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219 results for “Knowledge Graph”
Supplemental Material for Paper 'Taxonomy Extraction Using Knowledge Graph Embeddings and Hierarchical Clustering'
<p>Contains input data and gold standard for the non-expressive extraction task, as well as examples of extracted taxonomies for both the non-expressive and expressive cases. Extracted taxonomies can also be found at <a href="http://labowest.ca/sdb2020/">labowest.ca</a>.</p>
WheatGenomicsSLKG: Wheat Genomics Scientific Literature Knowledge Graph
<p>The <strong>Wheat Genomics Scientific Literature Knowledge Graph </strong>(<strong>WheatGenomicsSLKG</strong>) is a FAIR knowledge graph that exploits the Semantic Web technologies to integrate information about Named Entities (NE) extracted automatically from a corpus of PubMed scientific papers on wheat genetics and genomics.</p> <p>Code an details: https://github.com/Wimmics/WheatGenomicsKG</p>
Knowledge Graph Triple Validation by LLMs and Human-in-the-Loop
<p>Suplementary material for the sumbitted article to the IPM Special issue on<strong> </strong><em>Large Language Models and Data Quality for Knowledge Graphs. </em></p> <p>The dataset is an extension of [1] and includes the following columns:</p> <ul> <li><em>subj</em> the subject/head of the triple</li> <li><em>rel</em> the predicate of the triple</li> <li><em>obj</em> the object/tail of the triple</li> <li><em>support-level </em>indicating the reliability of the triple</li> <li><em>ann-random</em>[1: valid, 0: invalid], randomly selected annotation from the expert annotations avaialble in [1] </li> <li><em>ann-new</em> [1: valid, 0: invalid], junior expert annotation </li> <li><em>gpt-4o-1 </em>[1: valid, 0: invalid], response from 1st GPT prompt</li> <li><em>gpt-4o-2 </em>[1: valid, 0: invalid], response from 2nd GPT prompt</li> <li><em>gpt-4o-3</em> [1: valid, 0: invalid], response from 3rd GPT prompt</li> <li><em>gpt-4o-majority </em>[1: valid, 0: invalid], GPT annotation, computed as majority vote of gpt-4o-1,gpt-4o-2,gpt-4o-3</li> <li><em>claude-1 </em>[1: valid, 0: invalid], response from 1st <em>claude</em> prompt</li> <li><em>claude-2 </em>[1: valid, 0: invalid], response from 2nd <em>claude</em> prompt</li> <li><em>claude-3</em> [1: valid, 0: invalid], response from 3rd <em>claude</em> prompt</li> <li><em>claude-majority </em>[1: valid, 0: invalid], <em>claude</em> annotation, computed as majority vote of <em>claude</em>-1,<em>claude</em>-2,<em>claude</em>-3</li> <li><em>llama-1 </em>[1: valid, 0: invalid], response from 1st <em>llama</em> prompt</li> <li><em>llama-2 </em>[1: valid, 0: invalid], response from 2nd <em>llama</em> prompt</li> <li><em>llama-3</em> [1: valid, 0: invalid], response from 3rd <em>llama</em> prompt</li> <li><em>llama-majority </em>[1: valid, 0: invalid], <em>llama</em> annotation, computed as majority vote of <em>llama</em>-1,<em>llama</em>-2,<em>llama</em>-3</li> </ul> <p>[1] https://github.com/danilo-dessi/SKG-pipeline/tree/main/eval </p>
A Knowledge Graph of Distributed Ledger Technologies
<p>Distributed ledger systems have become more prominent and successful in recent years, with a major focus on blockchains and cryptocurrency especially. This has led to various misunderstandings about both the technology itself and its capabilities, as in many cases these two became synonymous and other applications are often overlooked. Therefore, as a whole, the view of distributed ledger technology beyond blockchains and cryptocurrencies is very limited. Existing vocabularies and ontologies often focus on single aspects of the technology, or in some cases even just on one product. This often leads to other types of distributed ledgers and their possible use cases being neglected.</p> <p>This dataset contains a knowledge graph built on an ontology for distributed ledger technologies, which includes concepts to model security aspects such as technical threats and vulnerabilities of distributed ledger systems, application domains, as well as relevant standards and regulations. A detailed documentation can be found here: <a href="https://w3id.org/DLTOntology">https://w3id.org/DLTOntology</a> </p>
Dataset - Clustering Semantic Predicates in the Open Research Knowledge Graph
<p>This dataset has been created for implementing a content-based recommender system in the context of the Open Research Knowledge Graph (ORKG). The recommender system accepts research paper's title and abstracts as input and recommends existing predicates in the ORKG semantically relevant to the given paper.</p> <p> </p> <p>The paper instances in the dataset are grouped by ORKG comparisons and therefore the <strong><em>data.json</em></strong> file is more comprehensive than <strong><em>training_set.json</em></strong> and <strong><em>test_set.json.</em></strong></p> <p> </p> <p><strong><em>data.json</em></strong></p> <p>The main JSON object consists of a list of comparisons. Each comparisons object has an ID, label, list of papers and list of predicates, whereas each paper object has ID, label, DOI, research field, research problems and abstract. Each predicate object has an ID and a label. See an example instance below.</p> <pre><code class="language-json">{ "comparisons": [ { "id": "R108331", "label": "Analysis of approaches based on required elements in way of modeling", "papers": [ { "id": "R108312", "label": "Rapid knowledge work visualization for organizations", "doi": "10.1108/13673270710762747", "research_field": { "id": "R134", "label": "Computer and Systems Architecture" }, "research_problems": [ { "id": "R108294", "label": "Enterprise engineering" } ], "abstract": "Purpose \u2013 The purpose of this contribution is to motivate a new, rapid approach to modeling knowledge work in organizational settings and to introduce a software tool that demonstrates the viability of the envisioned concept.Design/methodology/approach \u2013 Based on existing modeling structures, the KnowFlow toolset that aids knowledge analysts in rapidly conducting interviews and in conducting multi\u2010perspective analysis of organizational knowledge work is introduced.Findings \u2013 This article demonstrates how rapid knowledge work visualization can be conducted largely without human modelers by developing an interview structure that allows for self\u2010service interviews. Two application scenarios illustrate the pressing need for and the potentials of rapid knowledge work visualizations in organizational settings.Research limitations/implications \u2013 The efforts necessary for traditional modeling approaches in the area of knowledge management are often prohibitive. This contribution argues that future research needs ..." }, .... ], "predicates": [ { "id": "P37126", "label": "activities, behaviours, means [for knowledge development and/or for knowledge conveyance and transformation" }, { "id": "P36081", "label": "approach name" }, .... ] }, .... ] }</code></pre> <p> </p> <p><strong><em>training_set.json </em></strong>and<strong><em> test_set.json</em></strong></p> <p>The main JSON object consists of a list of training/test instances. Each instance has an instance_id with the format (comparison_id X paper_id) and a text. The text is a concatenation of the paper's label (title) and abstract. See an example instance below.</p> <p>Note that test instances are not duplicated and do not occur in the training set. Training instances are also not duplicated, BUT training papers can be duplicated in a concatenation with different comparisons.</p> <pre><code class="language-json">{ "instances": [ { "instance_id": "R108331xR108301", "comparison_id": "R108331", "paper_id": "R108301", "text": "A notation for Knowledge-Intensive Processes Business process modeling has become essential for managing organizational knowledge artifacts. However, this is not an easy task, especially when it comes to the so-called Knowledge-Intensive Processes (KIPs). A KIP comprises activities based on acquisition, sharing, storage, and (re)use of knowledge, as well as collaboration among participants, so that the amount of value added to the organization depends on process agents' knowledge. The previously developed Knowledge Intensive Process Ontology (KIPO) structures all the concepts (and relationships among them) to make a KIP explicit. Nevertheless, KIPO does not include a graphical notation, which is crucial for KIP stakeholders to reach a common understanding about it. This paper proposes the Knowledge Intensive Process Notation (KIPN), a notation for building knowledge-intensive processes graphical models." }, ... ] }</code></pre> <p> </p> <p><strong>Dataset Statistics:</strong></p> <table align="center"> <thead> <tr> <th scope="col">-</th> <th scope="col">Papers</th> <th scope="col">Predicates</th> <th scope="col">Research Fields</th> <th scope="col">Research Problems</th> </tr> </thead> <tbody> <tr> <td>Min/Comparison</td> <td>2</td> <td>2</td> <td>1</td> <td>0</td> </tr> <tr> <td>Max/Comparison</td> <td>202</td> <td>112</td> <td>5</td> <td>23</td> </tr> <tr> <td>Avg./Comparison</td> <td>21,54</td> <td>12,79</td> <td>1,20</td> <td>1,09</td> </tr> <tr> <td>Total</td> <td>4060</td> <td>1816</td> <td>46</td> <td>178</td> </tr> </tbody> </table> <p><strong>Dataset Splits:</strong></p> <table align="center"> <thead> <tr> <th scope="col">-</th> <th scope="col">Papers</th> <th scope="col">Comparisons</th> </tr> </thead> <tbody> <tr> <td>Training Set</td> <td>2857</td> <td>214</td> </tr> <tr> <td>Test Set</td> <td>1203</td> <td>180</td> </tr> </tbody> </table> <p> </p>
Intelligence Task Ontology and Knowledge Graph (ITO)
<p>The Intelligence Task Ontology and Knowledge Graph (ITO) provides a comprehensive, curated model of artificial intelligence tasks, benchmarks and benchmark results, including the biomedical domain.</p>
MEMEX_KG: Knowledge Graphs about the cities of Lisbon, Barcelona and Paris
<p>Knowledge Graphs about the cities of Lisbon, Barcelona and Paris. The datasets are used in the following paper: </p> <p>Mohamed, Hebatallah A., Sebastiano Vascon, Feliks Hibraj, Stuart James, Diego Pilutti, Alessio Del Bue, and Marcello Pelillo. "Geolocation of Cultural Heritage using Multi-View Knowledge Graph Embedding." <em>arXiv preprint arXiv:2209.03638</em> (2022).</p>
Data for The "Effect of Semantic Knowledge Graph Richness on Embedding Based Recommender Systems"
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Knowledge Graph Neural Network with Spatial-Aware Capsule for Drug-Drug Interaction Prediction
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Anomaly Detection on Dynamic Knowledge Graphs
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Bottom-up anytime discovery of generalised multimodal graph patterns for knowledge graphs
<p>These resources were generated during the research performed for a research paper with title "Bottom-up anytime discovery of generalised multimodal graph patterns for knowledge graphs", submitted in 2024. The research was performed by Xander Wilcke (first author), Rick Mourits, Auke Rijpma, and Richard Zijdeman, and was funded by the NWO CLARIAH-PLUS.</p> <p>File list</p> <p>This is the dataset that was used as input to our experiments. It contains the civil records of 100,000 Dutch citizen in RDF format (gzipped N-Triples), and was randomly sampled from the nationwide civil registry. All individuals in this dataset have been deceased for at least 100 years.</p> <p> - dataset_sample-100000.nt.gz</p> <p>This data was generated during the experiments. In other words, these are the (unfiltered) patterns that were founds during our research, and which are thus present in the 100,000 people sample that was used as input. The patterns are encoded as RDF (N-Triples) with provenance and metadata. </p> <p> - socchist-100K_output.tar.xz</p> <p>This file contains the results from the survey (cleaned) held during the evaluation of our research, and lists the Likert scores and free-form responses from the participants of the survey. The responses have been anonymised.</p> <p> - survey_results_240713_cleaned.csv</p>
LadiesDebating_HTO: A Knowledge Graph for representing the "Edinburgh Ladies' Debating Society Digital Collection" (1865 - 1880) following Heritage Textual Ontology
<p>This Knowlege Graph represents the information of the "Edinburgh Ladies’ Debating Society<strong>"</strong> (years: 1865 - 1880) collection in RDF (ttl format). This collection consists of the complete runs of two Edinburgh journals, <strong>‘The Attempt’ (10 volumes, 1865-74)</strong> and its successor ‘<strong>The Ladies’ Edinburgh Magazine’ (6 volumes, 1875-80)</strong>. These publications were produced by a leading Edinburgh women’s club, known during the period as the Edinburgh Essay Society or the Ladies’ Edinburgh Essay Society, but subsequently as the Ladies’ Edinburgh Debating Society. The Society existed from 1865 to 1935. The raw dataset is provided by the NLS in this <a href="https://data.nls.uk/data/digitised-collections/edinburgh-ladies-debating-society/">link</a>. As other NLS data collections, they are originally provided using two XMLs schemas: METS for descriptive, structural, technical and administrative metadata (Title, Author, Publisher, etc); and ALTO for encoding the OCR text of a page.</p> <p>In this work, we have extracted the information from METS and ALTO XMLS using <a href="https://github.com/francesNLP/defoe">defoe</a> tool. The KG uses the <a href="https://w3id.org/hto">HTO</a> to represent the information extracted. Furthermore, during the information extraction phase, we have employed several techniques to mitigate two common OCR errors: long-S and the line-break hyphenation.</p>
Station-B Biological Knowledge Graph Data
<p>This dataset contains all the experimental data and metadata collected as part of the Station-B project at Microsoft Research Cambridge. The data has been structured using the Biological Knowledge Graph Schema and was stored in <a href="https://docs.microsoft.com/en-us/azure/storage/tables/">Azure Tables</a> and <a href="https://docs.microsoft.com/en-us/azure/storage/blobs/">Azure Blobs</a>. This data includes two files:</p> <ul> <li>blobs.zip: This zipped file primarily contains blobs that stored raw and processed fluorescence data from the Microplate Reader at the Station-B wet lab. This zip also contains bundles compatible with the <a href="https://www.synthace.com/platform/">Synthace Platform</a> to enable lab automation with Liquid handling robots. </li> <li>tables.zip: This zipped file contains all the data and metadata associated with the Assembly and Characterization experiments conducted at Station-B. Each CSV in this zipped file represents data stored in an Azure Table. The columns in each CSV are based on the Biological Knowledge Graph Schema. </li> </ul>
Telecommunication Networks as Knowledge Graph Research Datasets
<p><strong>Telecommunication Networks as Knowledge Graph Research Datasets.</strong></p> <p>All the datasets were created using the PC with following parameters: Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz/16.00 GB RAM 1TB SSD</p> <p>All the software which is used for the datasets creation is available here: https://github.com/kulikovia/TN_KG_research<strong> </strong></p> <p>The datasets and supplementary files description of follow:</p> <table> <tbody> <tr> <td> <p><strong>#</strong></p> </td> <td> <p><strong>File</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>1.</p> </td> <td> <p>Computational_complexity_v5_ (ENG).pdf</p> </td> <td> <p>SPARQL performance tests report</p> </td> </tr> <tr> <td> <p>2.</p> </td> <td> <p>Synthesis_performance_tests_results_v1.pdf</p> </td> <td> <p>Inductive and deductive synthesis performance tests report</p> </td> </tr> <tr> <td>3.</td> <td>Computational_complexity_Parallel_v2_ (ENG).pdf</td> <td>Comparision of SPARQL performanse using multi-level KG structure approach and execution using distributed RDF storege </td> </tr> <tr> <td>4.</td> <td>Synthesis_additional_experiments_v4.pdf</td> <td>Additional Inductive and deductive synthesis performance tests report (with different elements distribution by levels)</td> </tr> <tr> <td> <p>5.</p> </td> <td> <p>Dataset_10k_hierarchical.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 10k hierarchical model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>6.</p> </td> <td> <p>Dataset_10k_one-level.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 10k one-level model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>7.</p> </td> <td> <p>Dataset_1k_hierarchical.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 1k hierarchical model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>8.</p> </td> <td> <p>Dataset_1k_one-level.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 1k one-level model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>9.</p> </td> <td> <p>Dataset_200k_hierarchical.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 200k hierarchical model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>10.</p> </td> <td> <p>Dataset_200k_one-level.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 200k one-level model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>11.</p> </td> <td> <p>Dataset_500k_hierarchical.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 500k hierarchical model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>12.</p> </td> <td> <p>Dataset_500k_one-level.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 500k one-level model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>13.</p> </td> <td> <p>Synthesis_Hierarchical_RDF-XML.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: RDF/XML datasets for hierarchical models (1k, 10k, 200k, 500k)</p> </td> </tr> <tr> <td> <p>14.</p> </td> <td> <p>Synthesis_Linear_RDF-XML.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: RDF/XML datasets for hierarchical models (1k, 10k, 200k, 500k)</p> </td> </tr> <tr> <td> <p>15.</p> </td> <td> <p>Hierarchy_model_results_Exp_10M.zip</p> </td> <td> <p>SPARQL performance tests: Datasets for 10M, 3-5-levels, exponential distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> <tr> <td>16.</td> <td>Hierarchy_model_results_Exp_15M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 15M, 3-5-levels, exponential distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>17.</td> <td>Hierarchy_model_results_Exp_200k.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 200k, 3-5-levels, exponential distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>18.</td> <td>Hierarchy_model_results_Linear_10M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 10M, 3-5-levels, linear distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>19.</td> <td>Hierarchy_model_results_Linear_15M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 15M, 3-5-levels, linear distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>20.</td> <td>Hierarchy_model_results_Linear_200k.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 200k, 3-5-levels, linear distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>21.</td> <td>Hierarchy_model_results_Quadro_10M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 10M, 3-5-levels, quadratic distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>22.</td> <td>Hierarchy_model_results_Quadro_15M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 15M, 3-5-levels, quadratic distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>23.</td> <td>Hierarchy_model_results_Quadro_200k.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 200k, 3-5-levels, quadratic distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>24.</td> <td>Hierarchy_model_results_Uniform_10M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 10M, 3-5-levels, uniform distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>25.</td> <td>Hierarchy_model_results_Uniform_15M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 15M, 3-5-levels, uniform distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>26.</td> <td>Hierarchy_model_results_Uniform_200k.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 200k, 3-5-levels, uniform distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td> <p>27.</p> </td> <td> <p>Linear_model_resuts.zip</p> </td> <td> <p>SPARQL performance tests: Datasets for 200k, 10M, 15M, one-level model in RDF/XML format</p> </td> </tr> </tbody> </table>
The Brill Knowledge Graph: A Database of Bibliographic References and Index Terms extracted from Books in Humanities and Social Sciences
<p>We present a complete dataset of linked bibliography and index data, partially disambiguated and augmented with references to external resources, extracted from the Brill’s archive in the field of Classics. Processed book identifiers are listed in a separate text file. Text fragments extracted from different books via this process are then parsed and compared using a string-based similarity metric to form clusters of bibliographic references to the same published work or (variants of) the same subjects discussed in these books. The entire set of references was then disambiguated using Google Books and Crossref APIs.</p> <p><a href="https://jdmdh.episciences.org/11062">Paper about extraction pipeline</a></p> <p><a href="https://www.nkokash.com/documents/KIEM-RDJ.pdf">Paper about extracted KG</a></p> <p> </p>
Embeddings of KG-COVID-19 knowledge graph (Aug 12 build), produced using node2vec, skipgram model, p=q=1, walk length = 100, num walks = 20
<p>Embeddings of KG-COVID-19 knowledge graph (Aug 12 build), produced using Embiggen, node2vec, skipgram model, p=q=1, walk length = 100, num walks = 20</p>
Embeddings of KG-COVID-19 knowledge graph (Aug 12 build), 80/20 training/test split, produced using node2vec, skipgram model, p=q=1, walk length = 100, num walks = 20
<p>KG-COVID-19 embedding data from Sep 8, 2020 experiment, for training/test split of 80/20: </p> <p>These embeddings and weights were produced from this notebook on or around Sep 8, 2020:</p> <p>https://github.com/justaddcoffee/kg_covid_19_drug_analyses/blob/master/Graph%20embedding%20using%20SkipGram%20homogeneous%20graph.ipynb</p> <p>SkipGram_80_20_training_test_epoch_500_delta_0.0001_embedding.npy<br> SkipGram_80_20_training_test_epoch_500_delta_0.0001_weights.h5</p> <p>I'm also including two runs just before this, with different epoch number and delta values:</p> <p>SkipGram_80_20_training_test_embedding_sep_6_2020_epoch_200_delta_0.001.npy</p> <p>SkipGram_80_20_training_test_weights_sep_6_2020_epoch_200_delta_0.001.h5</p> <p>SkipGram_80_20_training_test_embedding_sep_7_2020_epoch_200_delta_0.0001.npy<br> SkipGram_80_20_training_test_weights_sep_7_2020_epoch_200_delta_0.0001.h5</p>
GeoVectors - Knowledge Graph (v1.0)
<p>The GeoVectors corpus is a comprehensive large-scale linked open corpus of OpenStreetMap (https://www.openstreetmap.org/) entity embeddings that provides latent representations of over 980 million entities. The GeoVectors capture the semantic and geographic dimensions of OpenStreetMap entities and make them directly accessible to machine learning applications. The "-tags" datasets provide embeddings that capture the semantic dimension of OpenStreetMap entities. The "-location" datasets provide the geographic dimension.</p> <p>This dataset was derived from an OpenStreetMap snapshot that was taken on November 10, 2020 (© OpenStreetMap contributors).</p> <p>This repository contains the GeoVectors Knowledge graph that models metadata of the embeddings and links to well-established sources such as Wikidata and DBpedia. The GeoVectors corpus is partitioned into regional subsets. The GeoVectors knowledge graph can be used to identify the subset that contains a particular linked entity.</p> <p>For further information, please visit <a href="http://geovectors.l3s.uni-hannover.de">http://geovectors.l3s.uni-hannover.de</a></p> <p>GeoVectors consists of the following subsets:</p> <p><strong>Africa</strong></p> <ul> <li>Africa. Tags: <a href="https://doi.org/10.5281/zenodo.4320881">10.5281/zenodo.4320881</a>. Location: <a href="https://doi.org/10.5281/zenodo.4956827">10.5281/zenodo.4956827</a>.</li> </ul> <p><strong>Antarctica</strong></p> <ul> <li>Antarctica. Tags: <a href="https://doi.org/10.5281/zenodo.4320869">10.5281/zenodo.4320869</a>. Location: <a href="https://doi.org/10.5281/zenodo.4956951">10.5281/zenodo.4956951</a>.</li> </ul> <p><strong>Asia</strong></p> <ul> <li>Asia. Tags: <a href="https://doi.org/10.5281/zenodo.4320895">10.5281/zenodo.4320895</a>. Location: <a href="https://doi.org/10.5281/zenodo.4956955">10.5281/zenodo.4956955</a>.</li> <li>Japan. Tags: <a href="https://doi.org/10.5281/zenodo.4320895">10.5281/zenodo.4320895</a>. Location: <a href="https://doi.org/10.5281/zenodo.4957846">10.5281/zenodo.4957846</a>.</li> <li>Indonesia. Tags: <a href="https://doi.org/10.5281/zenodo.4320895">10.5281/zenodo.4320895</a>. Location: <a href="https://doi.org/10.5281/zenodo.4957818">10.5281/zenodo.4957818</a>.</li> </ul> <p><strong>Australia-Oceania</strong></p> <ul> <li>Australia-Oceania. Tags: <a href="https://doi.org/10.5281/zenodo.4320963">10.5281/zenodo.4320963</a>. Location: <a href="https://doi.org/10.5281/zenodo.4957176">10.5281/zenodo.4957176</a>.</li> </ul> <p><strong>Central-America</strong></p> <ul> <li>Central-America. Tags: <a href="https://doi.org/10.5281/zenodo.4321010">10.5281/zenodo.4321010</a>. Location: <a href="https://doi.org/10.5281/zenodo.4957278">10.5281/zenodo.4957278</a>.</li> </ul> <p><strong>Europe</strong></p> <ul> <li>Europe-east. Tags: <a href="https://doi.org/10.5281/zenodo.4321012">10.5281/zenodo.4321012</a>. Location: <a href="https://doi.org/10.5281/zenodo.4957475">10.5281/zenodo.4957475</a>.</li> <li>Europe-west. Tags: <a href="https://doi.org/10.5281/zenodo.4321099">10.5281/zenodo.4321099</a>. Location: <a href="https://doi.org/10.5281/zenodo.4957583">10.5281/zenodo.4957583</a>.</li> <li>France. Tags: <a href="https://doi.org/10.5281/zenodo.4321153">10.5281/zenodo.4321153</a>. Location: <a href="https://doi.org/10.5281/zenodo.4957689">10.5281/zenodo.4957689</a>.</li> <li>Germany-nodes-relations. Tags: <a href="https://doi.org/10.5281/zenodo.4321406">10.5281/zenodo.4321406</a>. Location: <a href="https://doi.org/10.5281/zenodo.4957746">10.5281/zenodo.4957746</a>.</li> <li>Germany-ways. Tags: <a href="https://doi.org/10.5281/zenodo.4321420">10.5281/zenodo.4321420</a>. Location: <a href="https://doi.org/10.5281/zenodo.4957746">10.5281/zenodo.4957746</a>.</li> <li>Great-Britain. Tags: <a href="https://doi.org/10.5281/zenodo.4321175">10.5281/zenodo.4321175</a>. Location: <a href="https://doi.org/10.5281/zenodo.4957805">10.5281/zenodo.4957805</a>.</li> <li>Italy. Tags: <a href="https://doi.org/10.5281/zenodo.4321206">10.5281/zenodo.4321206</a>. Location: <a href="https://doi.org/10.5281/zenodo.4957840">10.5281/zenodo.4957840</a>.</li> <li>Netherlands. Tags: <a href="https://doi.org/10.5281/zenodo.4321252">10.5281/zenodo.4321252</a>. Location: <a href="https://doi.org/10.5281/zenodo.4957583">10.5281/zenodo.4957583</a>.</li> <li>Poland. Tags: <a href="https://doi.org/10.5281/zenodo.4321267">10.5281/zenodo.4321267</a>. Location: <a href="https://doi.org/10.5281/zenodo.4957475">10.5281/zenodo.4957475</a>.</li> <li>Russia. Tags: <a href="https://doi.org/10.5281/zenodo.4321358">10.5281/zenodo.4321358</a>. Location: <a href="https://doi.org/10.5281/zenodo.4957903">10.5281/zenodo.4957903</a>.</li> </ul> <p><strong>North-America</strong></p> <ul> <li>North-America. Tags: <a href="https://doi.org/10.5281/zenodo.4321449">10.5281/zenodo.4321449</a>. Location: <a href="https://doi.org/10.5281/zenodo.4957873">10.5281/zenodo.4957873</a>.</li> <li>US-Other. Tags: <a href="https://doi.org/10.5281/zenodo.4321762">10.5281/zenodo.4321762</a>. Location: <a href="https://doi.org/10.5281/zenodo.4957931">10.5281/zenodo.4957931</a>.</li> <li>US-South. Tags: <a href="https://doi.org/10.5281/zenodo.4321641">10.5281/zenodo.4321641</a>. Location: <a href="https://doi.org/10.5281/zenodo.4957968">10.5281/zenodo.4957968</a>.</li> <li>US-West. Tags: <a href="https://doi.org/10.5281/zenodo.4321708">10.5281/zenodo.4321708</a>. Location: <a href="https://doi.org/10.5281/zenodo.4957931">10.5281/zenodo.4957931</a>.</li> </ul> <p><strong>South-America</strong></p> <ul> <li>South-America. Tags: <a href="https://doi.org/10.5281/zenodo.4321635">10.5281/zenodo.4321635</a>. Location: <a href="https://doi.org/10.5281/zenodo.4957911">10.5281/zenodo.4957911</a>.</li> </ul> <p><strong>Funding</strong>:</p> <p>This work was partially funded by DFG, German Research Foundation (“WorldKG", DE 2299/2-1), the Federal Ministry of Education and Research (BMBF), Germany (“Simple-ML", 01IS18054), the Federal Ministry for Economic Affairs and Energy (BMWi), Germany (“d-E-mand", 01ME19009B), and the European Commission (EU H2020, “smashHit", grant-ID 871477).</p>
OKG: A Knowledge Graph for Fine-grained Understanding of Social Media Discourse on Inequality
<p>The Observatory Knowledge Graph (OKG) is a knowledge graph with tweets on inequality in terms of the OBIO ontology (https://w3id.org/okg/obio-ontology/), which integrates social media metadata with various types of linguistic knowledge. The OKG can be used as the backbone of a social media observatory, to facilitate a deeper understanding of social media discourse on inequality.</p> <p>We retrieved tweets and retweets published from the end (30th) of May 2020 to the beginning (1st) of May 2023.</p> <p>In this version of the OKG, we use a sample of 85,247 tweets, published from May 30th to August 27th, 2020. To be compliant with Twitter's policies, we remove usernames and id's, as well as the tweet texts and sentences. We also replace user IRIs with skolem IRIs through skolemization. </p> <p>Access to the OKG as well as the SPARQL endpoint can be requested by sending a mail to the contact person (l.stork@uva.nl) with the following information: </p> <ol> <li>A description of the use case </li> <li>Affiliation of the researchers involved</li> <li>How their work is in line with Twitter's policies: https://developer.twitter.com/en/developer-terms/policy#4-d</li> </ol>
Full Data Tables: MeGNN-Join: Predicting Table Joinability in Data Lakes using a Metadata Knowledge Graph
<p>This is the anonymous upload for the full tabular datasets associated with the paper submission "MeGNN-Join: Predicting Table Joinability in Data Lakes using a Metadata Knowledge Graph".</p> <p>The full data is not strictly necessary for reproduction for our results - however, they would be necessary if one was interested in creating a whole new set of table joinability ground-truth data to evaluate our methods over. </p> <p>Please note that the full data is fairly large when unzipped (~30-40gb).</p>
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Allen Brain Atlas
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OpenNeuro
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