Kernel Methods for Mining Instance Data in Ontologies

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Modeling Relational Data with Graph Convolutional Networks (2017, methodology): “We remove relations that were used to create entity labels: employs and afﬁl-iation for AIFB, isMutagenic for MUTAG, hasLithogenesis for BGS, and objectCategory and material for AM.”
A Collection of Benchmark Datasets for Systematic Evaluations of Machine Learning on the Semantic Web (2016, methodology): “In [1] the dataset was first used to predict the affiliation (i.”
RDF2Vec: RDF Graph Embeddings for Data Mining (2016, methodology): “In [1], the dataset was first used to predict”
Factorizing YAGO: scalable machine learning for linked data (2012, background): “In the context of the Semantic Web, Inductive Logic Programming (ILP) and kernel learning have been the dominant Machine Learning approaches so far [7, 9, 11].”
Substructure counting graph kernels for machine learning from RDF data (2015, methodology): “Affiliation Prediction Our first classification experiment uses the AIFB dataset, used for the first time in [17] and repeated in following papers.”