the proceedings contain 26 papers. the special focus in this conference is on inductivelogicprogramming. the topics include: Automated synthesis of data analysis programs;at the interface of inductivelogic programm...
ISBN:
(纸本)9783540229414
the proceedings contain 26 papers. the special focus in this conference is on inductivelogicprogramming. the topics include: Automated synthesis of data analysis programs;at the interface of inductivelogicprogramming and statistics;from promising to profitable applications of ilp;experiences with extracting relations from biomedical text;macro operators revisited in inductivelogicprogramming;bottom-up ilp using large refinement steps;on the effect of caching in recursive theory learning;efficiently scaling foil for multi-relational data mining of large datasets;learning an approximation to inductivelogicprogramming clause evaluation;learning ensembles of first-order clauses for recall precision curves;automatic induction of first-order logic descriptors type domains from observations;on avoiding redundancy in inductivelogicprogramming;learning goal hierarchies from structured observations and expert annotations;an efficient algorithm for reducing clauses based on constraint satisfaction techniques;improving rule evaluation using multitask learning;learning logic programs with annotated disjunctions;a simulated annealing framework for ilp;modelling inhibition in metabolic pathways through abduction and induction;first order random forests with complex aggregates;a Monte Carlo study of randomised restarted search in ilp and learning, logic, and probability.
inductivelogicprogramming (ilp) is a generic tool aiming at learning rules from relational databases. Introducing fuzzy sets arid fuzzy implication connectives in this framework allows us to increase the expressive ...
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inductivelogicprogramming (ilp) is a generic tool aiming at learning rules from relational databases. Introducing fuzzy sets arid fuzzy implication connectives in this framework allows us to increase the expressive power of the induced rules while keeping the readability of the rules. Moreover, fuzzy sets facilitate the handling of numerical attributes by avoiding crisp and arbitrary transitions between classes. In this paper, the meaning of a fuzzy rule is encoded by its implication operator, which is to be determined in the learning process. An algorithm is proposed for inducing first order rules having fuzzy predicates, together withthe most appropriate implication operator. the benefits of introducing fuzzy logic in ilp and the validation process of what has been learnt are discussed and illustrated on a benchmark
inductivelogicprogramming can be viewed as a style of statistical inference where the model that is inferred to explain the observed data happens to be a logic program. In general, logic programs have important diff...
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ISBN:
(纸本)3540229418
inductivelogicprogramming can be viewed as a style of statistical inference where the model that is inferred to explain the observed data happens to be a logic program. In general, logic programs have important differences to other models (such as linear models, tree-based models, etc) found in the statistical literature. this why we have ilpconferences!
PharmaDM was founded end 2000 as a spin-off from three European universities (Oxford, Aberystwyth, and Leuven) that participated in two subsequent EC projects on inductivelogicprogramming (ilp I-II, 1992-1998). Amon...
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ISBN:
(纸本)3540229418
PharmaDM was founded end 2000 as a spin-off from three European universities (Oxford, Aberystwyth, and Leuven) that participated in two subsequent EC projects on inductivelogicprogramming (ilp I-II, 1992-1998). Amongst the projects highlights was a series of publications that demonstrated the added-value of ilp in applications related to the drug discovery process. the mission of PharmaDM is to build on those promising results, including software modules developed at the founding universities (i.e., Aleph, Tilde, Warmr, ilprolog), and develop a profitable ilp based data mining product customised to the needs of drug discovery researchers. Technology development at PharmaDM is mostly based on demand pull, i.e., driven by user requirements. In this presentation I will look at the way ilp technology at PharmaDM has evolved over the past four years and the user feedback that has stimulated this evolution.
We have been applying inductivelogicprogramming (ilp) to the task of learning how to extract relations from biomedical text (specifically, Medline abstracts). Our primary focus has been learning to recognize instanc...
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ISBN:
(纸本)3540229418
We have been applying inductivelogicprogramming (ilp) to the task of learning how to extract relations from biomedical text (specifically, Medline abstracts). Our primary focus has been learning to recognize instances of this protein is localized in this part of the cell from labeled training examples. ilp allows one to naturally make use of substantial background knowledge (e. g., biomedical ontologies such as the Gene Ontology - GO - and MEdical Subject Headings - MESH) and rich representations of the examples (e. g., parse trees). We discuss how we formulated this task for ilp and describe our methods for scaling ilp to this large task. We conclude with a discussion of some of the major challenges that ilp needs to address in order to scale to large tasks.
One challenge faced by many inductivelogicprogramming (ilp) systems is poor scalability to problems with large search spaces and many examples. Randomized search methods such as stochastic clause selection (SCS) and...
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ISBN:
(纸本)3540229418
One challenge faced by many inductivelogicprogramming (ilp) systems is poor scalability to problems with large search spaces and many examples. Randomized search methods such as stochastic clause selection (SCS) and rapid random restarts (RRR) have proven somewhat successful at addressing this weakness. However, on datasets where hypothesis evaluation is computationally expensive, even these algorithms may take unreasonably long to discover a good solution. We attempt to improve the performance of these algorithms on datasets by learning an approximation to ilp hypothesis evaluation. We generate a small set of hypotheses, uniformly sampled from the space of candidate hypotheses, and evaluate this set on actual data. these hypotheses and their corresponding evaluation scores serve as training data for learning an approximate hypothesis evaluator. We outline three techniques that make use of the trained evaluation-function approximator in order to reduce the computation required during an ilp hypothesis search. We test our approximate clause evaluation algorithm using the popular ilp system Aleph. Empirical results are provided on several benchmark datasets. We show that the clause evaluation function can be accurately approximated.
Program synthesis is the systematic, usually automatic construction of correct and efficient executable code from declarative statements. Program synthesis is routinely used in industry to generate GUIs and for databa...
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ISBN:
(纸本)3540229418
Program synthesis is the systematic, usually automatic construction of correct and efficient executable code from declarative statements. Program synthesis is routinely used in industry to generate GUIs and for database support.
the generation and testing of hypotheses is widely considered to be the primary method by which Science progresses. So much so, that it is still common to find a scientific proposal or an intellectual argument damned ...
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ISBN:
(纸本)3540229418
the generation and testing of hypotheses is widely considered to be the primary method by which Science progresses. So much so, that it is still common to find a scientific proposal or an intellectual argument damned on the grounds that it has no hypothesis being tested, it is merely a fishing expedition, and so on. Extreme versions run if there is no hypothesis, it is not Science, the clear implication being that hypothesis-driven programmes (as opposed to data-driven studies) are the only contributor to the scientific endeavour. this misrepresents how knowledge and understanding are actually generated from the study of natural phenomena and laboratory experiments. Hypothesis-driven and inductive modes of reasoning are not competitive, but complementary, and both are required in post-genomic biology.
the LOGAN-H system is a bottom up ilp system for learning multi-clause and multi-predicate function free Horn expressions in the framework of learning from interpretations. the paper introduces a new implementation of...
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ISBN:
(纸本)3540229418
the LOGAN-H system is a bottom up ilp system for learning multi-clause and multi-predicate function free Horn expressions in the framework of learning from interpretations. the paper introduces a new implementation of the same base algorithm which gives several orders of magnitude speedup as well as extending the capabilities of the system. New tools include several fast engines for subsumption tests, handling real valued features, and pruning. We also discuss using data from the standard ilp setting in our framework, which in some cases allows for further speedup. the efficacy of the system is demonstrated on several ilp datasets.
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 boththe 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.
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