Formal Concept Analysis(FCA),inductive logic programming(ILP) and Genetic programming(GP) have received increasing interest since them can be applied to many areas *** their formalisms are so different,these three app...
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Formal Concept Analysis(FCA),inductive logic programming(ILP) and Genetic programming(GP) have received increasing interest since them can be applied to many areas *** their formalisms are so different,these three approaches cannot be integrated easily though they share many common or similar goals and functionalities.A fusion will greatly enhance their problem solving *** this paper,a framework to combine FCA,ILP and GP is *** framework is based on a formalism of logic rules for refinement learning that can include concept and program both induction and evolution using FCA,ILP and *** experiment illustrates that our learner based on the framework is promising by compareing the performance with other learner.
Thrs special issue is associated with the 16th International Conference of inductive logic programming (ILP 2006), which represented a radical departure from previous years. Submissions were requested in two phases. T...
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Thrs special issue is associated with the 16th International Conference of inductive logic programming (ILP 2006), which represented a radical departure from previous years. Submissions were requested in two phases. The first phase involved submission of short papers (3 pages) which were then presented at the conference and included in a short papers proceedings. In the second phase, reviewers selected papers for long paper submission (15 pages maximum). These were then assessed by the same reviewers, who then decided which papers to include in the Journal Special Issue and Proceedings. In the first phase there were a record 77 papers, compared to the usual 20 or so long papers of previous years. Each paper was reviewed by 3 reviewers. Out of these, 71 were invited to submit long papers. Out of the long paper submissions, 7 were selected for the Machine Learning Journal Special Issue and 27 were accepted for the conference proceedings. In addition, two papers were nominated by PC referees for the applications prize and two for the theory prize. The papers in this special issue represent the diversity and vitality in present ILP research.
Fuzzy Description logics (DLs) are logics that allow to deal with structured vague knowledge. Although a relatively important amount of work has been carried out in the last years concerning the use of fuzzy DLs as on...
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Fuzzy Description logics (DLs) are logics that allow to deal with structured vague knowledge. Although a relatively important amount of work has been carried out in the last years concerning the use of fuzzy DLs as ontology languages, the problem of automatically managing the evolution of fuzzy ontologies has received very little attention so far. We describe here a logic-based computational method for the automated induction of fuzzy ontology axioms which follows the machine learning approach of inductive logic programming. The potential usefulness of the method is illustrated by means of an example taken from the tourism application domain.
Building 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 trad...
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Building 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,AL-log that integrates the description logic ALC and the function-free Horn clausal language DATALOG. 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 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.
In inductive logic programming (ILP), algorithms that are purely of the bottom-up or top-down type encounter several problems in practice. Since a majority of them are greedy ones, these algorithms stop when finding c...
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In inductive logic programming (ILP), algorithms that are purely of the bottom-up or top-down type encounter several problems in practice. Since a majority of them are greedy ones, these algorithms stop when finding clauses in local optima, according to the "quality" measure used for evaluating the results. Moreover, when learning clauses one by one, the induced clauses become less and less interesting as the algorithm is progressing to cover few remaining examples. In this paper, we propose a simulated annealing framework to overcome these problems. Using a refinement operator, we define neighborhood relations on clauses and on hypotheses (i.e. sets of clauses). With these relations and appropriate quality measures, we show how to induce clauses (in a coverage approach), or to induce hypotheses directly by using simulated annealing algorithms. We discuss the necessary conditions on the refinement operators and the evaluation measures to increase the effectiveness of the algorithm. Implementations (included a parallelized version of the algorithm) are described and experimentation results in terms of convergence of the method and in terms of accuracy are presented. (c) 2007 Elsevier Inc. All rights reserved.
Time plays an important role in the vast majority of problems and, as such, it is a vital issue to be considered when developing computer systems for solving problems. In the literature, one of the most influential fo...
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Time plays an important role in the vast majority of problems and, as such, it is a vital issue to be considered when developing computer systems for solving problems. In the literature, one of the most influential formalisms for representing time is known as Allen's Temporal Algebra based on a set of 13 relations (basic and reversed) that may hold between two time intervals. In spite of having a few drawbacks and limitations, Allen's formalism is still a convenient representation due to its simplicity and implementability and also, due to the fact that it has been the basis of several extensions. This paper explores the automatic learning of Allen's temporal relations by the inductive logic programming system FOIL, taking into account two possible representations for a time interval: (i) as a primitive concept and (ii) as a concept defined by the primitive concept of time point. The goals of the experiments described in the paper are (1) to explore the viability of both representations for use in automatic learning;(2) compare the facility and interpretability of the results;(3) evaluate the impact of the given examples for inducing a proper representation of the relations and (4) experiment with both representations under the assumption of a closed world (CWA), which would ease continuous learning using FOIL. Experimental results are presented and discussed as evidence that the CWA can be a convenient strategy when learning Allen's temporal relations.
inductive logic programming (ILP) deals with the problem of finding a hypothesis covering positive examples and excluding negative examples, where both hypotheses and examples are expressed in first-order logic. In th...
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inductive logic programming (ILP) deals with the problem of finding a hypothesis covering positive examples and excluding negative examples, where both hypotheses and examples are expressed in first-order logic. In this paper we employ constraint satisfaction techniques to model and solve a problem known as template ILP consistency, which assumes that the structure of a hypothesis is known and the task is to find unification of the contained variables. In particular, we present a constraint model with index variables accompanied by a Boolean model to strengthen inference and hence improve efficiency. The efficiency of models is demonstrated experimentally.
Meta-level abduction is a method to abduce missing rules in explaining observations. By representing rule structures of a problem in a form of causal networks, meta-level abduction infers missing links and unknown nod...
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Meta-level abduction is a method to abduce missing rules in explaining observations. By representing rule structures of a problem in a form of causal networks, meta-level abduction infers missing links and unknown nodes from incomplete networks to complete paths for observations. We examine applicability of meta-level abduction on networks containing both positive and negative causal effects. Such networks appear in many domains including biology, in which inhibitory effects are important in several biological pathways. Reasoning in networks with inhibition involves nonmonotonic inference, which can be realized by making default assumptions in abduction. We show that meta-level abduction can consistently produce both positive and negative causal relations as well as invented nodes. Case studies of meta-level abduction are presented in p53 signaling networks, in which causal relations are abduced to suppress a tumor with a new protein and to stop DNA synthesis when damage has occurred. Effects of our method are also analyzed through experiments of completing networks randomly generated with both positive and negative links.
This paper addresses semantic data mining, a new data mining paradigm in which ontologies are exploited in the process of data mining and knowledge discovery. This paradigm is introduced together with new semantic sub...
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This paper addresses semantic data mining, a new data mining paradigm in which ontologies are exploited in the process of data mining and knowledge discovery. This paradigm is introduced together with new semantic subgroup discovery systems SDM-search for enriched gene sets (SEGS) and SDM-Aleph. These systems are made publicly available in the new SDM-Toolkit for semantic data mining. The toolkit is implemented in the Orange4WS data mining platform that supports knowledge discovery workflow construction from local and distributed data mining services. On the basis of the experimental evaluation of semantic subgroup discovery systems on two publicly available biomedical datasets, the paper results in a thorough quantitative and qualitative evaluation of SDM-SEGS and SDM-Aleph and their comparison with SEGS, a system for enriched gene set discovery from microarray data.
Type Extension Trees are a powerful representation language for "count-of-count" features characterizing the combinatorial structure of neighborhoods of entities in relational domains. In this paper we prese...
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Type Extension Trees are a powerful representation language for "count-of-count" features characterizing the combinatorial structure of neighborhoods of entities in relational domains. In this paper we present a learning algorithm for Type Extension Trees (TET) that discovers informative count-of-count features in the supervised learning setting. Experiments on bibliographic data show that TET-learning is able to discover the count-of-count feature underlying the definition of the h-index, and the inverse document frequency feature commonly used in information retrieval. We also introduce a metric on TET feature values. This metric is defined as a recursive application of the Wasserstein-Kantorovich metric. Experiments with a k-NN classifier show that exploiting the recursive count-of-count statistics encoded in TET values improves classification accuracy over alternative methods based on simple count statistics. (C) 2013 Elsevier B.V. All rights reserved.
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