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43 results for “DBpedia”
Semantic Annotation for Tabular Data with DBpedia: Adapted SemTab 2019 with DBpedia 2016-10
<p>Semantic Annotation for Tabular Data with DBpedia: Adapted SemTab 2019 with DBpedia 2016-10</p> <p>Github: https://github.com/phucty/mtab4dbpedia<br> ---------------------------------------------------------------------------------------------------------------------------------------</p> <p>CEA: </p> <ul> <li> <p>Keep only valid entities in DBpedia 2016-10</p> </li> <li> <p>Resolve percentage encoding</p> </li> <li> <p>Add missing redirect entities</p> </li> </ul> <p>CTA: </p> <ul> <li> <p>Keep only valid types</p> </li> <li> <p>Resolve transitive types (parents and equivalent types of the specific type) with DBpedia ontology 2016-10</p> </li> </ul> <p>CPA:</p> <ul> <li> <p>Add equivalent properties</p> </li> </ul> <p>Statistic of Adapted Tabular data SemTab 2019</p> <pre><code>| | CEA | | | CPA | | | CTA | | | |---------|:--------:|:-------:|:------:|:--------:|:-------:|:------:|:--------:|---------|--------| | | Orginal | Adapted | Change | Orginal | Adapted | Change | Orginal | Adapted | Change | | Round 1 | 8418 | 8406 | -0.14% | 116 | 116 | 0.00% | 120 | 120 | 0.00% | | Round 2 | 463796 | 457567 | -1.34% | 6762 | 6762 | 0.00% | 14780 | 14333 | -3.02% | | Round 3 | 406827 | 406820 | 0.00% | 7575 | 7575 | 0.00% | 5762 | 5673 | -1.54% | | Round 4 | 107352 | 107351 | 0.00% | 2747 | 2747 | 0.00% | 1732 | 1717 | -0.87% |</code></pre> <p> </p> <p>---------------------------------------------------------------------------------------------------------------------------------------<br> DBpedia 2016-10 extra resources: (Original dataset http://downloads.dbpedia.org/2016-10/)</p> <p>---------------------------------------------------------------------------------------------------------------------------------------</p> <p>File: _dbpedia_classes_2016-10.csv</p> <p>Information: DBpedia classes and parents: (We remove the abstract types: Agent, Thing)</p> <p>Total: 759 classes</p> <p>Structure: [class, parents (separate with space)] (without prefix dbo: or http://dbpedia.org/ontology/)</p> <p>Example: "City","Location Place PopulatedPlace Settlement"</p> <p>---------------------------------------------------------------------------------------------------------------------------------------</p> <p>File: _dbpedia_properties_2016-10.csv</p> <p>Information: DBpedia properties and these equivalents</p> <p>Total: 2865 properties</p> <p>Structure: [property, it’s equivalent properties] (without prefix dbo: or http://dbpedia.org/ontology/)</p> <p>Example: "restingDate","deathDate"</p> <p>---------------------------------------------------------------------------------------------------------------------------------------</p> <p>File: _dbpedia_domains_2016-10.csv</p> <p>Information: DBpedia properties and these domain types</p> <p>Total: 2421 properties (have types as their domain)</p> <p>Structure: [property, type (domain)] (without prefix dbo: or http://dbpedia.org/ontology/)</p> <p>Example: "deathDate","Person"</p> <p>---------------------------------------------------------------------------------------------------------------------------------------</p> <p>File: _dbpedia_entities_2016-10.jsonl.bz2 </p> <p>Information: DBpedia entity dump</p> <p>Format: json list bz2 (bz2 Compressed json list)</p> <p>Source: DBpedia dump 2016-10 core</p> <p>Total: 5,289,577 entities (No disambiguation entities)</p> <p>Structure:</p> <p>An entity: for example “Tokyo”: (datatype: dictionary),</p> <p>{</p> <p>'wd': 'Q1322032', (Wikidata ID, datatype: string)</p> <p>'wp': 'Tokyo', (Wikipedia ID, add prefix <a href="https://en.wikipedia.org/wiki/">https://en.wikipedia.org/wiki/</a> + wp to get the Wikipedia URL, datatype: string)</p> <p>'dp': 'Tokyo', (DBpedia ID, add prefix <a href="http://dbpedia.org/resource/">http://dbpedia.org/resource/</a> + dp to get the DBpedia URL, datatype: string)</p> <p>'label': 'Tokyo', (Entity label, datatype: string)</p> <p>'aliases': ['To-kyo', 'Tôkyô Prefecture', ..], (Other entity names, datatype: list) </p> <p>'aliases_multilingual': ['东京小子', 'طوكيو', ...], (Other entity names in multilingual, datatype: list)</p> <p>'types_specific': 'City', (Entity direct type, datatype: string) </p> <p>'types_transitive': ['Human settlement', 'City', 'PopulatedPlace', 'Location', 'Place', 'Settlement'], (Entity transitive types, datatype: list)</p> <p>'claims_entity': { (entity statements, datatype: dictionary. Keys: properties, Values: list of tail entities)</p> <p>'governingBody': ['Tokyo Metropolitan Government'], </p> <p> 'subdivision': ['Honshu', 'Kantō region'],</p> <p>...</p> <p>},</p> <p>'claims_literal': {</p> <p>'string': { (String literal: datatype: dictionary. Keys: properties, Values: list of values</p> <p>'postalCode': ['JP-13'], </p> <p>'utcOffset': ['+09:00', '+9'],</p> <p>…</p> <p>}</p> <p>'time': { (Time literal: datatype: dictionary. Keys: properties, Values: list of date time</p> <p>'populationAsOf': ['2016-07-31'], </p> <p>...</p> <p>}), </p> <p>'quantity': { (Numerical literal: datatype: dictionary. Keys: properties, Values: list of values</p> <p>populationDesity: [6224.66, 6349.0], </p> <p>'maximumElevation': [2017], </p> <p>...</p> <p>},</p> <p>'pagerank': 2.2167366040153352e-06 (Entity page rank score calculated on DBpedia Graph)</p> <p>}</p> <p>---------------------------------------------------------------------------------------------------------------------------------------</p> <p>THIS DATA IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.</p>
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>
Dataset Sinetron Indonesia[DBPedia ID]
<p>Dataset berisi daftar Sinetron Indonesia yang diperoleh dari situs DBpedia menggunakan query yang dijalankan pada id.dbpedia.org/sparql. Data berisi lebih dari 300 baris dengan jumlah kolom sebanyak 3 kolom, yaitu kolom judul sinetron, sutradara, dan pemain sinetron.</p>
DBpedia Japanese
<p>Dataset for DBpedia Japanese</p>
Wikidata and DBpedia Space Travel Data Comparison with ABECTO
<p>This is an <a href="https://github.com/fusion-jena/abecto">ABECTO</a> execution plan to compare space travel data from <a href="https://www.wikidata.org">Wikidata</a> and <a href="https://www.dbpedia.org">DBpedia</a> and the according results.</p> <p>The generated result data are derived from the compared knowledge graphs, which are licensed as follows:</p> <ul> <li><a href="https://www.dbpedia.org">DBpedia</a> by DBpedia Association (<a href="http://en.wikipedia.org/wiki/Wikipedia:Text_of_Creative_Commons_Attribution-ShareAlike_3.0_Unported_License">CC BY-SA 3.0</a>)</li> <li><a href="https://wikidata.org">Wikidata</a> (<a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0 1.0</a>)</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>
Automatically Extracted SHACL Shapes for WikiData, DBpedia, YAGO-4, and LUBM & Associated Coverage Statistics
<p>The uploaded datasets contain <strong>automatically extracted </strong>SHACL shapes for the following datasets:</p> <ul> <li>WikiData (the truthy dump from September 2021 filtered by removing non-English strings) [1]</li> <li>DBpedia [2]</li> <li>YAGO-4 [3] </li> <li>LUBM (scale factor 500) [4]</li> </ul> <p>The validating shapes for these datasets are generated by a program that parses the corresponding RDF files (in `.nt` format). The extracted shapes encode various SHACL constraints, e.g., sh:minCount, sh:path, sh:class, sh:datatype etc. For each shape we encode coverage in terms of number of entities satisfying such shape, this information is encoded using the <a href="http://vocab.deri.ie/void#entities">void:entities</a> predicate. </p> <p>We have provided as executable Jar file the program we developed to extract these SHACL shapes.<br> More details about the datasets used to extract these shapes and <em>how to run the Jar</em> are available on our GitHub repository <a href="https://github.com/dkw-aau/qse">https://github.com/dkw-aau/qse</a>.</p> <p>Read more about our Quality Shapes Extraction (QSE) tool on our website <a href="https://relweb.cs.aau.dk/qse/">https://relweb.cs.aau.dk/qse/</a></p> <p>[1] Vrandečić, Denny, and Markus Krötzsch. "Wikidata: a free collaborative knowledgebase." Communications of the ACM 57.10 (2014): 78-85.</p> <p>[2] Auer, Sören, et al. "Dbpedia: A nucleus for a web of open data." The semantic web. Springer, Berlin, Heidelberg, 2007. 722-735.</p> <p>[3] Pellissier Tanon, Thomas, Gerhard Weikum, and Fabian Suchanek. "Yago 4: A reason-able knowledge base." European Semantic Web Conference. Springer, Cham, 2020.</p> <p>[4] Guo, Yuanbo, Zhengxiang Pan, and Jeff Heflin. "LUBM: A benchmark for OWL knowledge base systems." Journal of Web Semantics 3.2-3 (2005): 158-182.</p>
DBpedia Abstractive Summaries
<p>This dataset contains the abstractive summaries all DBpedia entities (dump September 2022, https://downloads.dbpedia.org/repo/dbpedia/text/long-abstracts/2022.09.01/) in English. In particular, we employed the pre-trained BART model for generating summaries of all entities based on their full DBpedia abstracts. Unlike the existing shortened abstracts, our dataset contains a comprehensive, concise summaries of all entities. </p> <p> </p>
CollabRec: DBpedia Subgraphs (2022-09)
<p>The core version of DBpedia has too many entities and statements to train recommendation models in a reasonable time frame, which is why we created two subsets (DB1M, and DBA240) of the core version of DBpedia from September 2022.</p> <p><strong>File structure</strong><br> Each dataset is located in their own folder with the following files:</p> <ul> <li><em>index.tsv.gz</em> is a file in tabular format that maps a simple integer to a URI, which identifies an entity in the KG.</li> <li><em>index_labels.tsv.gz</em> is a file that links entities (represented by their index number) to their label and description.</li> <li><em>relevant_entities.tsv.gz</em> is a file with all the entities, which occur as subject or/and as object in statements of the subsampled KG.</li> <li><em>statements.tsv.gz</em> is a file with all the statements of the subsampled KG. The first column contains the subjects, second column the predicates, and the third column the objects. All those entities are represented by their index number (see <em>index.tsv.gz</em>) and not their URI.</li> <li><em>statements.nt.gz</em> is a file with all the statements of the subsampled KG in N-Triples format.</li> </ul>
Results of SPARQL log summarisation on DBpedia logs from LSQ 2.0
<p>This dataset contains the results of applying a method called SPARQL log summarisation on the DBpedia logs from the <a href="http://lsq.aksw.org/">Linked SPARQL Queries (LSQ) 2.0 dataset</a>. This release is meant as a companion of a publication describing the method.</p> <p><strong>Description of the files</strong></p> <ul> <li><strong>datasetLabels_all.csv</strong> association between the identifiers used internally for the datasets and the corresponding labels and full names;</li> <li><strong>datasetLabels_forPaper.csv</strong> same as above, with some datasets omitted for brevity;</li> <li><strong>sparql-clustering-1.1.0.zip</strong> release 1.1.0 of the source code, available at <a href="https://github.com/miguel76/sparql-clustering">https://github.com/miguel76/sparql-clustering</a>;</li> <li><strong>templatesAsCSV.zip</strong> list of the templates found for each dataset, along with information and statistics;</li> <li><strong>templatesAsRDF.zip</strong> the templates found for each dataset, represented as RDF and linked to the RDF representation of LSQ queries.</li> </ul>
Semantic links between selected CSV datasets harvested by the European Data Portal and the DBpedia knowledge graph
<p>These dataset contains the results of the interlinking process between selected csv datasets harvested by the European DAta Portal and the DBpedia knowledge graph. </p> <p>We aim at answering the following questions:<br> What are the more popular column types? This will provide hindsight about what the datasets hold and how they can be joined. It will also provide hindsight on what specific linking schemes could be applied in future elements.<br> What datasets have columns of the same type? This will suggest datasets that may be similar or related.<br> What entities appear in most datasets (co-referent entities)? This will suggest entities for which more data is published.<br> What datasets share a particular entity? This will suggest datasets that may be joined, or are related through that particular entity</p> <p>Results are provided as augmented tables, that contain the columns of the original csv, plus a metadata file in JSON-LD format. The metadata files can be loaded in an RDF-store and queried.</p> <p>Refer to the accompanying report of activities for more details on the methodolog and how to query the dataset.</p> <p><br> </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>
KGloVe DBpedia inverse predicate frequency embeddings
<p>This dataset contains the vectors from computing KGloVe 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. Global RDF Vector Space Embeddings. In The Semantic Web – ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21–25, 2017, Proceedings, Part I.</p>
KGloVe DBpedia predicate object frequency embeddings
<p>This dataset contains the vectors from computing KGloVe 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. Global RDF Vector Space Embeddings. In The Semantic Web – ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21–25, 2017, Proceedings, Part I.</p>
KGloVe DBpedia uniform embeddings
<p>This dataset contains the vectors from computing KGloVe 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. Global RDF Vector Space Embeddings. In The Semantic Web – ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21–25, 2017, Proceedings, Part I.</p>
KGloVe DBpedia Page Rank frequency embeddings
<p>This dataset contains the vectors from computing KGloVe embeddings from a 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. Global RDF Vector Space Embeddings. In The Semantic Web – ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21–25, 2017, Proceedings, Part I.</p>
KGloVe DBpedia inverse predicate object frequency embeddings
<p>This dataset contains the vectors from computing KGloVe 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. Global RDF Vector Space Embeddings. In The Semantic Web – ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21–25, 2017, Proceedings, Part I.</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>
KGloVe DBpedia inverse object split embeddings
<p>This dataset contains the vectors from computing KGloVe embeddings from a inverse object 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. Global RDF Vector Space Embeddings. In The Semantic Web – ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21–25, 2017, Proceedings, Part I.</p>
KGloVe DBpedia inverse Page Rank split embeddings
<p>This dataset contains the vectors from computing KGloVe 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. Global RDF Vector Space Embeddings. In The Semantic Web – ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21–25, 2017, Proceedings, Part I.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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