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219 results for “Knowledge Graph”
MTL-QA : A dataset and multi-task learning approach for knowledge graph and natural language question answering
<p>The dataset used for this project is created by enhancing the publicly available MetaQA (Movie Text Audio QA), which is primarily a KGQA dataset pertaining to movies, an extension of WikiMovies. This involves questions requiring 1, 2, and 3 hops which can be answered by using a MetaQA Knowledge Graph. The questions are available in text and audio format. The text has vanilla (original) and its paraphrased version, and is called ntm. </p> <p>In order to develop a dataset to support NLQA, a series of dataset augmentation steps has been performed.</p> <p>The dataset consists of natural language questions and a tagged topic entity as ground truth. This topic entity is used to retrieve textual information related to the question from Wikipedia. The introduction section of the entity's page is used as the context that is required for NLQA. Hence, this dataset has information related to both KGQA and NLQA. Certain preliminary checks and validations are done to only retain those data samples whose context can be used to answer a given question.</p>
LawSampo Knowledge Graph
<p>The LawSampo Knowledge Graph contains Finnish consolidated legislation and case law as Linked Open Data.</p> <p>This dataset is available on a public SPARQL endpoint (<a href="http://ldf.fi/lawsampo/sparql"><em>http://ldf.fi/lawsampo/sparql</em></a>).</p> <p>To test and demonstrate its usefulness, this Knowledge Graph is in use in the semantic portal <a href="https://lakisampo.fi/">LawSampo</a>, explained in more detail in the <a href="https://seco.cs.aalto.fi/projects/lakisampo/en/">project page</a>.</p> <p>See the dataset page on <a href="https://www.ldf.fi/dataset/lawsampo">LDF.fi</a> for more details.</p>
NTDs2RDF: A heterogeneous and integrated knowledge graph for the exploration of neglected tropical diseases
<p>Neglected tropical diseases (NTDs) are a heterogeneous group of 20 bacterial, viral, parasitic, and fungal conditions that generally occur in developing tropical countries in the Americas, Africa, and Asia. NTDs mainly affect poor populations that do not have access to safe water, sanitation, and high-quality healthcare. Because of the severe effects of NTDs (i.e., they can cause long-lasting disabilities), they reinforce the cycle of poverty in vulnerable communities.</p> <p>The <strong>NTDs2RDF</strong> project aimed to create a knowledge graph (KG) of genes, proteins, metabolic pathways, gene ontologies, single nucleotide variants, drugs, and other relevant data for three NTDs (Chagas disease, leishmaniasis, and African trypanosomiasis), integrating all the information in a single data structure that can be explored through a query interface implemented with a Streamlit web application. This software provides a user-friendly platform to extract information from the KG using SPARQL queries.</p> <p>The project represents an initial step towards the creation of a heterogeneous database for different NTDs with several potential applications in advancing the understanding of NTDs biology and providing insights that cannot be obtained through alternative resources.</p>
A Benchmark Dataset with Knowledge Graph Generation for Industry 4.0 Production Lines
<p>A benchmark dataset for knowledge graph generation in Industry 4.0 production lines and to show the benefits of using ontologies and semantic annotations of data to showcase how I4.0 industry can benefit from KGs and semantic datasets. This work is a result of collaborations with the production line managers, supervisors, and engineers of a football industry to acquire realistic production line data. Knowledge Graphs (KGs) or a Knowledge Graph (KG) emerged as a significant technology to store the semantics of the domain entities. The data is mapped and populated with RGOM classes and relations using an automated solution based on JenaAPI, producing an I4.0 KG. <br> <br> Usage:<br> <br> Recently, we use this dataset to analyze the performance of the five state-of-the-art KG embedding models, namely ComplEx, DistMult,TransE, ConvKB, and ConvE. We evaluated the models using two key metrics: Mean Reciprocal Rank (MRR), and Hits@N (Hits@10, Hits@3, and Hits@1). We observed that the TransE model outperforms other models, followed by ComplEx and DistMult, with ConvE demonstrating the lowest performance. Similarly, the dataset can be used alternatively in other potential scenarios.</p>
Gollum: A Gold Standard for Large Scale\\Multi Source Knowledge Graph Matching
<p>The set of Knowledge Graphs (KGs) generated with automatic and manual approaches is constantly growing.<br> For an integrated view and usage, an alignment between these KGs is necessary on the schema as well as instance level.<br> There are already approaches which try to tackle this multi source knowledge graph matching problem,<br> but large gold standards are missing to evaluate their effectiveness and scalability.<br> In particular, most existing gold standards are fairly small and can be solved by matchers which match exactly two KGs (1:1), which are the majority of existing matching systems.<br> <br> We close this gap by presenting Gollum -- a gold standard for large-scale multi source knowledge graph matching with over 275,000 correspondences between 4,149 different KGs.<br> They originate from knowledge graphs derived by applying the DBpedia extraction framework to a large wiki farm.<br> <br> Three variations of the gold standard are made available:<br> (1) a version with all correspondences for evaluating unsupervised matching approaches, and two versions for evaluating supervised matching: (2) one where each KG is contained both in the train and test set, and (3) one where each KG is exclusively contained in the train or the test set.</p> <p><br> We plan to extend our KG track at the Ontology Alignment Evaluation Initiative (OAEI) to allow for matching systems <br> which are specifically designed to solve the multi KG matching problem.<br> As a first step towards this direction, we evaluate multi source matching approaches which reuse two-KG (1:1) matchers from the past OAEI.</p> <p> </p> <p>Due to the size of the KG files, they are hosted at the institute:</p> <p><a href="http://data.dws.informatik.uni-mannheim.de/dbkwik/gollum/40K.tar">http://data.dws.informatik.uni-mannheim.de/dbkwik/gollum/40K.tar</a> (50,3 GB)<br> <a href="http://data.dws.informatik.uni-mannheim.de/dbkwik/gollum/all.tar">http://data.dws.informatik.uni-mannheim.de/dbkwik/gollum/all.tar</a> (74,7 GB)<br> <a href="http://data.dws.informatik.uni-mannheim.de/dbkwik/gollum/gold.tar">http://data.dws.informatik.uni-mannheim.de/dbkwik/gollum/gold.tar</a> (25,3 GB)</p>
Text2KGBench: A Benchmark for Ontology-Driven Knowledge Graph Generation from Text
<p>This is the repository for ISWC 2023 Resource Track submission for <code>Text2KGBench: Benchmark for Ontology-Driven Knowledge Graph Generation from Text</code>. Text2KGBench is a benchmark to evaluate the capabilities of language models to generate KGs from natural language text guided by an ontology. Given an input ontology and a set of sentences, the task is to extract facts from the text while complying with the given ontology (concepts, relations, domain/range constraints) and being faithful to the input sentences.</p> <p>It contains two datasets (i) Wikidata-TekGen with 10 ontologies and 13,474 sentences and (ii) DBpedia-WebNLG with 19 ontologies and 4,860 sentences.</p> <p><strong>An example</strong></p> <p>An example test sentence:</p> <pre><code>Test Sentence: {"id": "ont_music_test_n", "sent": "\"The Loco-Motion\" is a 1962 pop song written by American songwriters Gerry Goffin and Carole King."} </code></pre> <p>An example of ontology:</p> <p>Ontology: <a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/ontologies/owl/ont_2_music.ttl">Music Ontology</a></p> <p>Expected Output:</p> <pre><code>{ "id": "ont_k_music_test_n", "sent": "\"The Loco-Motion\" is a 1962 pop song written by American songwriters Gerry Goffin and Carole King.", "triples": [ { "sub": "The Loco-Motion", "rel": "publication date", "obj": "01 January 1962" },{ "sub": "The Loco-Motion", "rel": "lyrics by", "obj": "Gerry Goffin" },{ "sub": "The Loco-Motion", "rel": "lyrics by", "obj": "Carole King" },] } </code></pre> <p>The data is released under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY 4.0) License.</p> <p>The structure of the repo is as the following.</p> <ul> <li>Text2KGBench <ul> <li>src: the source code used for generation and evaluation, and baseline <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/src/benchmark"><code>benchmark</code></a> the code used to generate the benchmark</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/src/evaluation"><code>evaluation</code></a> evaluation scripts for calculating the results</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/src/evaluation">baseline</a> code for generating the baselines including prompts, sentence similarities, and LLM client.</li> </ul> </li> <li>data: the benchmark datasets and baseline data. There are two datasets: wikidata_tekgen and dbpedia_webnlg. <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen">wikidata_tekgen</a> Wikidata-TekGen Dataset <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/ontologies">ontologies</a> 10 ontologies used by this dataset</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/train">train</a> training data</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/test">test</a> test data</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/manually_verified_sentences">manually_verified_sentences</a> ids of a subset of test cases manually validated</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/unseen_sentences">unseen_sentences</a> new sentences that are added by the authors which are not part of Wikipedia <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/unseen_sentences/test">test unseen</a> test unseen test sentences</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/unseen_sentences/ground_truth">ground_truth</a> ground truth for unseen test sentences.</li> </ul> </li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/ground_truth">ground_truth</a> ground truth for the test data</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines">baselines</a> data related to running the baselines. <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/test_train_sent_similarity">test_train_sent_similarity</a> for each test case, 5 most similar train sentences generated using SBERT T5-XXL model.</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/prompts">prompts</a> prompts corresponding to each test file <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/prompts/unseen">unseen prompts</a> unseen prompts for the unseen test cases</li> </ul> </li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Alpaca-LoRA-13B">Alpaca-LoRA-13B</a> data related to the Alpaca-LoRA model <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Alpaca-LoRA-13B/llm_responses">llm_responses</a> raw LLM responses and extracted triples</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Alpaca-LoRA-13B/eval_metrics">eval_metrics</a> ontology-level and aggregated evaluation results</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Alpaca-LoRA-13B/unseen">unseen results</a> results for the unseen test cases <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Alpaca-LoRA-13B/unseen/llm_responses">llm_responses</a> raw LLM responses and extracted triples</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Alpaca-LoRA-13B/unseen/eval_metrics">eval_metrics</a> ontology-level and aggregated evaluation results</li> </ul> </li> </ul> </li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Vicuna-13B">Vicuna-13B</a> data related to the Vicuna-13B model <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Vicuna-13B/llm_responses">llm_responses</a> raw LLM responses and extracted triples</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Vicuna-13B/eval_metrics">eval_metrics</a> ontology-level and aggregated evaluation results</li> </ul> </li> </ul> </li> </ul> </li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg">dbpedia_webnlg</a> DBpedia Dataset <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/ontologies">ontologies</a> 19 ontologies used by this dataset</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/train">train</a> training data</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/test">test</a> test data</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/ground_truth">ground_truth</a> ground truth for the test data</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines">baselines</a> data related to running the baselines. <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/test_train_sent_similarity">test_train_sent_similarity</a> for each test case, 5 most similar train sentences generated using SBERT T5-XXL model.</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/prompts">prompts</a> prompts corresponding to each test file</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/Alpaca-LoRA-13B">Alpaca-LoRA-13B</a> data related to the Alpaca-LoRA model <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/Alpaca-LoRA-13B/llm_responses">llm_responses</a> raw LLM responses and extracted triples</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/Alpaca-LoRA-13B/eval_metrics">eval_metrics</a> ontology-level and aggregated evaluation results</li> </ul> </li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/Vicuna-13B">Vicuna-13B</a> data related to the Vicuna-13B model <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/Vicuna-13B/llm_responses">llm_responses</a> raw LLM responses and extracted triples</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/Vicuna-13B/eval_metrics">eval_metrics</a> ontology-level and aggregated evaluation results</li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul> <p>This benchmark contains data derived from the TekGen corpus (part of the KELM corpus) [1] released under CC BY-SA 2.0 license and WebNLG 3.0 corpus [2] released under CC BY-NC-SA 4.0 license.</p> <p>[1] Oshin Agarwal, Heming Ge, Siamak Shakeri, and Rami Al-Rfou. 2021. Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3554–3565, Online. Association for Computational Linguistics.</p> <p>[2] Claire Gardent, Anastasia Shimorina, Shashi Narayan, and Laura Perez-Beltrachini. 2017. Creating Training Corpora for NLG Micro-Planners. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 179–188, Vancouver, Canada. Association for Computational Linguistics.</p>
Patterns Knowledge Graph
<p>The Patterns Knowledge Graph contains data about patterns in folk / traditional music. The patterns are those extracted using the <a href="https://github.com/polifonia-project/folk_ngram_analysis">FONN tools</a>.</p> <ul> <li>Allows exploration of patterns as linked open data</li> <li>Multiple corpora, multiple definitions of patterns (e.g. n=(4, 5, 6), accent-level versus note-level)</li> <li>Running public SPARQL endpoint and MELODY data stories as demonstrations.</li> </ul>
PubGraph: A Large-Scale Scientific Knowledge Graph
<p>We present PubGraph, a new resource for studying scientific progress that takes the form of a large-scale knowledge graph (KG) with more than 385M entities, 13B main edges, and 1.5B qualifier edges. PubGraph is comprehensive and unifies data from various sources, including Wikidata, OpenAlex, and Semantic Scholar, using the Wikidata ontology. Beyond the metadata available from these sources, PubGraph includes outputs from auxiliary community detection algorithms and large language models. To further support studies on reasoning over scientific networks, we create several large-scale benchmarks extracted from PubGraph for the core task of knowledge graph completion (KGC). These benchmarks present many challenges for knowledge graph embedding models, including an adversarial community-based KGC evaluation setting, zero-shot inductive learning, and large-scale learning. All of the aforementioned resources are accessible at <a href="https://purl.archive.org/pubgraph">https://purl.archive.org/pubgraph</a> and released under the CC-BY-SA license. We plan to update PubGraph quarterly to accommodate the release of new publications.</p>
MechRepoNet with DrugCentral Indications Knowledge Graph
<p><em><strong>M</strong></em>echanistic Repositioning Network with <em><strong>Ind</strong></em>ications (MIND) is a knowledge graph incorporating two biomedical resources: MechRepoNet and DrugCentral. MechRepoNet is a knowledge graph comprising of 18 biomedical resources that reflects and expands on important drug mechanism relationships identified from a curated biomedical drug mechanism dataset. MechRepoNet consists of 9,652,116 edges, 250,035 nodes, 9 node types and 22 relations. DrugCentral is a curated, open-access, online resource that integrates structure, bioactivity, regulatory, pharmacologic actions and approved drug indications from the FDA and other regulatory agencies. As of October 2021, DrugCentral has 11,292 indications, consisting of 2,494 unique compounds and 1,459 unique diseases.</p> <p>MIND was created by mapping DrugCentral edges to existing MechRepoNet nodes through the Unified Medical Language System (UMLS) and Medical Subject Headings (MeSH). A total of 5,379 indication edges made up of 1,308 unique compounds and 1,030 unique diseases were mapped to MechRepoNet. DrugCentral edges incorporated in MIND are labeled have the relationship indication. The indication edge is differentiated from the treat edge in that the latter represents a weaker link between a drug and a disease as some treat edges are not approved by a regulatory body in contrast to the former. In MIND, when both indication and treat edges exist, indication superseded and replaced treat edges. The MIND training set has a total of 9,651,040 edges. </p> <p>The indications from DrugCentral were divided into a train set (80%; 4303 triples) and a test/valid set (20%; 1076 triples; test.txt/valid.txt). The DrugCentral train set was merged into MechRepoNet to form the train.txt file.</p>
Knowledge graphs for interoperable NFDI: Digital editions
<p>Präsentation im Rahmen des Text+ FAIR February Meetup 2023: <em>I wie Interoperability, </em>15. Februar 2023.</p>
The evaluation of ActivityNet-EKG* knowledge graph
<p>Evaluation of ActivityNet-EKG* knowledge graph for 286 multimodal documents.</p>
Semantics 2023 Knowledge Graph
<p>Input data sources, mappings and RDF results used to generate the Semantics 2023 KG.</p> <p>We follow a simple data model, reusing the [Schema.org](https://schema.org/) vocabulary as much as possible. All papers are schema:ScholarlyArticles, that are part of tracks (schema:Event) and are authored by schema:Persons. Each article has either datasets (schema:Dataset), code repositories (schema:SoftwareSourceCode), ontologies (owl:Ontology) or Demos (schema:SoftwareApplication).</p> <p>As a result, our KG has 2521 triples, which describe 50 papers, 22 code repositories, 11 datasets, 7 demos and 5 ontologies.</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>
Results and log of LLM-KG-Bench runs described in article "Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph Engineering", Meyer et al. 2023
<p>Results and logs of <a href="https://github.com/AKSW/LLM-KG-Bench">LLM-KG-Bench</a> runs described in article "Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph Engineering", Meyer et al., to appear in <a href="https://2023-eu.semantics.cc/page/accepted_posters">SEMANTICS 2023 poster track</a> proceedings.</p>
Building a knowledge graph of the Benetton's group suppliers network
<p>Some resources used for the PW4 of the master's AI4GOV. It concerns the Benetton Group's supplier network</p>
Knowledge-Graphs-for-Seismic-Data-and-Metadata: Data
<p><strong>Abstract</strong></p> <p>The increasing scale and diversity of seismic data, and the growing role of big data in seismology, has raised interest in methods to make data exploration more accessible. This paper presents the use of knowledge graphs (KGs) for representing seismic data and metadata to improve data exploration and analysis, focusing on usability, flexibility, and extensibility. Using constraints derived from domain knowledge in seismology, we define semantic models of seismic station and event information used to construct the KGs. Our approach utilizes the capability of KGs to integrate data across many sources and diverse schema formats. We use schema-diverse, real-world seismic data to construct KGs with millions of nodes, and illustrate potential applications with three big-data examples. Our findings demonstrate the potential of KGs to enhance the efficiency and efficacy of seismological workflows in research and beyond, indicating a promising interdisciplinary future for this technology.</p> <p><strong>Methods</strong></p> <p>The data here consists of, and was collected from:</p> <ul> <li>Station metadata, in StationXML format, acquired from IRIS DMC using the fdsnws-station webservice (https://service.iris.edu/fdsnws/station/1/).</li> <li>Earthquake event data, in NDK format, acquired from the Global Centroid-Moment Tensor (GCMT) catalog webservice (https://www.globalcmt.org) [1,2]. </li> <li>Earthquake event data, in CSV format, acquired from the Northern California Seismic Network (NCSN) catalog using the NCEDC's Northern California Earthquake Catalog Search webservice (doi.org/10.7932/NCEDC) [3].</li> <li>Earthquake event data, in CSV format, acquired from the USGS earthquake catalog webservice (doi.org/10.5066/F7MS3QZH) [4].</li> </ul> <p>A complete description of the StationXML file format can be found at https://www.fdsn.org/xml/station/.</p> <p>A complete description of the NDK file format can be found at https://www.ldeo.columbia.edu/~gcmt/projects/CMT/catalog/allorder.ndk_explained. </p> <p>A complete description of the NCEDC file format can be found at https://ncedc.org/pub/doc/cat1/catlist.txt.</p> <p>A complete description of the USGS file format can be found at https://earthquake.usgs.gov/data/comcat/#event-terms.</p> <p>Also provided are conversions from NDK and StationXML file formats into JSON format. </p> <p><strong>Usage Notes</strong></p> <p>No special programs or software is reqired to open the data files included here.</p> <p><strong>References</strong></p> <p>[1] Dziewonski, A. M., Chou, T. A., & Woodhouse, J. H. (1981). Determination of earthquake source parameters from waveform data for studies of global and regional seismicity. <em>Journal of Geophysical Research: Solid Earth</em>, <em>86</em>(B4), 2825-2852.</p> <p>[2] Ekström, G., Nettles, M., & Dziewoński, A. M. (2012). The global CMT project 2004–2010: Centroid-moment tensors for 13,017 earthquakes. <em>Physics of the Earth and Planetary Interiors</em>, <em>200</em>, 1-9.</p> <p>[3] NCEDC (2014), Northern California Earthquake Data Center. UC Berkeley Seismological Laboratory. Dataset. doi:10.7932/NCEDC.</p> <p>[4] U.S. Geological Survey, Earthquake Hazards Program, 2017, Advanced National Seismic System (ANSS) Comprehensive Catalog of Earthquake Events and Products: Various, https://doi.org/10.5066/F7MS3QZH.</p> <p> </p>
Entity comparison in knowledge graphs
<p>Here are the datasets, figures and table results used in the Entity comparison in KGs project.</p>
CORD-19 Named Entities Knowledge Graph (CORD19-NEKG)
<p>CORD-19 Named Entities Knowledge Graph (CORD19-NEKG) is an RDF dataset describing named entities identified in the scholarly articles of the <a href="https://www.semanticscholar.org/cord19">COVID-19 Open Research Dataset</a> (CORD-19), a resource of over 47,000 articles about COVID-19 and the coronavirus family of viruses.</p> <p>Homepage: https://github.com/Wimmics/cord19-nekg</p> <p>License: see LICENCE file in the dataset.</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>
Toxicological Effect and Risk Assessment (TERA) Knowledge Graph
<p>The Toxicological Effect and Risk Assessment (TERA) Knowledge Graph is based on chemical effect data from U.S. EPA ECOTOX. This data is aligned to non-proprietary identifiers using ontology alignment tools and external sources (eg. wikidata). This enables the use of external chemical knowledge graphs (eg. ChEBI, PubChem, Mesh, "Courtesy of the U.S. National Library of Medicine"). This data set also included an aggregated (into a knowledge graph) of the NCBI taxonomy and Encyclopedia of Life traits data. </p> <p>Linking ECOTOX to external sources enables the extrapolation of effect data, which can extend the reach of ecological risk assessment and limit laboratory experiments. </p> <p>The construction and APIs to facilitate access can be found at <a href="https://gitlab.com/Erik-BM/rappt">https://github.com/NIVA-Knowledge-Graph/TERA</a> </p> <p> </p> <p>LICENCES:<br> <strong>EOL</strong>: Various Creative Commons (CC),<br> <strong>NCBI</strong>: Creative Commons CC0 1.0 Universal (CC0 1.0),<br> <strong>ECOTOX</strong>: No restrictions,<br> <strong>PubChem</strong>: Open Data Commons Open Database License, <br> <strong>ChEMBL</strong>: CC Attribution,<br> <strong>MeSH</strong>: Open<em>, Courtesy of the U.S. National Library of Medicine,</em><br> <strong>Wikidata</strong><em>: CC0 1.0.</em></p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.