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14 results for “rdf2vec”
DBpedia RDF2Vec Graph Embeddings
<p>DBpedia graph embeddings using RDF2Vec. RDF2Vec embedding generation code can be found <a href="https://github.com/dwslab/jRDF2Vec">here</a> and is based on a publication by Portisch et al. [1].</p> <p>The embeddings dataset consists of 200-dimensional vectors of DBpedia entities (from 1/9/2021).</p> <p>Figure of cosine similarities between a selected set of DBpedia entities are provided in the dataset <a href="https://zenodo.org/record/6384728/files/heatmap.pdf?download=1">here</a>.</p> <p> </p> <p><strong>Generating Embeddings</strong></p> <p>The code for generating these embeddings can be found <a href="https://github.com/EDAO-Project/DBpediaEmbedding">here</a>.</p> <p>Run the run.sh script that wraps all the necessary commmands to generate embeddings</p> <pre><code class="language-bash">bash run.sh</code></pre> <p>The script downloads a set of DBpedia files, which are listed in <code>dbpedia_files.txt</code>. It then builds a Docker image and runs a container of that image that generates the embeddings for the DBpedia graph defined by the DBpedia files.</p> <p>A folder <code>files</code> is created containing all the downloaded DBpedia files, and a folder <code>embeddings/dbpedia</code> is created containing the embeddings in <code>vectors.txt</code> along a set of random walk files.</p> <p> </p> <p><strong>Run Time of Embeddings Generation</strong></p> <p>Generating embeddings can take more than a day, but it depends on the number of DBpedia files chosen to be downloaded. Following are some basic run time statistics when embeddings are generated on a 64 GB RAM, 8 cores (AMD EPYC), 1 TB SSD, 1996.221 MHz machine.</p> <ul> <li><strong>Total</strong>: 1 day, 8 hours, 52 minutes, 41 seconds</li> <li><strong>Walk generation</strong>: 0 days, 7 minutes, 24 minutes, 36 seconds</li> <li><strong>Training</strong>: 1 day, 1 hour, 28 minutes, 5 seconds</li> </ul> <p> </p> <p><strong>Parameters Used</strong></p> <p>Here is listed the parameters used to generate the embeddings provided here:</p> <ul> <li><strong>Number of walks per entity</strong>: 100</li> <li><strong>Depth (hops) per walk</strong>: 4</li> <li><strong>Walk generation mode</strong>: RANDOM_WALKS_DUPLICATE_FREE</li> <li><strong>Threads</strong>: # of processors / 2</li> <li><strong>Training mode</strong>: sg</li> <li><strong>Embeddings vector dimension</strong>: 200</li> <li><strong>Minimum word2vec word count</strong>: 1</li> <li><strong>Sample rate</strong>: 0.0</li> <li><strong>Training window size</strong>: 5</li> <li><strong>Training epochs</strong>: 5</li> </ul>
RDF2Vec DBpedia uniform embeddings in HDF5 file format
<p>This dataset contains the vectors from computing RDF2vec embeddings from a uniformly weighted DBpedia 2016-04 graph.</p> <p>The file has a group called "Vectors" which contains a dataset for each entity in the graph. The dataset name is the entity name and the dataset content is the embedded vector (length 200).</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Biased graph walks for RDF graph embeddings. In <em>Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics</em> (WIMS '17). ACM, New York, NY, USA, Article 21, 12 pages. DOI: https://doi.org/10.1145/3102254.3102279</p>
RDF2Vec DBpedia uniform embeddings
<p>This dataset contains the vectors from computing RDF2vec embeddings from a uniformly weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Biased graph walks for RDF graph embeddings. In <em>Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics</em> (WIMS '17). ACM, New York, NY, USA, Article 21, 12 pages. DOI: https://doi.org/10.1145/3102254.3102279</p>
RDF2Vec DBpedia Page Rank split embeddings
<p>This dataset contains the vectors from computing RDF2vec embeddings from a Page Rank split weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Biased graph walks for RDF graph embeddings. In Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics (WIMS '17). ACM, New York, NY, USA, Article 21, 12 pages. DOI: https://doi.org/10.1145/3102254.3102279</p>
RDF2Vec DBpedia object frequency embeddings
<p>This dataset contains the vectors from computing RDF2vec embeddings from a object frequency weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Biased graph walks for RDF graph embeddings. In Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics (WIMS '17). ACM, New York, NY, USA, Article 21, 12 pages. DOI: https://doi.org/10.1145/3102254.3102279</p>
RDF2Vec DBpedia predicate frequency embeddings
<p>This dataset contains the vectors from computing RDF2vec embeddings from a predicate frequency weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Biased graph walks for RDF graph embeddings. In Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics (WIMS '17). ACM, New York, NY, USA, Article 21, 12 pages. DOI: https://doi.org/10.1145/3102254.3102279</p>
RDF2Vec DBpedia inverse predicate object frequency embeddings
<p>This dataset contains the vectors from computing RDF2vec embeddings from a inverse predicate object frequency weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Biased graph walks for RDF graph embeddings. In Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics (WIMS '17). ACM, New York, NY, USA, Article 21, 12 pages. DOI: https://doi.org/10.1145/3102254.3102279</p>
RDF2Vec DBpedia inverse object frequency embeddings
<p>This dataset contains the vectors from computing rdf2vec embeddings from a inverse object frequency weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Biased graph walks for RDF graph embeddings. In Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics (WIMS '17). ACM, New York, NY, USA, Article 21, 12 pages. DOI: https://doi.org/10.1145/3102254.3102279</p>
RDF2Vec DBpedia Page Rank embeddings
<p>This dataset contains the vectors from computing RDF2vec embeddings from a Page Rank weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Biased graph walks for RDF graph embeddings. In Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics (WIMS '17). ACM, New York, NY, USA, Article 21, 12 pages. DOI: https://doi.org/10.1145/3102254.3102279<br> </p>
RDF2Vec DBpedia inverse predicate frequency embeddings
<p>This dataset contains the vectors from computing RDF2vec embeddings from a inverse predicate frequency weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Biased graph walks for RDF graph embeddings. In Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics (WIMS '17). ACM, New York, NY, USA, Article 21, 12 pages. DOI: https://doi.org/10.1145/3102254.3102279</p>
RDF2Vec DBpedia inverse page rank split embeddings
<p>This dataset contains the vectors from computing RDF2vec embeddings from a inverse page rank split weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Biased graph walks for RDF graph embeddings. In Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics (WIMS '17). ACM, New York, NY, USA, Article 21, 12 pages. DOI: https://doi.org/10.1145/3102254.3102279</p>
RDF2Vec DBpedia predicate object frequency embeddings
<p>This dataset contains the vectors from computing RDF2vec embeddings from a predicate object frequency weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Biased graph walks for RDF graph embeddings. In Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics (WIMS '17). ACM, New York, NY, USA, Article 21, 12 pages. DOI: https://doi.org/10.1145/3102254.3102279</p>
RDF2Vec DBpedia inverse Page Rank frequency embeddings
<p>This dataset contains the vectors from computing RDF2vec embeddings from a inverse Page Rank frequency weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Biased graph walks for RDF graph embeddings. In Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics (WIMS '17). ACM, New York, NY, USA, Article 21, 12 pages. DOI: https://doi.org/10.1145/3102254.3102279</p>
Evaluation Framework tests on RDF2Vec
<p>The ZIP file contains the results of the tests run through the evaluation framework available at https://git.rwth-aachen.de/KGEmbedding/evaluationFramework executed on vectors produce by RDF2Vec combined with 11 different weighting techniques, described in <br> <br> Cochez, M., Ristoski, P., Ponzetto, S.P., Paulheim, H.:<br> Biased graph walks for RDF graph embeddings.<br> In: Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics (2017).<br> <br> The framework tested the vectors upon Machine Learning tasks - classification, regression, and clustering - and semantic tasks - document modeling, semantic analogies, and entity relatedness. <br> <br> At the top level, there is a summary of the results, detailed in the inner folder. </p>
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