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8 results for “semtab”
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>
SemTab 2024: Semantic Web Challenge on Tabular Data to Knowledge Graph Matching Data Sets - WikidataTables2024R1 and WikidataTables2024R2
<p>Data Sets from the ISWC 2024 Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, Round 1, Wikidata Tables. Links to other datasets can be found on the challenge website: https://sem-tab-challenge.github.io/2024/ as well as the proceedings of the challenge published on CEUR.</p> <p>For details about the challenge, see: http://www.cs.ox.ac.uk/isg/challenges/sem-tab/</p> <p>For 2024 edition, see: https://sem-tab-challenge.github.io/2024/</p> <p>Note on License: This data includes data from the following sources. Refer to each source for license details:<br>- Wikidata https://www.wikidata.org/</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>
SemTab 2021 BioTable Dataset
<p>This dataset was developed in the context of <a href="https://www.cs.ox.ac.uk/isg/challenges/sem-tab/2021/index.html">SemTab 2021: Semantic Web Challenge on Tabular Data to Knowledge Graph Matching</a>. It was used to test the participant systems in the Bio-Track in Round 2. Here you can find not only the files for annotation that were available during the competition but also the ground truths used to evaluate the performance of the system.</p>
SemTab 24: Semantic Table Annotations Benchmark for LLM-based approaches
<p><strong>SuperSemtab24 </strong>is a dataset for tabular data to knowledge graph matching.</p> <p>The dataset is divided into training and validation sets. The dataset includes general-purpose tables and intentionally misspelled entities to evaluate the model's robustness. Participants must annotate the entity mentions in the validation set and submit their annotations (following a target file).</p> <p>The repository contains the full version of the dataset; the ground truth (GT) of the test set will be uploaded in the future.</p>
GitTables dataset for SemTab 2022
<p><strong>Note: the entire GitTables corpus is <a href="https://zenodo.org/record/6517052">here</a>. Visit <a href="https://gittables.github.io/">https://gittables.github.io</a> for more background and contact details.</strong></p> <p> </p> <p>This dataset represents a <strong>subset</strong> of tables from <a href="https://gittables.github.io/">GitTables</a> curated for benchmarking column type detection methods in round 3 of <a href="https://sem-tab-challenge.github.io/2022/">SemTab 2022</a>.</p> <p>This dataset consists of the following files:</p> <ul> <li>“GitTables_SemTab_2022_dataset.zip”: tables from GitTables used in Round 3 from SemTab 2022. Filenames correspond to table IDs, column names are replaced with "col_0", "col_1", etc. which match to the targets and labels (semantic types) as provided on the <a href="https://sem-tab-challenge.github.io/2022/#round3">main website</a>.</li> <li>"dbpedia_property_gt.csv": groundtruth DBpedia labels for the target columns.</li> <li>"dbpedia_property_targets.csv": column annotation targets for DBpedia.</li> <li>"schema_property_gt.csv": groundtruth Schema.org labels for the target columns.</li> <li>"schema_property_targets.csv": column annotation targets for Schema.org.</li> <li>"schema_class_gt.csv": groundtruth Schema.org labels for the target columns.</li> <li>"schema_class_targets.csv": column annotation targets for Schema.org.</li> </ul>
SOTAB V2 for SemTab 2023
<p>SOTAB V2 for SemTab 2023 includes datasets used to evaluate <a href="https://paperswithcode.com/task/column-type-annotation">Column Type Annotation </a>(CTA) and <a href="https://paperswithcode.com/task/columns-property-annotation">Columns Property Annotation</a> (CPA) systems in the 2023 edition of the <a href="https://www.cs.ox.ac.uk/isg/challenges/sem-tab/">SemTab challenge</a>. The datasets for both rounds of the challenge were down-sampled from the full train, test and validation splits of the <a href="http://webdatacommons.org/structureddata/sotab/v2/">SOTAB V2</a> (WDC Schema.org Table Annotation Benchmark version 2) benchmark, so that the datasets of the first round have a smaller vocabulary of 40 and 50 labels for CTA and CPA respectively corresponding to easier/more general domains, and the datasets of the second round include the full vocabulary size of 80 and 105 labels and are therefore considered to be harder to annotate. The columns and the relationships between columns are annotated using the Schema.org and DBpedia vocabulary.</p> <p>SOTAB V2 for SemTab 2023 contains the splits used in Round 1 and Round 2 of the challenge. Each round includes a training, validation and test split together with the ground truth for the test splits and the vocabulary list. The ground truth of the test sets of both rounds are manually verified.</p> <p>Files contained in SOTAB V2 for SemTab 2023:</p> <ol> <li>Round1-SOTAB-CPA-DatasetsAndGroundTruth = This file contains the csv files of the first round of the challenge for the task of CPA for training, validation and test set, as well as label set and ground truth in the "gt" folder. The columns in these files are annotated with Schema.org.</li> <li>Round1-SOTAB-CTA-DatasetsAndGroundTruth = This file contains the csv files of the first round of the challenge for the task of CTA for training, validation and test set, as well as label set and ground truth in the "gt" folder. The columns in these files are annotated with Schema.org.</li> <li>Round2-SOTAB-CPA-SCH-DatasetsAndGroundTruth = This file contains the csv files of the second round of the challenge for the task of CPA for training, validation and test set, as well as label set and ground truth in the "gt" folder. The columns in these files are annotated with Schema.org.</li> <li>Round2-SOTAB-CTA-SCH-DatasetsAndGroundTruth = This file contains the csv files of the second round of the challenge for the task of CTA for training, validation and test set, as well as label set and ground truth in the "gt" folder. The columns in these files are annotated with Schema.org.</li> <li>Round2-SOTAB-CPA-DBP-DatasetsAndGroundTruth = This file contains the csv files of the second round of the challenge for the task of CPA for training, validation and test set, as well as label set and ground truth in the "gt" folder. The columns in these files are annotated with DBpedia.</li> <li>Round2-SOTAB-CTA-DBP-DatasetsAndGroundTruth = This file contains the csv files of the second round of the challenge for the task of CTA for training, validation and test set, as well as label set and ground truth in the "gt" folder. The columns in these files are annotated with DBpedia.</li> </ol> <p>All the corresponding tables can be found in the "Tables" zip folders.</p> <p> </p> <p>Note on License: This data includes data from the following sources. Refer to each source for license details:</p> <p>- CommonCrawl <a href="https://commoncrawl.org/">https://commoncrawl.org/</a></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>
SemTab 2024 - Table Metadata to Knowledge Graph Track Datasets
<p>Data Sets from the ISWC 2024 Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, Table Metadata to Knowledge Graph Track.</p> <p>See round1/README.md and round2/README.md for details on how to use the dataset.</p> <p>Links to other datasets can be found on the challenge website: https://sem-tab-challenge.github.io/2024/ as well as the proceedings of the challenge published on CEUR.</p> <p>For details about the challenge, see: http://www.cs.ox.ac.uk/isg/challenges/sem-tab/</p> <p>For 2024 edition, see: https://sem-tab-challenge.github.io/2024/</p> <p>Note on License: This data includes data from the following sources. Refer to each source for license details:<br>- Wikidata https://www.wikidata.org/<br>- DBpedia https://www.dbpedia.org/<br>- Kaggle https://www.kaggle.com</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>
Mammotab 24 (SemTab)
<p>MammoTab is a dataset designed to evaluate semantic table annotation approaches.</p> <p>It includes annotations about cell/mentions to Knowledge Graph (KG) entity matching (CEA task).</p> <p>It is composed of 2500 (2000 for training and 500 for testing) tables extracted from 21149260 Wikipedia pages and annotated through Wikidata v. 20220708. The dataset is compliant with the data format used in SemTab</p>
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