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9 results for “Link graph”
Link-prediction on Biomedical Knowledge Graphs
<p>Release of code and experimental data from the paper <em>Towards Linking Graph Topology to Model Performance for Biomedical Knowledge Graph Completion </em>(<em>Machine Learning for Life and Material Sciences</em> workshop @ ICML2024) and <a href="https://arxiv.org/abs/2409.04103" rel="nofollow">The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models</a>.</p> <div> <div>Knowledge Graph Completion has been increasingly adopted as a useful method for several tasks in biomedical research, like drug repurposing or drug-target identification. To that end, a variety of datasets and Knowledge Graph Embedding models has been proposed over the years. However, little is known about the properties that render a dataset useful for a given task and, even though theoretical properties of Knowledge Graph Embedding models are well understood, their practical utility in this field remains controversial. We conduct a comprehensive investigation into the topological properties of publicly available biomedical Knowledge Graphs and establish links to the accuracy observed in real-world applications. By releasing all model predictions we invite the community to build upon our work and continue improving the understanding of these crucial applications.</div> <div> </div> <div>Experiments were conducted on six datasets: five from the biomedical domain (<a href="../records/268568">Hetionet</a>, <a href="https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/IXA7BM">PrimeKG</a>, <a href="../records/4077338">PharmKG</a>, <a href="../records/5361324">OpenBioLink2020 HQ</a>, <a href="../records/7011027">PharMeBINet</a>) and one trivia KG (<a href="https://aclanthology.org/W15-4007.pdf">FB15k-237</a>). All datasets were randomly split into training, validation and test set (80% / 10% / 10%; in the case of PharMeBINet, 99.3% / 0.35% / 0.35% to mitigate the increased inference cost on the larger dataset).</div> <div>On each dataset, five different KGE models were compared: <a href="https://dl.acm.org/doi/10.5555/2999792.2999923">TransE</a>, <a href="https://arxiv.org/abs/1412.6575">DistMult</a>, <a href="https://arxiv.org/abs/1902.10197">RotatE</a>, <a href="https://arxiv.org/abs/2209.08271">TripleRE</a>, <a href="https://dl.acm.org/doi/10.5555/3504035.3504256">ConvE</a>. Hyperparameters were tuned on the validation split (see final train configurations in <code>train/scripts</code>). We release results for tail predictions on the test split. In particular, each test query <code>(h,r,?)</code> is scored against all entities in the KG and we compute the rank of the score of the correct completion <code>(h,r,t)</code> , after masking out scores of other <code>(h,r,t')</code> triples contained in the graph.</div> <div>Note: the ranks provided are computed as the average between the optimistic and pessimistic ranks of triple scores.</div> <div> </div> <div>Inside <code>experimental_data.zip</code>, the following files are provided.</div> <div> <ul> <li><code>datasets/{dataset}</code>: a folder for each dataset, containing <ul> <li><code>{dataset}_preprocessing.ipynb</code>: a Jupyter notebook for downloading and preprocessing the datasets. In particular, this generates the custom label->ID mapping for entities and relations, and the numerical tensor of <code>(h_ID,r_ID,t_ID)</code> triples for all edges in the graph, which can be used to compute graph topological metrics (e.g., using <a href="https://github.com/graphcore-research/kg-topology-toolbox">kg-topology-toolbox</a>) and compare them with the edge prediction accuracy.</li> <li><code>test_ranks.csv</code>: csv table with columns <code>["h", "r", "t"]</code> specifying the head, relation, tail IDs of the test triples, and columns <code>["DistMult", "TransE", "RotatE", "TripleRE", "ConvE"]</code> with the rank of the ground-truth tail in the ordered list of predictions made by the five KGE models;</li> <li><code>entity_dict.csv</code>: list of entity labels, ordered by entity ID (as generated in the preprocessing notebook);</li> <li><code>relation_dict.csv</code>: list of relation labels, ordered by relation ID (as generated in the preprocessing notebook).</li> </ul> </li> <li><code>train</code>: code to reproduce training (and validation) of the five KGE models, using the <a href="https://github.com/graphcore-research/bess-kge">BESS-KGE</a> distribution framework. <ul> <li><code>train/scripts</code>: executable scripts, with specifications of the final hyperparameters for all models and datasets.</li> </ul> </li> <li><code>notebooks</code>: Jupyter notebooks for data analysis and generation of all the figures in the paper.</li> </ul> <p>The separate <code>top_100_tail_predictions.zip</code> archive contains, for each of the test queries in the corresponding <code>test_ranks.csv</code> table, the IDs of the top-100 tail predictions made by each of the five KGE models, ordered by decreasing likelihood. The predictions are released in a <code>.npz</code> archive of numpy arrays (one array of shape <code>(n_test_triples, 100)</code> for each of the KGE models). </p> </div> </div>
OpenAire Research Graph linked with OpenAlex
<p>This package contains linked datasets of OpenAire Research Graph and OpenAlex. </p> <p>Files descriptions:</p> <p>- author_to_publication_dic.json contains a mapping of authors to their publications</p> <p>- downloads_views_dic.json contains mappings of the publication id to the number of its downloads and views</p> <p>- id_doi_dic.json contains a mapping of the publication id to its doi</p> <p>- merged1..5.json contain all publication data from the OARG dataset</p> <p>- necessary_fields_dic.json contains extracted publications’ fields necessary for the work</p> <p>- oarg_ref_rel_dic.json contains mapping of publication id to referenced and related work present in OpenAlex dataset</p> <p>- openalex_found_publications5_4.json contains all data on found publications from the OpenAlex</p> <p>- publication_to_author_dic.json contains a mapping of publications to their authors</p>
Cross-language Wikipedia link graph
<p>Wikipedia articles use Wikidata to list the links to the same article in other language versions. Therefore, each Wikipedia language edition stores the Wikidata Q-id for each article.</p> <p>This dataset constitutes a Wikipedia link graph where all the article identifiers are normalized to Wikidata Q-ids. It contains the normalized links from all Wikipedia language versions. Detailed link count statistics are attached. Note that articles that have no incoming nor outgoing links are not part of this graph.</p> <p>The format is as follows:</p> <p>Q-id of linking page (outgoing) <tab> Q-id of linked page (incoming) <tab> language version - dump date (20241101)</p> <p>This dataset was used to compute <a href="https://danker.s3.amazonaws.com/index.html">Wikidata PageRank</a>. More information can be found on the <a href="https://github.com/athalhammer/danker">danker</a> repository, where the source code of the link extraction as well as the PageRank computation is hosted.</p> <p>Example entries:<br><br>$ bzcat 2024-11-06.allwiki.links.bz2 | head</p> <p>1 107 ckbwiki-20241101<br>1 107 lawiki-20241101<br>1 107 ltwiki-20241101<br>1 107 tewiki-20241101<br>1 107 wuuwiki-20241101<br>1 111 hywwiki-20241101<br>1 11379 bat_smgwiki-20241101<br>1 11471 cdowiki-20241101<br>1 150 ckbwiki-20241101<br>1 150 lowiki-20241101</p> <p> </p> <p> </p>
NASA GES-DISC Knowledge Graph for Link Prediction
<p>This dataset includes a knowledge graph of NASA GES-DISC collections, featuring interconnected nodes for datasets, data center, projects, platforms, instruments, science keywords, and publications. Designed for link prediction tasks, it aids machine learning research in satellite observation, remote sensing, and climate change. The dataset is in CSV format, ready for graph databases and ML frameworks.</p> <p> </p>
Supplementary dataset for "Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking"
<p>The supplementary dataset for the paper "Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking". We include the splits for cora, citeseer, and pubmed, the hard negative samples, and the node2vec embeddings. We also include a jupyter file <em>read_data.ipynb</em> to show how to read the non-txt file.</p> <ul> <li>heart_test_samples.npy, heart_valid_samples.npy: the heard negative samples</li> <li>*-n2v-embedding.pt: node2vec embeddings</li> <li>test_samples_index.pt, valid_samples_index.pt: the node index of the selected samples in ogbl-ppa under HeaRT</li> <li>gnn_feature: the input feature of cora, citeseer, pubmed</li> </ul> <p>More details for our code and how to use the dataset are on the code repository: https://github.com/Juanhui28/HeaRT .</p>
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>
Freebase Datasets for Robust Evaluation of Knowledge Graph Link Prediction Models
<p><strong>Freebase</strong> is amongst the largest public cross-domain knowledge graphs. It possesses three main data modeling idiosyncrasies. It has a strong <strong>type system</strong>; its properties are purposefully represented in <strong>reverse pairs</strong>; and it uses <strong>mediator objects</strong> to represent multiary relationships. These design choices are important in modeling the real-world. But they also pose nontrivial challenges in research of embedding models for knowledge graph completion, especially when models are developed and evaluated agnostically of these idiosyncrasies. We make available several variants of the Freebase dataset by inclusion and exclusion of these data modeling idiosyncrasies. This is the first-ever publicly available <strong>full-scale</strong> Freebase dataset that has gone through <strong>proper preparation</strong>. </p><p> </p><p>Dataset Details</p><p>The dataset consists of the four variants of Freebase dataset as well as related mapping/support files. For each variant, we made three kinds of files available:</p><ul><li>Subject matter triples file<ul><li><i>fb+/-CVT+/-REV</i> One folder for each variant. In each folder there are 5 files: train.txt, valid.txt, test.txt, entity2id.txt, relation2id.txt Subject matter triples are the triples belong to subject matters domains—domains describing real-world facts.<ul><li>Example of a row in train.txt, valid.txt, and test.txt: <ul><li>2, 192, 0</li></ul></li><li>Example of a row in entity2id.txt:<ul><li>/g/112yfy2xr, 2</li></ul></li><li>Example of a row in relation2id.txt:<ul><li>/music/album/release_type, 192</li></ul></li><li>Explaination<ul><li>"/g/112yfy2xr" and "/m/02lx2r" are the MID of the subject entity and object entity, respectively. "/music/album/release_type" is the realtionship between the two entities. 2, 192, and 0 are the IDs assigned by the authors to the objects.</li></ul></li></ul></li></ul></li><li>Type system file<ul><li><i>freebase_endtypes</i>: Each row maps an edge type to its required subject type and object type.<ul><li>Example<ul><li>92, 47178872, 90</li></ul></li><li>Explanation<ul><li>"92" and "90" are the type id of the subject and object which has the relationship id "47178872".</li></ul></li></ul></li></ul></li><li>Metadata files<ul><li><i>object_types</i>: Each row maps the MID of a Freebase object to a type it belongs to.<ul><li>Example<ul><li>/g/11b41c22g, /type/object/type, /people/person</li></ul></li><li>Explanation<ul><li>The entity with MID "/g/11b41c22g" has a type "/people/person"</li></ul></li></ul></li><li><i>object_names</i>: Each row maps the MID of a Freebase object to its textual label.<ul><li>Example<ul><li>/g/11b78qtr5m, /type/object/name, "Viroliano Tries Jazz"@en</li></ul></li><li>Explanation<ul><li>The entity with MID "/g/11b78qtr5m" has name "Viroliano Tries Jazz" in English.</li></ul></li></ul></li><li><i>object_ids</i>: Each row maps the MID of a Freebase object to its user-friendly identifier.<ul><li>Example<ul><li>/m/05v3y9r, /type/object/id, "/music/live_album/concert"</li></ul></li><li>Explanation<ul><li>The entity with MID "/m/05v3y9r" can be interpreted by human as a music concert live album.</li></ul></li></ul></li><li><i>domains_id_label</i>: Each row maps the MID of a Freebase domain to its label.<ul><li>Example<ul><li>/m/05v4pmy, geology, 77</li></ul></li><li>Explanation<ul><li>The object with MID "/m/05v4pmy" in Freebase is the domain "geology", and has id "77" in our dataset.</li></ul></li></ul></li><li><i>types_id_label</i>: Each row maps the MID of a Freebase type to its label.<ul><li>Example<ul><li>/m/01xljxh, /government/political_party, 147</li></ul></li><li>Explanation<ul><li>The object with MID "/m/01xljxh" in Freebase is the type "/government/political_party", and has id "147" in our dataset.</li></ul></li></ul></li><li><i>entities_id_label</i>: Each row maps the MID of a Freebase entity to its label.<ul><li>Example<ul><li>/g/11b78qtr5m, Viroliano Tries Jazz, 2234</li></ul></li><li>Explanation<ul><li>The entity with MID "/g/11b78qtr5m" in Freebase is "Viroliano Tries Jazz", and has id "2234" in our dataset.</li></ul></li><li><i>properties_id_label</i>: Each row maps the MID of a Freebase property to its label.<ul><li>Example<ul><li>/m/010h8tp2, /comedy/comedy_group/members, 47178867</li></ul></li><li>Explanation<ul><li>The object with MID "/m/010h8tp2" in Freebase is a property(relation/edge), it has label "/comedy/comedy_group/members" and has id "47178867" in our dataset.</li></ul></li></ul></li><li><i>uri_original2simplified</i> and <i>uri_simplified2original</i>: The mapping between original URI and simplified URI and the mapping between simplified URI and original URI repectively.<ul><li>Example<ul><li><i>uri_original2simplified</i><ul><li>"<a href="http://rdf.freebase.com/ns/type.property.unique">http://rdf.freebase.com/ns/type.property.unique</a>": "/type/property/unique"</li></ul></li><li><i>uri_simplified2original</i><ul><li>"/type/property/unique": "<a href="http://rdf.freebase.com/ns/type.property.unique">http://rdf.freebase.com/ns/type.property.unique</a>"</li></ul></li></ul></li><li>Explanation<ul><li>The URI "<a href="http://rdf.freebase.com/ns/type.property.unique">http://rdf.freebase.com/ns/type.property.unique</a>" in the original Freebase RDF dataset is simplified into "/type/property/unique" in our dataset.</li><li>The identifier "/type/property/unique" in our dataset has URI <a href="http://rdf.freebase.com/ns/type.property.unique">http://rdf.freebase.com/ns/type.property.unique</a> in the original Freebase RDF dataset.</li></ul></li></ul></li></ul></li></ul></li></ul>
Linked Papers With Code: Knowledge Graph Embeddings
<p>We provide <strong>knowledge graph embeddings</strong> for the <strong>Linked Papers With Code</strong> Knowledge Graph. More information at <a href="https://linkedpaperswithcode.com/">https://linkedpaperswithcode.com/</a></p>
Linked Papers With Code: The Latest in Machine Learning as an RDF Knowledge Graph
<p><strong>Linked Papers With Code (LPWC)</strong> is an <strong>RDF knowledge graph </strong>that comprehensively models the research field of <strong>machine learning</strong>. It contains information about almost 400,000 machine learning <strong>publications</strong>, including the <strong>tasks</strong> addressed, the <strong>datasets</strong> utilized, the <strong>methods</strong> implemented, and the <strong>evaluations</strong> conducted, along with their <strong>results</strong>. The data set is based on <strong>Papers With Code</strong> and licensed under the CC BY-SA 4.0 license. Furthermore, we provide <strong>knowledge graph embeddings</strong> for entities and relations represented in LPWC.</p><p>More information can be found at <a href="https://linkedpaperswithcode.com/"><strong>https://linkedpaperswithcode.com/</strong></a> and in the ISWC'23 publication <a href="https://linkedpaperswithcode.com/"><strong>"</strong></a><a href="https://aifb.kit.edu/web/Inproceedings3993"><strong>Linked Papers With Code: The Latest in Machine Learning as an RDF Knowledge Graph".</strong></a></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.