Search engine for discovering works of Art, research articles, and books related to Art and Culture
Javascript must be enabled to continue!
AI-augmented systems engineering: conceptual application of retrieval-augmented generation for model-based systems engineering graph
View through CrossRef
ABSTRACT:This paper presents the MBSE-Graph-RAG framework to address key challenges in Model-Based Systems Engineering (MBSE). Traditional MBSE tools suffer from usability barriers, limited accessibility, and integration challenges. By combining knowledge graphs with Retrieval-Augmented Generation (RAG), the proposed framework enables AI-Augmented engineering through natural language interactions and automated system architecture generation. A systematic literature review establishes a solid research foundation, identifying gaps in AI-assisted MBSE. Key contributions include a structured MBSE-Graph interface, improved usability via Large Language Models (LLMs), and automated graph construction aligned with SysML. A proof-of-concept demonstrates the potential of this approach to enhance MBSE by reducing complexity, improving data accessibility, and supporting engineering collaboration.
Cambridge University Press (CUP)
Title: AI-augmented systems engineering: conceptual application of retrieval-augmented generation for model-based systems engineering graph
Description:
ABSTRACT:This paper presents the MBSE-Graph-RAG framework to address key challenges in Model-Based Systems Engineering (MBSE).
Traditional MBSE tools suffer from usability barriers, limited accessibility, and integration challenges.
By combining knowledge graphs with Retrieval-Augmented Generation (RAG), the proposed framework enables AI-Augmented engineering through natural language interactions and automated system architecture generation.
A systematic literature review establishes a solid research foundation, identifying gaps in AI-assisted MBSE.
Key contributions include a structured MBSE-Graph interface, improved usability via Large Language Models (LLMs), and automated graph construction aligned with SysML.
A proof-of-concept demonstrates the potential of this approach to enhance MBSE by reducing complexity, improving data accessibility, and supporting engineering collaboration.
Related Results
Graph-based Interactive Bibliographic Information Retrieval Systems
Graph-based Interactive Bibliographic Information Retrieval Systems
In the big data era, we have witnessed the explosion of scholarly literature. This explosion has imposed challenges to the retrieval of bibliographic information. Retrieval of inte...
Nanjing Yunjin intelligent question-answering system based on knowledge graphs and retrieval augmented generation technology
Nanjing Yunjin intelligent question-answering system based on knowledge graphs and retrieval augmented generation technology
Abstract
Nanjing Yunjin, a traditional Chinese silk weaving craft, is celebrated globally for its unique local characteristics and exquisite workmanship, forming an integ...
Abstract 902: Explainable AI: Graph machine learning for response prediction and biomarker discovery
Abstract 902: Explainable AI: Graph machine learning for response prediction and biomarker discovery
Abstract
Accurately predicting drug sensitivity and understanding what is driving it are major challenges in drug discovery. Graphs are a natural framework for captu...
Domination of Polynomial with Application
Domination of Polynomial with Application
In this paper, .We .initiate the study of domination. polynomial , consider G=(V,E) be a simple, finite, and directed graph without. isolated. vertex .We present a study of the Ira...
E-Cordial Labeling of Some Families of Graphs
E-Cordial Labeling of Some Families of Graphs
An E-cordial labeling σ: E →{0,1} induces σ∗: V →{0,1} on graph G=(V,E), where (σ(v)=(∑_(u∈V)▒〖σ(uv)〗) mod 2 is taken over all edges uv∈E, and the labelling satisfies the condition...
Multimodal Information Integration and Retrieval Framework Based on Graph Neural Networks
Multimodal Information Integration and Retrieval Framework Based on Graph Neural Networks
In the context of the rapid proliferation of multimodal data (e.g. text, image, audio), the effective integration and retrieval of information across different modalities has emerg...
Data Analytics on Graphs Part I: Graphs and Spectra on Graphs
Data Analytics on Graphs Part I: Graphs and Spectra on Graphs
The area of Data Analytics on graphs promises a paradigm shift, as we approach information processing of new classes of data which are typically acquired on irregular but structure...
Subgraph Mining
Subgraph Mining
The amount of available data is increasing very fast. With this data, the desire for data mining is also growing. More and larger databases have to be searched to find interesting ...