0000000000043626

AUTHOR

Simone Paolo Ponzetto

showing 4 related works from this author

Biased GraphWalks for RDF Graph Embeddings

2017

Knowledge Graphs have been recognized as a valuable source for background information in many data mining, information retrieval, natural language processing, and knowledge extraction tasks. However, obtaining a suitable feature vector representation from RDF graphs is a challenging task. In this paper, we extend the RDF2Vec approach, which leverages language modeling techniques for unsupervised feature extraction from sequences of entities. We generate sequences by exploiting local information from graph substructures, harvested by graph walks, and learn latent numerical representations of entities in RDF graphs. We extend the way we compute feature vector representations by comparing twel…

graph embeddingsyhdistetty avoin tietotiedonlouhintaavoin tieto
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Global RDF Vector Space Embeddings

2017

Vector space embeddings have been shown to perform well when using RDF data in data mining and machine learning tasks. Existing approaches, such as RDF2Vec, use local information, i.e., they rely on local sequences generated for nodes in the RDF graph. For word embeddings, global techniques, such as GloVe, have been proposed as an alternative. In this paper, we show how the idea of global embeddings can be transferred to RDF embeddings, and show that the results are competitive with traditional local techniques like RDF2Vec.

Theoretical computer scienceComputer science020204 information systems0202 electrical engineering electronic engineering information engineeringRdf graph020201 artificial intelligence & image processing02 engineering and technologycomputer.file_formatLinked dataRDFcomputerWord (computer architecture)Vector space
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Biased graph walks for RDF graph embeddings

2017

Knowledge Graphs have been recognized as a valuable source for background information in many data mining, information retrieval, natural language processing, and knowledge extraction tasks. However, obtaining a suitable feature vector representation from RDF graphs is a challenging task. In this paper, we extend the RDF2Vec approach, which leverages language modeling techniques for unsupervised feature extraction from sequences of entities. We generate sequences by exploiting local information from graph substructures, harvested by graph walks, and learn latent numerical representations of entities in RDF graphs. We extend the way we compute feature vector representations by comparing twel…

ta113graph embeddingsGraph kernelComputer scienceVoltage graphComparability graphdata mining02 engineering and technologycomputer.software_genre020204 information systemsyhdistetty avoin tietolinked open data0202 electrical engineering electronic engineering information engineeringTopological graph theoryGraph (abstract data type)020201 artificial intelligence & image processingData miningtiedonlouhintaGraph propertyNull graphLattice graphavoin tietocomputerProceedings of the 7th International Conference on Web Intelligence, Mining and Semantics
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Global RDF Vector Space Embeddings

2017

Vector space embeddings have been shown to perform well when using RDF data in data mining and machine learning tasks. Existing approaches, such as RDF2Vec, use local information, i.e., they rely on local sequences generated for nodes in the RDF graph. For word embeddings, global techniques, such as GloVe, have been proposed as an alternative. In this paper, we show how the idea of global embeddings can be transferred to RDF embeddings, and show that the results are competitive with traditional local techniques like RDF2Vec. peerReviewed

graph embeddingsyhdistetty avoin tietotiedonlouhintasemanttinen web
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