Structured Knowledge
Structured Knowledge - Related Videos
05:47
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Cited by 2 •
2025
This article describes RUGGED (Retrieval Under Graph-Guided Explainable disease Distinction), which integrates Large Language Model (LLM) inference with Retrieval-Augmented Generation (RAG). It draws evidence from expert-curated biomedical knowledge bases and peer-reviewed biomedical publications to synthesize new knowledge from up-to-date information, identify explainable and actionable predictions, and pinpoint promising directions for hypothesis-driven investigations.
07:35
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Cited by 2 •
2023
A computational protocol, CaseOLAP LIFT, and a use case are presented for investigating mitochondrial proteins and their associations with cardiovascular disease as described in biomedical reports. This protocol can be easily adapted to study user-selected cellular components and diseases.
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.
05:45
Uncovering Hidden Dynamics of Natural Photonic Structures Using Holographic Imaging
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Cited by 6 •
2022
The paper is primarily focused on the combined power of optical (linear and nonlinear) and holographic methods used to reveal phenomena at the nanoscale. The results obtained from the biophotonic and oscillatory chemical reactions' studies are given as representative examples, highlighting holography's ability to reveal dynamics at a nanoscale.
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.
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.