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4 results for “Knowledge Graph Completion”
Datasets for Non-Parametric Class Completeness Estimators for Collaborative Knowledge Graphs
<p><strong>Non-Parametric Class Completeness Estimators for Collaborative Knowledge Graphs</strong></p> <p>This are intermediary datasets used for the calculation of the Class Completeness Estimators on Wikidata. For more information see: https://github.com/eXascaleInfolab/cardinal/</p> <p><strong>edits_wikidatawiki-20181001-pages.csv</strong></p> <p>This is an extract from <em>wikidatawiki-20181001-pages-meta-history</em> (All pages with complete page edit history (.bz2)) found at <a href="https://dumps.wikimedia.org/wikidatawiki/">https://dumps.wikimedia.org/wikidatawiki/</a>.</p> <p>The extract was created by the following SQL query:</p> <pre> SELECT page_title, rev_comment, rev_user_text, rev_timestamp FROM revisions WHERE rev_comment LIKE '%[[Property:%]]%[[Q%' ORDER BY rev_id INTO OUTFILE 'edits_wikidatawiki-20181001-pages.csv'; </pre> <p> </p> <p><strong>wikidata-20180813-all.json.bz2.universe.noattr.gt.bz2</strong></p> <p>This is a graph-tool representation of the WikiData graph. Output of <a href="https://github.com/eXascaleInfolab/cardinal/blob/master/1_create_inmemory_graph.py">https://github.com/eXascaleInfolab/cardinal/blob/master/1_create_inmemory_graph.py</a>.</p> <p><strong>observations_wikidatawiki-20181001-pages.pickle</strong></p> <p>Extracted observations. Output of <a href="https://github.com/eXascaleInfolab/cardinal/blob/master/2_extract_observations.py">https://github.com/eXascaleInfolab/cardinal/blob/master/2_extract_observations.py</a>.</p> <p> </p> <p><strong>estimates_wikidatawiki-20181001-pages.pickle</strong></p> <p>Extracted estimates. Output of <a href="https://github.com/eXascaleInfolab/cardinal/blob/master/3_calculate_estimates.py">https://github.com/eXascaleInfolab/cardinal/blob/master/3_calculate_estimates.py</a></p> <p> </p> <p><strong>results_wikidatawiki-20181001-pages.pickle </strong></p> <p>Results. Output of <a href="https://github.com/eXascaleInfolab/cardinal/blob/master/4_draw_graphs.py">https://github.com/eXascaleInfolab/cardinal/blob/master/4_draw_graphs.py</a></p>
CoDEx: A Comprehensive Knowledge Graph Completion Benchmark
<p>This repository hosts the <strong>relational-only part</strong> of the CoDEx benchmark, which was presented at the EMNLP 2020 conference. You can access the paper <a href="https://www.aclweb.org/anthology/2020.emnlp-main.669.pdf">here</a> and the full dataset, including text and pretrained models, <a href="https://bit.ly/2EPbrJs">on GitHub</a>.</p> <p>Abstract:</p> <p><em>We present CoDEx, a set of knowledge graph completion datasets extracted from Wikidata and Wikipedia that improve upon existing knowledge graph completion benchmarks in scope and level of difficulty. In terms of scope, CoDEx comprises three knowledge graphs varying in size and structure, multilingual descriptions of entities and relations, and tens of thousands of hard negative triples that are plausible but verified to be false. To characterize CoDEx, we contribute thorough empirical analyses and benchmarking experiments. First, we analyze each CoDEx dataset in terms of logical relation patterns. Next, we report baseline link prediction and triple classification results on CoDEx for five extensively tuned embedding models. Finally, we differentiate CoDEx from the popular FB15K-237 knowledge graph completion dataset by showing that CoDEx covers more diverse and interpretable content, and is a more difficult link prediction benchmark. Data, code, and pretrained models are available <a href="https://bit.ly/2EPbrJs">here</a>.</em></p>
DrugProt Complete PubMed Knowledge Graph
<p><strong>DrugProt Complete PubMed Knowledge Graph</strong></p><p><strong>Please cite if you use any DrugProt resource:</strong></p><p>Antonio Miranda-Escalada, Farrokh Mehryary, Jouni Luoma, Darryl Estrada-Zavala, Luis Gasco, Sampo Pyysalo, Alfonso Valencia, Martin Krallinger, Overview of DrugProt task at BioCreative VII: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical–protein relations, <i>Database</i>, Volume 2023, 2023, baad080</p><blockquote><p><i>@article{miranda2023overview, title={Overview of DrugProt task at BioCreative VII: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical--protein relations}, author={Miranda-Escalada, Antonio and Mehryary, Farrokh and Luoma, Jouni and Estrada-Zavala, Darryl and Gasco, Luis and Pyysalo, Sampo and Valencia, Alfonso and Krallinger, Martin}, journal={Database}, volume={2023}, pages={baad080}, year={2023}, publisher={Oxford University Press UK} }</i></p></blockquote><p><strong>Description</strong></p><p>This dataset contains a knowledge graph built from PubMed dump abstracts (December 2021). A NER system has been applied to each of the abstracts to extract mentions of type "CHEMICAL" and "GENE", as well as a RE system to detect existing relations between these mentions such as ACTIVATOR, INHIBITOR, AGONIST or PRODUCT_OF, among others (see article for a full list of relations considered).</p><p>Given the volume of the dataset, the repository is divided into 1114 folders. Each of these folders contains a chunk of PubMed abstracts, entities and relationships, divided into the following 3 files:</p><ul><li><i>abstracts.tsv</i>: Tabular file in which each line represents a pubmed document. The file has 3 columns:<ul><li>Pubmed_id: Numerical identifier of the document in PubMed</li><li>Title: Title of the document</li><li>Abstract: Abstract text.</li></ul></li><li>entities.tsv: List of the entities extracted from the abstracts. Each line represents an extracted entity, and has 5 columns:<ul><li>Pubmed_id: Numerical identifier of the document in PubMed</li><li>Mention_id: Numerical identifier of the mention in the document.</li><li>Entity_type: Type of mention. It can be CHEMICAL or GENE.</li><li>Span_ini: Index of the first character of the annotated span in the text</li><li>Span_end: Index of the first character after the annotated span.</li><li>Span: Text span of the annotation</li></ul></li><li>relations.tsv: File of existing relations between entities. Each line represents a relationship, and has the following fields:<ul><li>Pubmed_id: Numerical identifier of the document in PubMed</li><li>Relation_type: DrugProt relation type among entities/arguments.</li><li>Arg1: Mention of CHEMICAL</li><li>Arg2: Mention of GENE</li></ul></li></ul><p> </p><p><strong>Files:</strong></p><ul><li>drugprot-silver-standard-kg.zip : Folders with the files previously explained</li></ul><p> </p><p><strong>Related resources:</strong></p><ul><li><a href="https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-1/">Web</a></li><li><a href="https://doi.org/10.5281/zenodo.4955410">DrugProt corpus</a></li><li><a href="https://github.com/tonifuc3m/drugprot-evaluation-library">Evaluation library</a></li><li><a href="https://codalab.lisn.upsaclay.fr/competitions/8293">Online evaluation (CodaLab)</a></li><li><a href="https://doi.org/10.5281/zenodo.4957137">Relation annotation guidelines</a></li><li><a href="https://doi.org/10.5281/zenodo.4957576">Gene and protein annotation guidelines</a></li><li><a href="https://doi.org/10.5281/zenodo.4957518">Chemicals and drugs annotation guidelines</a></li><li><a href="https://doi.org/10.5281/zenodo.5042178">FAQ</a></li><li><a href="https://doi.org/10.5281/zenodo.5119878">DrugProt Large Scale Additional SubTrack</a></li><li><a href="https://doi.org/10.5281/zenodo.5656991">DrugProt Large Scale document collection protocol</a></li><li><a href="https://doi.org/10.5281/zenodo.8246229">DrugProt Complete PubMed Knowledge Graph</a><br> </li></ul>
Multilingual Knowledge Graph Completion With Joint Relation and Entity Alignment
<p>Code and data accompanying AlignKGC.</p>
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