Substructure counting graph kernels for machine learning from RDF data-Reference-Cited by-同舟云学术

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Substructure counting graph kernels for machine learning from RDF data

Published:2015-12 Issue: Volume:35 Page:71-84
ISSN:1570-8268
Container-title:Journal of Web Semantics
language:en
Short-container-title:Journal of Web Semantics

Author:

de Vries Gerben Klaas Dirk,de Rooij Steven

Publisher

Elsevier BV

Subject

Computer Networks and Communications,Human-Computer Interaction,Software

Reference41 articles.

1. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond;Schölkopf,2001

2. Kernel Methods for Pattern Analysis;Shawe-Taylor,2004

3. Optimizing search engines using clickthrough data;Joachims,2002

4. Efficient algorithms for ranking with svms;Chapelle;Inform. Retrieval,2010

5. Weighted graph cuts without eigenvectors — a multilevel approach;Dhillon;IEEE Trans. Pattern Anal. Mach. Intell.,2007

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1. RHGNN: imposing relational inductive bias for heterogeneous graph neural network;International Journal of Machine Learning and Cybernetics;2024-08-10

2. Inductive autoencoder for efficiently compressing RDF graphs;Information Sciences;2024-03

3. Feature selection based on the complexity of structural patterns in RDF graphs;International Journal of Data Science and Analytics;2023-11-10

4. Benchmarking Whole Knowledge Graph Embedding Techniques;International Journal of Software Engineering and Knowledge Engineering;2023-10-04

5. pyRDF2Vec: A Python Implementation and Extension of RDF2Vec;The Semantic Web;2023

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