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55 results for “knowledge modeling”
Modelling knowledge organization systems and structures
<p>In the last few decades, knowledge organization systems (KOS), especially thesauri,<br> classification schemes and lists of subject headings, have largely followed or conformed<br> with the established data models defined by standards, recommendations or best practices.<br> This long list contains some widely used models, such as ISO5964 Part 1, ISO2788, Z39.19,<br> BS 5723 and BS 6723, (Dextre Clarke, 2008) IFLA Principles Underlying Subject Heading<br> Languages (SHLs), and MARC 21 Format for Classification Data.<br> The FRSAD (Functional Requirements for Subject Authority Data) conceptual model is<br> the third member of the FRBR family, developed under the auspices of IFLA. The report<br> was approved in 2010 and will be published in 2011. FRSAD is a general conceptual model<br> that focuses on the subject relationship and therefore provides a theoretical framework for<br> all KOS and their data models. In addition, it also assists in the assessment of the potential<br> for international sharing and (re)use of subject authority data both within the library sector<br> and beyond.<br> In this paper FRSAD is compared to SKOS and SKOS XL as data models (with implementa-<br> tion examples).</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>
Results and log of LLM-KG-Bench runs described in article "Benchmarking the Abilities of Large Language Models for RDF Knowledge Graph Creation and Comprehension: How Well Do LLMs Speak Turtle?", Frey et al. 2023
<p>Results and log of LLM-KG-Bench runs described in article ""Benchmarking the Abilities of Large Language Models for RDF Knowledge Graph Creation and Comprehension: How Well Do LLMs Speak Turtle?", Frey et al. 2023, to appear in proceedings for workshop DL4KG@ISWC 2023.</p> <p>For data on task FactExtractStatic please contact authors.</p>
The Effect of the Flipped Classroom Model on Pediatric Pain Management Knowledge and Learning Motivation of Nursing Students
ClinicalTrials.gov study NCT07129044. IPD Sharing: NO. Countries: 1. Publications: 23.
Protein Subcellular localization prediction data used in the article entitled "MSclassifier: Median-Supplement model-based Classification tool for automated knowledge discovery"
<p>This repository contains data used to obtain results from a 5-fold cross-validation testing of how MSclassifier and other packages accurately predict protein subcellular localization in the software article entitled "MSclassifier: median-supplement model-based classification tool for automated knowledge discovery." The data used in the software article is derived from data generated in "G. K. Acquaah-Mensah, S. M. Leach, and C. Guda, Predicting the subcellular localization of human proteins using machine learning and exploratory data analysis, Genomics Proteomics Bioinformatics, 4(2):120-133, 2006, <a href="https://doi.org/10.1016/S1672-0229(06)60023-5">https://doi.org/10.1016/S1672-0229(06)60023-5</a>"</p>
The best of both worlds: highlighting the synergies of combining knowledge modelling and automated techniques to improve information search and discovery in oil and gas exploration
<p><span>Research suggests organizations across all sectors waste a significant amount of time looking for information and often fail to leverage the information they have. In response, many organizations have deployed some form of enterprise search to improve the ‘findability’ of information. Debates persist as to whether thesauri and manual indexing or automated machine learning techniques should be used to enhance discovery of information. In addition, the extent to which a Knowledge Organization System (KOS) enhances discoveries or indeed blinds us to new ones remains a moot point. The oil and gas industry is used as a case study using a representative organization. Drawing on prior research, a theoretical model is presented which aims to overcome the shortcomings of each approach. This synergistic model could help to re-conceptualize the ‘manual’ versus ‘automatic’ debate in many enterprises, accommodating a broader range of information needs. This may enable enterprises to develop more effective information and knowledge management strategies and ease the tension between what are often perceived as mutually exclusive competing approaches. Certain aspects of the theoretical model may be transferable to other industries, which is an area for further research.</span></p>
Cytoscape session for the potato knowledge graph extracted with IBM Watson's supervised NLP model
<p>2 cytoscape session files (.cys) representing the genotypic-phenotypic knowledge networks retrieved from scientific literature using IBM Watson. Input to these files were the following: </p> <ul> <li>cytoscapeSession_trainingSet: A training set of 34 full-test articles about potato flesh color</li> <li>cytoscapesession_testSet: A testing set of a 4023 abstracts from PubMed.</li> </ul> <p> </p>
Supporting information for the RNAct Data Science Wizard (DSW) knowledge model for early-stage researchers.
<p>The two attached Excel sheets contain information about the datasets collected by the Early Stage Researchers (ESRs) in the RNAct MSCA-ITN project (RNAct_datasets.xlsx) and on the questionnaire that was put to the ESRs in relation to the Data Science Wizard (DSW) knowledge model that was developed as part of RNAct. </p> <p>The zip file contains data in relation to the Data Science Wizard template development:</p> <p>- DMP_1stRound_introductory_presentation.pdf: Presentation for the ESRs to prepare them for filling in the first version of the DMP</p> <p>- DMPs_1stRound, DMPs_2ndRound: The filled in DMPs by the ESRs in the first and second round</p> <p>- RNAct-ESRtraining-KM_1.0.3.km: The first version of the knowledge model to create the DMP</p> <p>- RNAct-ESRtraining-KM_1.0.14.km: The second and final version of the knowledge model to create the DMP</p> <p>- TemplateDMP_RNAct-ESRtraining-KM_1.0.14.pdf: A PDF overview of the second and final version of the knowledge model</p>
Exposure to a Vitamin D Best Practices Toolkit, Model, & E-Tools Increases Knowledge, Confidence, and Translation of Research to Public Health and Practice
<p>Preventable vitamin D deficiency (VDD) is a global health concern. Prevention, early detection, and treatment of vitamin D deficiency aligning with serum 25-hydroxyvitamin D recommendations of 40-60 ng/ml (100-150 nmol/L) by an international panel of 48 vitamin D researchers would result in significant health benefits and cost-savings to individuals and society. However, research shows healthcare professionals lack knowledge and confidence in vitamin D best practices. A vitamin D toolkit was developed, including a model for decision-making support, e-tools, and accompanying resources, and implemented using an online, asynchronous learning management system. The pre-, post-test, and follow-up survey study design aimed to increase nurses’ and dietitians' levels of vitamin D knowledge, confidence, translation of evidence into spheres of practice and influence, and identify translation barriers. Toolkit completion increased participants’ (n=119) knowledge, 31% to 65% (p <0.001) and confidence 2.0 to 3.3 (p<0.001) on a scale of 1-5. Respondents reported using the model (100%) as a framework successfully guiding the translation of vitamin D knowledge into their sphere of influence or practice (94%) and identified translation barriers. The toolkit should be included in interdisciplinary continuing education, research/quality improvement initiatives, healthcare policy, and institutions of higher learning to increase movement of research to practice.</p>
Assessing an Educational Intervention Program on Knowledge, Attitude and Behaviour Towards Pregnancy Prevention Based on Health Belief Model Amongst Adolescent Girls in Northern Ghana
ClinicalTrials.gov study NCT03384251. IPD Sharing: YES. Countries: 1. Publications: 8.
Pelvic Floor Disorder Assessment of Knowledge and Symptoms: an Educational Model in Spanish-Speaking Women
ClinicalTrials.gov study NCT04829721. IPD Sharing: NO. Countries: 1. Publications: 13.
Improving the Quality of Radiotherapy by Multi-Institution Knowledge-Based Planning Optimization Models (Acronym: MIKAPOCo, Multi-Institutional Knowledge-based Approach in Plan Optimization for the Co
ClinicalTrials.gov study NCT06317948. IPD Sharing: NO. Countries: 1. Publications: 5.
Flipped Learning Model on Students' Knowledge and Health Beliefs
ClinicalTrials.gov study NCT04851197. IPD Sharing: NO. Countries: 1. Publications: 10.
Test of A Model of Representational Knowledge Stored in the Human Prefrontal Cortex
ClinicalTrials.gov study NCT00024908. IPD Sharing: Not stated. Countries: 1. Publications: 3.
The Effect of Web-Based Breastfeeding Education Based on Knowledge, Motivation, Behavioral Skills Model on Fathers' Breastfeeding Support
ClinicalTrials.gov study NCT07205458. IPD Sharing: NO. Countries: 1. Publications: 7.
A Palliative Care Model Impact on Knowledge and Attitudes
ClinicalTrials.gov study NCT06860932. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Applying the ADDIE Model to Develop a Multimedia Program to Enhance Chemotherapy Knowledge and Self-Efficacy Among Intensive Care Unit Nurses
ClinicalTrials.gov study NCT07269132. IPD Sharing: NO. Countries: 1. Publications: 7.
Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation
<p>Teacher train files (ids only), based on the MSMARCO-Passage collection, for the paper: </p> <p><em>Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation</em> Sebastian Hofstätter, Sophia Althammer, Michael Schröder, Mete Sertkan and Allan Hanbury (<a href="https://arxiv.org/abs/2010.02666">https://arxiv.org/abs/2010.02666</a>)</p> <p>for the documentation please see: https://github.com/sebastian-hofstaetter/neural-ranking-kd</p>
About epiTCR-KDA: Knowledge Distillation model on Dihedral Angles for TCR-peptide prediction
Open the record for dataset details and reuse information.
Supplementary Material for Paper "Distilling Event Sequence Knowledge From Large Language Models"
<p>Supplementary Material for Paper:<br>Distilling Event Sequence Knowledge From Large Language Models<br>Somin Wadhwa, Oktie Hassanzadeh, Debarun Bhattacharjya, Ken Barker, and Jian Ni</p> <p>Appendix:<br>- <code>Appendix.pdf</code>: contains our prompts and a description of our human evaluation details.</p> <p>Data:<br>- <code>base_kg_v7.jsonl</code>: Our Wikidata-based Event Causal Knowledge Graph.</p> <p>Outputs:<br>- <code>sample_new_patterns_discovered.txt</code>: examples of observed new patters through application of sequential pattern mining algorithms, described in section 3.<br>- <code>precision_eval_sample.txt:</code> examples of output evaluated with a precision-evaluator model. <br>- <code>bsumm_output.txt</code>: sample outputs of identified influencing events through the application of binary summary markov model, described in section 5.2.</p> <p>Code:<br>- <code>src/generator.py</code>: ingests ICL prompts and generates requisite event sequences.<br>- <code>src/benchmarking.py</code>: ingests a _trained_ Flan-style seq2seq model to evaluate precision, and recall from the base KG.<br>- <code>src/utils.py</code>: utilities for generator and benchmarking.</p> <p>To cite:</p> <pre><code>@inproceedings{WadhwaHBBN24, author = {Somin Wadhwa and Oktie Hassanzadeh and Debarun Bhattacharjya and Ken Barker and Jian Ni}, title = {Distilling Event Sequence Knowledge From Large Language Models}, booktitle = {The Semantic Web - 23rd International Conference, {ISWC} 2024}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, year = {2024}, }</code></pre>
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.