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15 results for “Knowledge representation”
BRAIN Journal-Right-Linear Languages Generated in Systems of Knowledge Representation based on LSG-Right-Figure 3. Representation of the grammar G1 in the labelled graph G0 1
<p>If we take the labeled graph G0 1 given in Figure 3 and construct the stratified graph structure over (99) such that (100) we obtain (101), (102). </p> <p>In this paper, we proposed a new system for formal language generation by means of stratified graphs structures. This mechanism can generate languages of the first type and of the second type. More precisely, we propose a new system for formal language generation by means of a system of knowledge based on stratified graphs. We exemplified that, using an interpretation system specially defined for stratified graphs representations, a particular formal language can be obtained by means of the resulted accepted structured paths.</p>
BRAIN Journal-Right-Linear Languages Generated in Systems of Knowledge Representation based on LSG-Right-Figure 2. The representation of the rule
<p>In order to model these derivations in the stratified graph G, each production of the grammar will be represented in the labeled graph G0 by a direct arc of the form given in Figure 2.</p> <p> </p>
BRAIN Journal-Right-Linear Languages Generated in Systems of Knowledge Representation based on LSG-Right-Figure 1. The graphical representation of the morphism
<p>A morphism of partial algebras such that (30) and if (31), then (32) (see Figure 1). We obtain f(L) = T which means that “for every element of L the associated element of T is computed by the morphism f” (Ţăndăreanu, 2000).</p>
Perspectivism on Knowledge Representation
<p>Talk at the <a href="https://www.digital-philosophy.org/">Philosophy [in:of:for:and] Digital Knowledge Infrastructures</a> online workshop (08/09/2022).</p>
Learning Unsupervised Knowledge-Enhanced Representations to Reduce the Semantic Gap in Information Retrieval (Evaluation datasets)
<p>This dataset contains all the runs, pools, plots and analyses to reproduce the results presented in the paper: "Learning Unsupervised Knowledge-Enhanced Representations to Reduce the Semantic Gap in Information Retrieval ", 2020.</p>
Knowledge representation of an ALICE Analysis
<p>The diagram shows a mindmap representing all the pieces of knowledge that need to be collected to preserve an analysis of the ALICE experiment in the CERN Analysis Preservation system. This diagram provides the basis for the metadata schema used in CAP.</p>
Knowledge representation of a CMS Analysis
<p>The diagram shows a mindmap representing all the pieces of knowledge that need to be collected to preserve an analysis of the CMS experiment in the CERN Analysis Preservation system. This diagram provides the basis for the metadata schema used in CAP.</p>
Knowledge representation of a LHCb Analysis
<p>The diagram shows a mindmap representing all the pieces of knowledge that need to be collected to preserve an analysis of the LCHb experiment in the CERN Analysis Preservation system. This diagram provides the basis for the metadata schema used in CAP.</p>
Comparison of Knowledge Graph Representations for Consumer Scenarios - Datasets
<p>These are the datasets used for the evaluations carried out in the submission "Comparison of Knowledge Graph Representations for Consumer Scenarios" to ISWC 2023</p>
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.
Figure 1 from: Conte M, Flynn AJ, Barrison P, Boisvert P, Landis-Lewis Z, Friedman C (2023) Digital objects to make computable biomedical knowledge FAIR: an infrastructural approach to knowledge representation, dissemination and implementation. Research Ideas and Outcomes 9: e109307. https://doi.org/10.3897/rio.9.e109307
Figure 1 Conceptual model of a Knowledge Object (KO) containing a payload, machine-actionable service and deployment specifications, metadata and a unique persistent identifier. We are exploring aligning our conceptual model with emerging best practices for FAIR Digital Objects. Derived from Wittenburg et al's Digital Objects as Drivers towards Convergence in Data Infrastructures (Wittenburg et al. 2019).
Figure 3 from: Conte M, Flynn AJ, Barrison P, Boisvert P, Landis-Lewis Z, Friedman C (2023) Digital objects to make computable biomedical knowledge FAIR: an infrastructural approach to knowledge representation, dissemination and implementation. Research Ideas and Outcomes 9: e109307. https://doi.org/10.3897/rio.9.e109307
Figure 3 This figure illustrates the dual nature of Knowledge Objects: knowledge-as-resource and knowledge-as-service. A KO can be curated and maintained in a repository, pass metadata to a knowledge graph or deployed into applications. Different to other digital objects, the methods to deploy the KO to applications via custom or generic runtimes called by microservices are built into the KO.
Figure 2 from: Conte M, Flynn AJ, Barrison P, Boisvert P, Landis-Lewis Z, Friedman C (2023) Digital objects to make computable biomedical knowledge FAIR: an infrastructural approach to knowledge representation, dissemination and implementation. Research Ideas and Outcomes 9: e109307. https://doi.org/10.3897/rio.9.e109307
Figure 2 (L) Sample KO as viewed from the KGrid Library, from which the KO can be implemented in a hosted runtime environment or downloaded. (R) Sample output results from deploying the KO.
Object color knowledge representation occurs in the macaque brain despite the absence of a developed language system
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From knowledge organization to knowledge representation
<p>LIS developed its own very successful solutions for the classification and search of documents. In KO, search is focused on document properties (e.g. title, author, subject). KO tends to fail in situations when users express their needs in terms of entity properties (e.g., of the author).</p> <p>The usefulness of moving from KO to KR</p> <p>KO is methodologically very strong, but subjects are limited in formality and expressiveness as, by employing classification ontologies, it only supports queries by document properties.<br> KR, by employing descriptive ontologies, supports queries by any entity property, but it is methodologically weaker than KO.<br> We propose the DERA* faceted KR approach<br> DERA, being faceted, allows the development of high quality and scalable descriptive ontologies<br> DERA, being a KR approach, allows modeling relevant entities of the domain and their E/R/A properties and enables automated reasoning.<br> It supports a highly expressive search of documents exploiting entity properties.<br> * DERA is a KR approach as it models entities of a domain (D) by their entity classes (E), relations (R) and attributes (A)</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.