Structured Knowledge
Structured Knowledge - Related Videos
06:08
Exploring the Role of Deontic Reasoning and World Knowledge in Wason´s Selection Task
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2025
This study aims to analyze how people reason with and about normative rules, deontic reasoning, and the importance of world knowledge in subjects´ inferences. For this purpose, one of the main experimental paradigms in reasoning research was used: Wason´s selection task.
12:08
From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
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Cited by 14 •
2014
The bottleneck for cellular 3D electron microscopy is feature extraction (segmentation) in highly complex 3D density maps. We have developed a set of criteria, which provides guidance regarding which segmentation approach (manual, semi-automated, or automated) is best suited for different data types, thus providing a starting point for effective segmentation.
11:05
Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
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Cited by 10 •
2016
The following paper presents a novel FE simulation technique (KBC-FE), which reduces computational cost by performing simulations on a cloud computing environment, through the application of individual modules. Moreover, it establishes a seamless collaborative network between world leading scientists, enabling the integration of cutting edge knowledge modules into FE simulations.
12:03
Dynamics of Structures
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2023
Source: Roberto Leon, Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA It is rare nowadays that a whole year goes by without a major earthquake event wreaking havoc somewhere around the world. In some cases, like the 2005 Banda Ache earthquake in Indonesia, the damage involved large geographic areas and casualties in the six figures. In general, the number and intensity of earthquakes is not increasing, however, the vulnerability of the built environment is...
09:54
Structure Of Ferrocene
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2023
Source: Tamara M. Powers, Department of Chemistry, Texas A&M University In 1951, Kealy and Pauson reported to Nature the synthesis of a new organometallic compound, ferrocene.1 In their original report, Pauson suggested a structure for ferrocene in which the iron is singly bonded (sigma bonds) to one carbon atom of each cyclopentadiene ligand (Figure 1, Structure I).1,2,3 This initial report led to wide-spread interest in the structure of ferrocene, and many leading scientists participated...
FAQs
These components organize engineering meaning at complementary levels. Schemas, taxonomies, and ontologies provide defined structures, concepts, and semantics, while knowledge graphs connect those elements through explicit relationships. Together, they allow engineering information to move beyond isolated records, supporting consistent interpretation, relationship-based queries, and automated reasoning across connected data.
They make the meaning and permitted use of engineering information visible to both people and computational systems. Relationships show how entities connect, attributes describe relevant properties, and rules allow constraints to be applied. This reduces ambiguity and duplication while enabling systems to check connections, retrieve related information, and support more reliable reasoning.
It links design documents, sensor information, simulations, and operational records to defined meanings rather than treating each source as an isolated collection of terms. Shared entities, attributes, and relationships provide a common interpretive structure. That consistency improves interoperability and helps engineering teams trace information across data-driven workflows and lifecycle decisions.
An engineering workflow can begin by identifying relevant entities, relationships, attributes, and rules, then representing them through an appropriate schema, taxonomy, ontology, or knowledge graph. The resulting structure can connect design, sensing, simulation, and operational information. Systems can then query relationships, apply constraints, and use the organized information in engineering tasks.
Its applications include requirements management, digital twins, fault diagnosis, design reuse, and lifecycle decision-making. In requirements work, connected information supports traceability; in diagnosis, relationships help link operational records to relevant engineering entities. Digital twins and design reuse likewise benefit from integrated information that can be interpreted consistently across engineering activities.
By connecting information from design through operation, the approach preserves relationships among requirements, models, observations, and operational records. Engineers can use those connections to trace decisions, reuse relevant design knowledge, support fault diagnosis, and interpret lifecycle information consistently. The result is stronger interoperability and more reliable data-driven engineering workflows rather than disconnected records.