Enterprise Knowledge Management
Enterprise Knowledge Management - Related Videos
01:30
Behavioral Enterprise Decision-Making
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2026
Behavioral enterprise decision-making combines psychology, behavioral economics, and management science insights to address complex organizational challenges, consumer behavior, and societal impacts. Traditional decision-making models assume that individuals act rationally, maximizing utility based on available information. However, behavioral decision-making recognizes that human biases, emotions, and social influences often shape managerial choices, leading to opportunities and inefficiencies.
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 •
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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.
03:47
Workflow and Framework for Collecting and Implementing Point-of-Care Ultrasound Data in the Management of Heart Failure Patients
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2024
This protocol presents a scalable workflow for the use of point-of-care ultrasound in the management of hospitalized and outpatient heart failure patients.
01:34
Early Intervention and Pain Management
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2026
All animal procedures described here must be conducted in accordance with institutional animal ethics guidelines and approved by IACUC. All procedures must follow the principles of the 3Rs—Replacement, Reduction, and Refinement—and must be performed by trained personnel. Monitoring Animals for Signs of Pain and Distress Recognizing early signs of pain is essential for ethical animal research. Animals are routinely monitored using behavioral, physical, and physiological parameters that reflect...
FAQs
The system works by linking three functions: people, processes, and technology. People contribute expertise; processes determine how project information is captured and maintained; and technologies make it findable through repositories, taxonomies, search tools, and collaboration workflows. This connection turns knowledge that might remain with one engineer or team into material that others can use for decisions and performance.
Taxonomies and search tools solve different parts of the access problem. A taxonomy gives engineering information a consistent organizational structure, while search helps users locate relevant content within that structure. Used with repositories, these tools make design records, standards, test results, and other project knowledge easier to retrieve across teams, reducing repeated searches and duplicated work.
Continuity improves when expertise is captured before it remains confined to an individual or project team. Enterprise Knowledge Management provides a shared way to preserve lessons learned, technical records, and maintenance information as personnel or assignments change. That continuity helps later engineers make informed decisions without rebuilding knowledge that the organization has already developed.
A practical engineering workflow begins by recording information as work is created, then capturing it in a shared repository, organizing it with an appropriate taxonomy, and making it searchable. Collaboration workflows help teams contribute and use the material, while later projects draw on design records, standards, test results, lessons learned, and maintenance data. This sequence connects knowledge across projects rather than leaving it isolated.
Engineering teams can connect design records with standards, test results, lessons learned, and maintenance data. Bringing these categories together gives project and maintenance work a broader information base, allowing relevant experience and prior results to inform subsequent decisions. The cross-project view is especially useful when different teams need access to information produced elsewhere in the organization.
The approach is useful wherever engineers must support quality, regulatory requirements, maintenance, problem solving, or innovation. Accessible institutional knowledge can reduce duplicated effort and help teams locate prior information instead of recreating it. In practice, its value appears both in routine continuity, such as preserving maintenance data, and in improvement work that uses lessons learned to guide future projects.