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3 results for “Entity Typing”
Types, open citations, closed citations, publishers, and participation reports of Crossref entities
<p>This publication contains several datasets that have been used in the paper "Crowdsourcing open citations with CROCI – An analysis of the current status of open citations, and a proposal" submitted to the <a href="https://www.issi2019.org/">17th International Conference on Scientometrics and Bibliometrics (ISSI 2019)</a>, available at <a href="https://opencitations.wordpress.com/2019/02/07/crowdsourcing-open-citations-with-croci/">https://opencitations.wordpress.com/2019/02/07/crowdsourcing-open-citations-with-croci/</a>.</p> <p>Additional information about the analyses described in the paper, including the code and the data we have used to compute all the figures, is available as a Jupyter notebook at <a href="https://github.com/sosgang/pushing-open-citations-issi2019/blob/master/script/croci_nb.ipynb">https://github.com/sosgang/pushing-open-citations-issi2019/blob/master/script/croci_nb.ipynb</a>. The datasets contain the following information.</p> <p><strong>non_open.zip:</strong> it is a zipped (~5 GB unzipped) CSV file containing the numbers of open citations and closed citations received by the entities in the Crossref dump used in our computation, dated October 2018. All the entity types retrieved from Crossref were aligned to one of following five categories: journal, book, proceedings, dataset, other. The open CC0 citation data we used came from the CSV dump of <a href="https://doi.org/10.6084/m9.figshare.6741422.v3">most recent release of COCI dated 12 November 2018</a>. The number of closed citations was calculated by subtracting the number of open citations to each entity available within COCI from the value “is-referenced-by-count” available in the Crossref metadata for that particular cited entity, which reports all the DOI-to-DOI citation links that point to the cited entity from within the whole Crossref database (including those present in the Crossref ‘closed’ dataset).</p> <p>The columns of the CSV file are the following ones:</p> <ul> <li><em>doi:</em> the DOI of the publication in Crossref;</li> <li><em>type:</em> the type of the publication as indicated in Crossref;</li> <li><em>cited_by:</em> the number of open citations received by the publication according to COCI;</li> <li><em>non_open:</em> the number of closed citations received by the publication according to Crossref + COCI.</li> </ul> <p><strong>croci_types.csv:</strong> it is a CSV file that contains the numbers of open citations and closed citations received by the entities in the Crossref dump used in our computation, as collected in the previous CSV file, alligned in five classes depening on the entity types retrieved from Crossref: <em>journal</em> (Crossref types: journal-article, journal-issue, journal-volume, journal), <em>book</em> (Crossref types: book, book-chapter, book-section, monograph, book track, book-part, book-set, reference-book, dissertation, book series, edited book), <em>proceedings</em> (Crossref types: proceedings-article, proceedings, proceedings-series), <em>dataset</em> (Crossref types: dataset), <em>other</em> (Crossref types: other, report, peer review, reference-entry, component, report-series, standard, posted-content, standard-series).</p> <p>The columns of the CSV file are the following ones:</p> <ul> <li><em>type:</em> the type publication between "journal", "book", "proceedings", "dataset", "other";</li> <li><em>label:</em> the label assigned to the type for visualisation purposes;</li> <li><em>coci_open_cit</em>: the number of open citations received by the publication type according to COCI;</li> <li><em>crossref_close_cit:</em> the number of closed citations received by the publication according to Crossref + COCI.</li> </ul> <p><strong>publishers_cits.csv:</strong> it is a CSV file that contains the top twenty publishers that received the greatest number of open citations. The columns of the CSV file are the following ones:</p> <ul> <li><em>publisher</em>: the name of the publisher;</li> <li><em>doi_prefix</em>: the list of DOI prefixes used assigned by the publisher;</li> <li><em>coci_open_cit</em>: the number of open citations received by the publications of the publisher according to COCI;</li> <li><em>crossref_close_cit</em>: the number of closed citations received by the publications of the publishers according to Crossref + COCI;</li> <li><em>total_cit</em>: the total number of citations received by the publications of the publisher (= <em>coci_open_cit</em> + <em>crossref_close_cit</em>).</li> </ul> <p><strong>20publishers_cr.csv: </strong>it is a CSV file that contains the numbers of the contributions to open citations made by the twenty publishers introduced in the previous CSV file as of 24 January 2018, according to the data available through the Crossref API. The counts listed in this file refers to the number of publications for which each publisher has submitted metadata to Crossref that include the publication’s reference list. The categories 'closed', 'limited' and 'open' refer to publications for which the reference lists are not visible to anyone outside the Crossref Cited-by membership, are visible only to them and to Crossref Metadata Plus members, or are visible to all, respectively. In addition, the file also record the total number of publications for which the publisher has submitted metadata to Crossref, whether or not those metadata include the reference lists of those publications.</p> <p>The columns of the CSV file are the following ones:</p> <ul> <li><em>publisher: </em>the name of the publisher;</li> <li><em>open: </em>the number of publications in Crossref with an 'open' visibility for their reference lists;</li> <li><em>limited: </em>the number of publications in Crossref with an 'limited' visibility for their reference lists;</li> <li><em>closed: </em>the number of publications in Crossref with an 'closed' visibility for their reference lists;</li> <li><em>overall_deposited:</em> the overall number of publications for which the publisher has submitted metadata to Crossref.</li> </ul>
Entity Typing Datasets
<p>These are the datasets used in the Entity Type Prediction task for Knowledge Graph Completion.</p> <ul> <li><strong>DB630k_Fine-grained_Hierarchical.zip</strong> dataset has been used in the papers [1] and [2]. It is an extended version of DBpedia630k dataset originally created for Text classification and is available <a href="http://shorturl.at/oDPW6">here</a>.</li> <li><strong>FIGER.zip</strong> dataset has also been used in the papers [1] and [2].</li> <li><strong>MultilingualETdata.zip</strong> dataset has been used in the paper [3]</li> <li><strong>NamesETdata.zip</strong> dataset has been used in the paper [4]. The CaLiGraph test dataset can also be downloaded <a href="https://data.dws.informatik.uni-mannheim.de/CaLiGraph/whats-in-a-name/">here</a>.</li> </ul> <p> </p> <p>[1] Biswas R, Sofronova R, Sack H, Alam M. Cat2type: Wikipedia category embeddings for entity typing in knowledge graphs. InProceedings of the 11th on Knowledge Capture Conference 2021 Dec 2 (pp. 81-88).</p> <p>[2] Biswas R, Portisch J, Paulheim H, Sack H, Alam M. Entity type prediction leveraging graph walks and entity descriptions. In The Semantic Web–ISWC 2022: 21st International Semantic Web Conference, Virtual Event, October 23–27, 2022, Proceedings 2022 Oct 16 (pp. 392-410). Cham: Springer International Publishing.</p> <p>[3] Biswas R, Chen Y, Paulheim H, Sack H, Alam M. It’s All in the Name: Entity Typing Using Multilingual Language Models. In The Semantic Web: ESWC 2022 Satellite Events: Hersonissos, Crete, Greece, May 29–June 2, 2022, Proceedings 2022 Jul 20 (pp. 36-41). Cham: Springer International Publishing.</p> <p>[4] Biswas R, Sofronova R, Alam M, Heist N, Paulheim H, Sack H. Do judge an entity by its name! entity typing using language models. In The Semantic Web: ESWC 2021 Satellite Events: Virtual Event, June 6–10, 2021, Revised Selected Papers 18 2021 (pp. 65-70). Springer International Publishing.</p> <p> </p> <p> </p> <p> </p>
Data for "RegulaTome: a corpus of typed, directed, and signed relations between biomedical entities in the scientific literature"
<div> <p><strong>RegulaTome corpus</strong>: this <a href="../api/records/10808330/files/RegulaTome-corpus.tar.gz/content" target="_blank" rel="noopener">file</a> contains the RegulaTome corpus in <a href="https://brat.nlplab.org/">BRAT</a> format. The directory <strong>"splits" </strong>has the corpus split based on the train/dev/test used for the training of the relation extraction system</p> <p><strong>RegulaTome annodoc</strong>: The annotation guidelines along with the annotation configuration files for BRAT are provided in <a href="../api/records/10808330/files/annodoc+config.tar.gz/content" target="_blank" rel="noopener">annodoc+config.tar.gz</a>. The online version of the annotation documentation can be found here: <a href="https://katnastou.github.io/s1000-corpus-annotation-guidelines/">https://katnastou.github.io/regulatome-annodoc/ </a></p> <p>The tagger software can be found here: <a href="https://github.com/larsjuhljensen/tagger">https://github.com/larsjuhljensen/tagger</a>. The command used to run tagger before large-scale execution of the RE system is:</p> <p><code>gzip -cd `ls -1 pmc/*.en.merged.filtered.tsv.gz` `ls -1r pubmed/*.tsv.gz` | cat dictionary/excluded_documents.txt - | tagger/tagcorpus --threads=16 --autodetect --types=dictionary/curated_types.tsv --entities=dictionary/all_entities.tsv --names=dictionary/all_names_textmining.tsv --groups=dictionary/all_groups.tsv --stopwords=dictionary/all_global.tsv --local-stopwords=dictionary/all_local.tsv --type-pairs=dictionary/all_type_pairs.tsv --out-matches=all_matches.tsv</code></p> <p><strong>Input documents </strong>for large-scale execution, which is done on entire <a href="https://a3s.fi/March-2024-PubMed/PubMed_20230314.tar.gz" target="_blank" rel="noopener">PubMed</a> (as of March 2024) and <a href="https://a3s.fi/Jan-2024-documents/PMC_Nov_23.tar.gz" target="_blank" rel="noopener">PMC Open Access</a> (as of November 2023) articles in BioC format. The files are converted to a <a href="https://a3s.fi/March-2024-PubMed/all_documents.tsv" target="_blank" rel="noopener">tab-delimited format </a>to be compatible with the RE system input (see below).</p> <p><strong>Input dictionary files</strong>: all the files necessary to execute the command above are available in <a href="../api/records/10808330/files/tagger_dictionary_files.tar.gz/content" target="_blank" rel="noopener">tagger_dictionary_files.tar.gz </a></p> <p><strong>Tagger output</strong>: we filter the results of the tagger run down to gene/protein hits, and documents with more than 1 hit (since we are doing relation extraction) before feeding it to our RE system. The filtered output is available in <a href="../api/records/10808330/files/tagger_matches_ggp_only_gt_1_hit.tsv.gz/content" target="_blank" rel="noopener">tagger_matches_ggp_only_gt_1_hit.tsv.gz</a></p> <p><strong>Relation extraction system input</strong>: <a href="../api/records/10808330/files/combined_input_for_re.tar.gz/content" target="_blank" rel="noopener">combined_input_for_re.tar.gz</a>: these are the directories with all the .ann and .txt files used as input for the large-scale execution of the relation extraction pipeline. The files are generated from the tagger tsv output (see above, <a href="../api/records/10808330/files/tagger_matches_ggp_only_gt_1_hit.tsv.gz/content" target="_blank" rel="noopener">tagger_matches_ggp_only_gt_1_hit.tsv.gz</a>) using the <a href="https://github.com/spyysalo/string-db-tools/blob/main/tagger2standoff.py">tagger2standoff.py</a> script from the <a href="https://github.com/spyysalo/string-db-tools/">string-db-tools</a> repository.</p> <p><strong>Relation extraction models</strong>. The Transformer-based model used for large-scale relation extraction and prediction on the test set is at <a href="../api/records/10808330/files/relation_extraction_multi-label-best_model.tar.gz/content" target="_blank" rel="noopener">relation_extraction_multi-label-best_model.tar.gz</a></p> <p>The pre-trained RoBERTa model on PubMed and PMC and MIMIC-III with a BPE Vocab learned from PubMed (RoBERTa-large-PM-M3-Voc), which is used by our system is available <a href="https://github.com/facebookresearch/bio-lm/blob/main/README.md">here</a>.</p> <p><strong>Relation extraction system output</strong>: the tab-delimited outputs of the relation extraction system are found at <a href="https://a3s.fi/regulatome-ls/large_scale_relation_extraction_results.tar.gz" target="_blank" rel="noopener">large_scale_relation_extraction_results.tar.gz </a><strong>!!!ATTENTION this file is approximately 1TB in size, so make sure you have enough space to download it on your machine!!!</strong></p> <p>The relation extraction system output files have 86 columns: PMID, Entity BRAT ID1, Entity BRAT ID2, and scores per class produced by the relation extraction model. Each file has a header to denote which score is in which column.</p> </div>
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