Titlebook: Inductive Logic Programming; 25th International C Katsumi Inoue,Hayato Ohwada,Akihiro Yamamoto Conference proceedings 2016 Springer Interna

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书目名称Inductive Logic Programming影响因子(影响力)




书目名称Inductive Logic Programming影响因子(影响力)学科排名




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书目名称Inductive Logic Programming被引频次学科排名




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书目名称Inductive Logic Programming年度引用学科排名




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书目名称Inductive Logic Programming读者反馈学科排名

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oncologist

Ontology Learning from Interpretations in Lightweight Description Logics,goal is to correctly identify the TBox given this input. We characterize the key constraints on the models that warrant finite learnability of TBoxes expressed in selected fragments of the Description Logic . and define corresponding learning algorithms.

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Constructing Markov Logic Networks from First-Order Default Rules,ethods for default reasoning. The resulting Markov logic networks could then be refined based on available training data. Our method thus offers a convenient way of using expert knowledge for constraining or guiding the process of learning Markov logic networks.

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An Exercise in Declarative Modeling for Relational Query Mining,icient solving, especially for what concerns subsumption testing. We propose such second order extensions and evaluate their potential effectiveness with a number of experiments in subsumption as well as in query mining.

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Using ILP to Identify Pathway Activation Patterns in Systems Biology,tion patterns in pathways previously implicated in the development of cancers. Our method identified a model with comparable predictive performance to the winning algorithm of a recent challenge, while providing biologically relevant explanations that may be useful to a biologist.

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kProbLog: An Algebraic Prolog for Kernel Programming,f state-of-the-art graph kernels such as Weisfeiler-Lehman graph kernels, propagation kernels and an instance of Graph Invariant Kernels (GIKs), a recent framework for graph kernels with continuous attributes. The number of feature extraction schemas, that we can compactly specify in kProbLog, shows its potential for machine learning applications.

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Conference proceedings 2016tional learning, relational data mining, statistical relational learning, multi-relational data mining, relational reinforcement learning, graph mining, connections with other learning paradigms, among others..