree conforming to the annotation provided.
inductive logic programming (ILP) is concerned with learning relational descriptions that typically have the form of logic programs. In a transformation approach, an ILP task is transformed into an equivalent learning...
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This paper presents a methodology to design a discrete-event system (DES) for the on-line supervision of biotechnological process. The DES is synthesised applying Wavelet Transform and inductive logic programming on t...
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ISBN:
(纸本)0780367227
This paper presents a methodology to design a discrete-event system (DES) for the on-line supervision of biotechnological process. The DES is synthesised applying Wavelet Transform and inductive logic programming on the measured signals constrained to the biotechnologist expert validation.
Recently there has been growing interest both to extend ILP to description logics and to apply it to knowledge discovery in databases. In this paper we present a novel approach to association rule mining which deals w...
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Recently there has been growing interest both to extend ILP to description logics and to apply it to knowledge discovery in databases. In this paper we present a novel approach to association rule mining which deals with multiple levels of description granularity. It relies on the hybrid language AL-log which allows a unified treatment of both the relational and structural features of data. A generality order and a downward refinement operator for AL-log pattern spaces is defined on the basis of query subsumption. This framework has been implemented in SPADA, an ILP system for mining multi-level association rules from spatial data. As an illustrative example, we report experimental results obtained by running the new version of SPADA on geo-referenced census data of Manchester Stockport.
Evolving and maintaining software requires adequate documentation of its implementation. However, due to the software's constant evolution, the documentation and implementation do not remain synchronised. Intentio...
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Evolving and maintaining software requires adequate documentation of its implementation. However, due to the software's constant evolution, the documentation and implementation do not remain synchronised. Intentional software views have been proposed as a documentation technique to alleviate this problem. Creating such views is not at all a trivial task, however. In this paper, we propose to use a learning algorithm that derives such intentional software views from extensional software views, which are much easier to build. The resulting approach combines the advantages of intentional software views with the ease of constructing extensional views. (C) 2003 Elsevier Ltd. All rights reserved.
The Variable Precision Rough Set inductive logic programming model (VPRSILP model) extends the Variable Precision Rough Set (VPRS) model to inductive logic programming (ILP). The generic Rough Set inductivelogic Prog...
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The Variable Precision Rough Set inductive logic programming model (VPRSILP model) extends the Variable Precision Rough Set (VPRS) model to inductive logic programming (ILP). The generic Rough Set inductive logic programming (gRS-ILP) model provides a framework for ILP when the setting is imprecise and any induced logic program will not be able to distinguish between certain positive and negative examples. The gRS-ILP model is extended in this paper to the VPRSILP model by including features of the VPRS model. The VPRSILP model is applied to strings and an illustrative experiment on transmembrane domains in amino acid sequences is presented.
In this paper we present 1BC and 1BC2, two systems that perform naive Bayesian classification of structured individuals. The approach of 1BC is to project the individuals along first-order features. These features are...
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In this paper we present 1BC and 1BC2, two systems that perform naive Bayesian classification of structured individuals. The approach of 1BC is to project the individuals along first-order features. These features are built from the individual using structural predicates referring to related objects ( e. g., atoms within molecules), and properties applying to the individual or one or several of its related objects ( e. g., a bond between two atoms). We describe an individual in terms of elementary features consisting of zero or more structural predicates and one property;these features are treated as conditionally independent in the spirit of the naive Bayes assumption. 1BC2 represents an alternative first-order upgrade to the naive Bayesian classifier by considering probability distributions over structured objects ( e. g., a molecule as a set of atoms), and estimating those distributions from the probabilities of its elements ( which are assumed to be independent). We present a unifying view on both systems in which 1BC works in language space, and 1BC2 works in individual space. We also present a new, efficient recursive algorithm improving upon the original propositionalisation approach of 1BC. Both systems have been implemented in the context of the first-order descriptive learner Tertius, and we investigate the differences between the two systems both in computational terms and on artificially generated data. Finally, we describe a range of experiments on ILP benchmark data sets demonstrating the viability of our approach.
This paper introduces a proof procedure that integrates Abductive logicprogramming (ALP) and inductive logic programming (ILP) to automate the learning of first order Horn clause theories from examples and background...
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This paper introduces a proof procedure that integrates Abductive logicprogramming (ALP) and inductive logic programming (ILP) to automate the learning of first order Horn clause theories from examples and background knowledge. The work builds upon a recent approach called Hybrid Abductive inductive Learning (HAIL) by showing how language bias can be practically and usefully incorporated into the learning process. A proof procedure for HAIL is proposed that utilises a set of user-specified mode declarations to learn hypotheses that satisfy a given language bias. A semantics is presented that accurately characterises the intended hypothesis space and includes the hypotheses derivable by the proof procedure. An implementation is described that combines an extension of the Kakas-Mancarella ALP procedure within an ILP procedure that generalises the Progol system of Muggleton. The explicit integration of abduction and induction is shown to allow the derivation of multiple clause hypotheses in response to a single seed example and to enable the inference of missing type information in a way not previously possible.
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