Knowledge Base Construction
Knowledge Base Construction - 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 1 •
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.
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.
06:19
Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Cited by 3 •
2025
Mime is a flexible computational framework to construct a machine learning-based integration model with elegant performance. Here, we provide a detailed step-by-step procedure for developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with disease progression, patient outcomes, and therapeutic response.
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.
10:25
Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
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Cited by 1 •
2016
We present here a protocol to construct and validate models for nondestructive prediction of total sugar, total organic acid, and total anthocyanin content in individual blueberries by near-infrared spectroscopy.
FAQs
Domain scope determines which engineering concepts and sources belong in the system, while entity and relationship models specify how those concepts connect. Encoding these decisions in a structured schema makes the information machine-readable and supports consistent retrieval. Validation then checks whether entries conform to the schema, helping maintain dependable data as the knowledge base grows.
Search indexes and ontologies contribute different forms of organization. An index supports efficient retrieval of stored information, whereas an ontology represents concepts and their relationships within a domain. Rule-based methods can apply explicitly encoded logic, while machine-learning methods can support automated reasoning. Combining these mechanisms lets an engineering system retrieve information and use modeled structure.
Version control is important because engineering knowledge changes as technical documents, databases, and designs are updated. Recording revisions makes it possible to manage successive states of the structured knowledge base alongside validation checks. This supports controlled maintenance and helps teams keep retrieval, troubleshooting, design work, and technical documentation aligned with the currently accepted technical information.
A practical construction workflow begins by defining the engineering domain scope, followed by extracting facts from technical documents or databases. The extracted information is modeled as entities and relationships, encoded in a schema, and checked through validation. Version control then manages changes, after which search indexes, ontologies, rules, or machine-learning methods can support retrieval and reasoning.
It is useful when information must support design decisions, troubleshooting, system integration, or technical documentation through reliable retrieval. The structured representation also provides a foundation for expert systems, intelligent assistants, and data-driven engineering workflows that need organized technical knowledge. This approach is especially relevant when teams must apply information consistently across multiple engineering activities.
Representing engineering entities and their relationships in a common structured form helps connect information across systems. That organization supports integration as well as retrieval, allowing technical information to contribute to design, troubleshooting, and documentation workflows. In broader computational settings, the same foundation can support automated reasoning through explicitly encoded rules or machine-learning methods.