the proceedings contain 28 papers. the topics discussed include: probabilistic relational learning and inductive logicprogramming at a global scale;practical probabilistic programming;learning multi-class theories in...
ISBN:
(纸本)9783642212949
the proceedings contain 28 papers. the topics discussed include: probabilistic relational learning and inductive logicprogramming at a global scale;practical probabilistic programming;learning multi-class theories in ILP;a numerical refinement operator based on multi-instance learning;not far away from home: a relational distance-based approach to understanding images of houses;approximate inference for logic programs with annotated disjunctions;approximate Bayesian computation for the parameters of PRISM programs;probabilistic rule learning;interactive discriminative mining of chemical fragments;MMRF for proteome annotation applied to human protein disease prediction;multivariate prediction for learning on the semantic web;hypothesizing about causal networks with positive and negative effects by meta-level abduction;BET: an inductive logicprogramming workbench;and seeing the world through homomorphism: an experimental study on reducibility of examples.
In this paper we describe a logicprogramming based implementation of the OWL 2 RL fragment. We show how goals are used for querying, forward reasoning permits to infer new knowledge, and ontology inconsistency is han...
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Rules are definitely among the main kinds of knowledge representation in Artificial Intelligence. In recent years, there has been much discussion about production rules and logicprogramming to understand whether the ...
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the proceedings contain 9 papers. the special focus in this conference is on Functional and (Constraint) logicprogramming. the topics include: A new compiler from curry to Haskell;new functional logic design patterns...
ISBN:
(纸本)9783642225307
the proceedings contain 9 papers. the special focus in this conference is on Functional and (Constraint) logicprogramming. the topics include: A new compiler from curry to Haskell;new functional logic design patterns;xquery in the functional-logic language toy;size invariant and ranking function synthesis in a functional language;memoizing a monadic mixin DSL;a functional approach to worst-case execution time analysis;building a faceted browser in couchdb using views on views and erlang metaprogramming;combining object-oriented and logicprogramming;on proving termination of constrained term rewrite systems by eliminating edges from dependency graphs.
Coalgebra may be used to provide semantics for SLD-derivations, both finite and infinite. We first give such semantics to classical SLD-derivations, proving results such as adequacy, soundness and completeness. then, ...
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logic Programs with Annotated Disjunctions (LPADs) are a promising language for Probabilistic Inductive logicprogramming. In order to develop efficient learning systems for LPADs, it is fundamental to have high-perfo...
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ISBN:
(纸本)9783642212949;9783642212956
logic Programs with Annotated Disjunctions (LPADs) are a promising language for Probabilistic Inductive logicprogramming. In order to develop efficient learning systems for LPADs, it is fundamental to have high-performing inference algorithms. the existing approaches take too long or fail for large problems. In this paper we adapt to LPAD the approaches for approximate inference that have been developed for ProbLog, namely k-best and Monte Carlo. k-Best finds a lower bound of the probability of a query by identifying the k most probable explanations while Monte Carlo estimates the probability by smartly sampling the space of programs. the two techniques have been implemented in the cplint suite and have been tested on real and artificial datasets representing graphs. the results show that both algorithms are able to solve larger problems often in less time than the exact algorithm.
In this paper we investigate the lack of reliability and consistency of those binary rule learners in ILP that employ the one-vs-rest binarisation technique when dealing with multi-class domains. We show that we can l...
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ISBN:
(纸本)9783642212949;9783642212956
In this paper we investigate the lack of reliability and consistency of those binary rule learners in ILP that employ the one-vs-rest binarisation technique when dealing with multi-class domains. We show that we can learn a simple, consistent and reliable multi-class theory by combining the rules of the multiple one-vs-rest theories into one rule list or set. We experimentally show that our proposed methods produce coherent and accurate rule models from the rules learned by a well known ILP learner Aleph.
Many SRL models pose logical inference as weighted satisfiability solving. Performing logical inference after completely grounding clauses with all possible constants is computationally expensive and approaches such a...
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ISBN:
(纸本)9783642212949;9783642212956
Many SRL models pose logical inference as weighted satisfiability solving. Performing logical inference after completely grounding clauses with all possible constants is computationally expensive and approaches such as LazySAT [8] utilize the sparseness of the domain to deal withthis. Here, we investigate the efficiency of restricting the Knowledge Base (S) to the set of first order horn clauses. We propose an algorithm that prunes the search space for satisfiability in horn clauses and prove that the optimal solution is guaranteed to exist in the pruned space. the approach finds a model, if it exists, in polynomial time;otherwise it finds an interpretation that is most likely given the weights. We provide experimental evidence that our approach reduces the size of search space substantially.
Existing ILP (Inductive logicprogramming) systems are implemented in different languages namely C, Progol, etc. Also, each system has its customized format for the input data. this makes it very tedious and time cons...
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ISBN:
(纸本)9783642212949;9783642212956
Existing ILP (Inductive logicprogramming) systems are implemented in different languages namely C, Progol, etc. Also, each system has its customized format for the input data. this makes it very tedious and time consuming on the part of a user to utilize such a system for experimental purposes as it demands a thorough understanding of that system and its input specification. In the spirit of Weka [1], we present a relational learning workbench called BET(Background + Examples = theories), implemented in Java. the objective of BET is to shorten the learning curve of users (including novices) and to facilitate speedy development of new relational learning systems as well as quick integration of existing ILP systems. the standardized input format makes it easier to experiment with different relational learning algorithms on a common dataset.
Building on advances in statistical-relational AI and the Semantic Web, this talk outlined how to create knowledge, how to evaluate knowledge that has been published, and how to go beyond the sum of human knowledge. I...
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ISBN:
(纸本)9783642212949;9783642212956
Building on advances in statistical-relational AI and the Semantic Web, this talk outlined how to create knowledge, how to evaluate knowledge that has been published, and how to go beyond the sum of human knowledge. If there is some claim of truth, it is reasonable to ask what evidence there is for that claim, and to not believe claims that do not provide evidence. thus we need to publish data that can provide evidence. Given such data, we can also learn from it. this talk outlines how publishing ontologies, data, and probabilistic hypotheses/theories can let us base beliefs on evidence, and how the resulting world-wide mind can go beyond the aggregation of human knowledge. Much of the world's data is relational, and we want to make probabilistic predictions in order to make rational decisions. thus probabilistic relational learning and inductive logicprogramming need to be a foundation of the semantic web. this talk overviewed the technology behind this vision and the considerable technical and social problem that remain.
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