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Titlebook: Inductive Logic Programming; 26th International C James Cussens,Alessandra Russo Conference proceedings 2017 Springer International Publish

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發(fā)表于 2025-3-25 03:54:40 | 只看該作者
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發(fā)表于 2025-3-25 09:12:57 | 只看該作者
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發(fā)表于 2025-3-25 12:34:46 | 只看該作者
Learning Through Advice-Seeking via Transfer,ernative domains, to guide the expert to give useful advice. This knowledge is captured in the form of first-order logic horn clauses. We demonstrate empirically the value of the transferred knowledge, as well as the contribution of the expert in providing initial knowledge, plus revising and directing the use of the transferred knowledge.
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發(fā)表于 2025-3-25 17:02:21 | 只看該作者
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發(fā)表于 2025-3-25 22:21:45 | 只看該作者
Towards Nonmonotonic Relational Learning from Knowledge Graphs,osed which, however, applies to a flattened representation of a KG with only unary facts. In this work we make the first steps towards extending this approach to KGs in their original relational form, and provide preliminary evaluation results on real-world KGs, which demonstrate the effectiveness of our method.
26#
發(fā)表于 2025-3-26 02:23:22 | 只看該作者
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發(fā)表于 2025-3-26 04:56:48 | 只看該作者
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發(fā)表于 2025-3-26 10:00:37 | 只看該作者
Online Structure Learning for Traffic Management, data, as well as synthetic data generated by a professional traffic micro-simulator. The experimental results demonstrate that . can effectively learn traffic congestion definitions and, in some cases, outperform rules constructed by human experts.
29#
發(fā)表于 2025-3-26 14:48:07 | 只看該作者
Learning Relational Dependency Networks for Relation Extraction,evaluate the different components in the benchmark KBP 2015 task and show that RDNs effectively model a diverse set of features and perform competitively with current state-of-the-art relation extraction methods.
30#
發(fā)表于 2025-3-26 20:14:03 | 只看該作者
0302-9743 learning; logical foundations; statistical relational learning; probabilistic ILP; implementation and scalability; applications in robotics, cyber security and games..978-3-319-63341-1978-3-319-63342-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
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