the proceedings contain 24 papers. the topics discussed include: knowledge-directed theory revision;towards clausal discovery for stream mining;on the relationship between logical Bayesian networks and probabilistic l...
ISBN:
(纸本)364213839X
the proceedings contain 24 papers. the topics discussed include: knowledge-directed theory revision;towards clausal discovery for stream mining;on the relationship between logical Bayesian networks and probabilistic logicprogramming based on the distribution semantics;induction of relational algebra expressions;a logic-based approach to relation extraction from texts;discovering rules by meta-level abduction;inductive generalization of analytically learned goal hierarchies;nonmonotonic onto-relational learning;cp-logictheory inference with contextual variable elimination and comparison to BDD based inference methods;speeding up inference in statistical relational learning by clustering similar query literals;an inductivelogicprogramming approach to validate hexose binding biochemical knowledge;boosting first-order clauses for large, skewed data sets;and transfer learning via relational templates.
the proceedings contain 19 papers. the special focus in this conference is on inductivelogicprogramming. the topics include: A Simulated Annealing Meta-heuristic for Concept Learning in Description logics;generative...
ISBN:
(纸本)9783030974534
the proceedings contain 19 papers. the special focus in this conference is on inductivelogicprogramming. the topics include: A Simulated Annealing Meta-heuristic for Concept Learning in Description logics;generative Clausal Networks: Relational Decision Trees as Probabilistic Circuits;human-Like Rule Learning from Images Using One-Shot Hypothesis Derivation;learning and Revising Dynamic Temporal theories in the Full Discrete Event Calculus;learning logic Programs Using Neural Networks by Exploiting Symbolic Invariance;feature Learning by Least Generalization;a First Step Towards Even More Sparse Encodings of Probability Distributions;mapping Across Relational Domains for Transfer Learning with Word Embeddings-Based Similarity;programmatic Policy Extraction by Iterative Local Search;online Learning of logic Based Neural Network Structures;embedding Models for Knowledge Graphs Induced by Clusters of Relations and Background Knowledge;using Domain-Knowledge to Assist Lead Discovery in Early-Stage Drug Design;preface;non-parametric Learning of Embeddings for Relational Data Using Gaifman Locality theorem;transfer Learning for Boosted Relational Dependency Networks through Genetic Algorithm;Ontology Graph Embeddings and ilp for Financial Forecasting;synthetic Datasets and Evaluation Tools for inductive Neural Reasoning.
the proceedings contain 20 papers. the topics discussed include: building theories of the world: human and machine learning perspectives;SRL without tears: an ilp perspective;semantic web meets ilp: unconsumated love,...
ISBN:
(纸本)3540859276
the proceedings contain 20 papers. the topics discussed include: building theories of the world: human and machine learning perspectives;SRL without tears: an ilp perspective;semantic web meets ilp: unconsumated love, or no love lost?;learning expressive models of gene regulation;information overload and FP7 funding opportunities in 2009-10;a model to study phase transition and plateaus in relational learning;top-down induction of relational model trees in multi-instance learning;challenges in relational learning for real-time systems applications;discriminative structure learning of Markov logic networks;an experiment in robot discovery withilp;using the bottom clause and mode declarations on FOL theory revision from examples;DL-FOIL: concept learning in description logics;feature discovery with type extension trees;and feature construction using theory-guided sampling and randomised search.
the handling of exceptions in multiclass problems is a tricky issue in inductivelogicprogramming (ilp). In this paper we propose a new formalization of the ilp problem which accounts for default reasoning, and is en...
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the handling of exceptions in multiclass problems is a tricky issue in inductivelogicprogramming (ilp). In this paper we propose a new formalization of the ilp problem which accounts for default reasoning, and is encoded with first-order possibilistic logic. We show that this formalization allows us to handle rules with exceptions, and to prevent an example to be classified in more than one class. the possibilistic logic view of ilp problem, can be easily handled at the algorithmic level as an optimization problem.
In this paper we developed an inductivelogicprogramming (ilp) based framework ExOpaque that is able to extract a set of Horn clauses from an arbitrary opaque machine learning model, to describe the behavior of the o...
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ISBN:
(纸本)076953015X
In this paper we developed an inductivelogicprogramming (ilp) based framework ExOpaque that is able to extract a set of Horn clauses from an arbitrary opaque machine learning model, to describe the behavior of the opaque model with high fidelity while maintaining the simplicity of the Horn clauses for human interpretations.
Many inductivelogicprogramming systems have operators reorganizing the program so far inferred, such as the intra-construction operator of CIGOL. At the same time, there is a similar reorganizing operator, called th...
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Many inductivelogicprogramming systems have operators reorganizing the program so far inferred, such as the intra-construction operator of CIGOL. At the same time, there is a similar reorganizing operator, called the "folding rule," developed in program transformation. We argue that there are advantages in using an extended folding rule as a reorganizing operator for inductive-inference systems. Such an extended folding rule allows an inductive-inference system not only to recognize already-learned concepts, but also to increase the efficiently of execution of inferred programs.
Hexoses are simple sugars that play a key role in many cellular pathways, and in the regulation of development and disease mechanisms. Current protein-sugar computational models are based, at least partially, on prior...
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ISBN:
(纸本)9783642138393
Hexoses are simple sugars that play a key role in many cellular pathways, and in the regulation of development and disease mechanisms. Current protein-sugar computational models are based, at least partially, on prior biochemical findings and knowledge. they incorporate different parts of these findings in predictive black-box models. We investigate the empirical support for biochemical findings by comparing inductivelogicprogramming (ilp) induced rules to actual biochemical results. We mine the Protein Data Bank for a representative data set of hexose binding sites, non-hexose binding sites and surface grooves. We build an ilp model of hexose-binding sites and evaluate our results against several baseline machine learning classifiers. Our method achieves an accuracy similar to that of other black-box classifiers while providing insight into the discriminating process. In addition, it confirms wet-lab findings and reveals a previously unreported TRP-GLU amino acids dependency.
Research on natural language interfaces has mainly concentrated on question interpretation as well as answer computation, but not focused as Much on answer presentation. In most natural language interfaces, answers ar...
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Research on natural language interfaces has mainly concentrated on question interpretation as well as answer computation, but not focused as Much on answer presentation. In most natural language interfaces, answers are in fact provided extensionally as a list of all those instances satisfying the query description. In this paper, we aim to go beyond such a mere listing of facts and move towards producing additional descriptions of the query results referred to as intensional answers. We define an intensional answer (IA) as a logical description of the actual set of answer items to a given query in terms of properties that are shared by exactly these answer items. We argue that IAs can enhance a user's understanding of the answer itself but also of the underlying knowledge base. In particular, we present an approach for computing an intensional answer given an extensional answer (i.e. a set of entities) returned as a result of a question. In our approach, an intensional answer is represented by a clause and computed based on inductivelogicprogramming (ilp) techniques, in particular bottom-up clause generalization. the approach is evaluated in terms of usefulness and time performance, and we discuss its potential for helping to detect flaws in the knowledge base as well as to interactively enrich it with new knowledge. While the approach is used in the context of a natural language question answering system in our settings, it clearly has applications beyond, e.g. in the context of research on generating referring expressions. (C) 2009 Elsevier B.V. All rights reserved.
inductivelogicprogramming (ilp) is one of the main approaches to relational learning, withthe stronger expressive power and the ease of using background knowledge. However, compared withthe traditional attribute-v...
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inductivelogicprogramming (ilp) is an inductive reasoning method based on the first-order predicative logic. this technology is widely used for data mining using symbolic artificial intelligence. ilp searches for a ...
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ISBN:
(纸本)9783031291258;9783031291265
inductivelogicprogramming (ilp) is an inductive reasoning method based on the first-order predicative logic. this technology is widely used for data mining using symbolic artificial intelligence. ilp searches for a suitable hypothesis that covers positive examples and uncovers negative examples. the searching process requires a lot of execution cost to interpret many given examples for practical problems. In this paper, we propose a new hypothesis search method using particle swarm optimization (PSO). PSO is a meta-heuristic algorithm based on behaviors of particles. In our approach, each particle repeatedly moves from a hypothesis to another hypothesis within a hypothesis space. At that time, some hypotheses are refined based on the value returned by a predefined evaluation function. Since PSO just searches a part of the hypothesis space, it contributes to the speed up of the execution of ilp. In order to demonstrate the effectiveness of our method, we have implemented it on Progol that is one of the ilp systems [6], and then we conducted numerical experiments. the results showed that our method reduced the hypothesis search time compared to another conventional Progol.
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