Large language Models-empowered automatic knowledge graph development based on multi-modal data for building health resilience

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AUCSR4DIR" target="_blank" >RIV/00216208:11320/26:UCSR4DIR - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1016/j.aei.2025.103655" target="_blank" >http://dx.doi.org/10.1016/j.aei.2025.103655</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.aei.2025.103655" target="_blank" >10.1016/j.aei.2025.103655</a>

Result language

angličtina

Original language name

Large language Models-empowered automatic knowledge graph development based on multi-modal data for building health resilience

Original language description

Improving the health resilience of building (BHR) helps keep stable health status of both the building and its occupants under disasters. As BHR is an emerging concept, there is no structured knowledge graph to understand the whole process of BHR under disasters. Therefore, this study aims to build a structured BHR knowledge graph based on multi-modal data, providing sufficient structured knowledge for BHR enhancement. An automated knowledge graph construction approach is proposed to empower the ontology design and triple extraction by large language models (LLMs), and validation processes based on In-context Learning (ICL) prompts. A case study is conducted to construct the knowledge graph of BHR under rainstorms in Hong Kong. The performance of the proposed LLMs-empowered knowledge extraction is also validated based on natural language processing metrics and LLMs-based Evaluation (LLMs-Eval). BHR knowledge graph indicates the potential relations between disasters, factors, response actions, and the health status of the building and occupants, and provides insight to guide the BHR enhancement. The superiority of the proposed LLMs-empowered automated knowledge graph construction approach is proven, implying LLMs have great potential in knowledge graph construction, not only for BHR but also for other concepts that require structured knowledge for further explorations and analyses. © 2025 Elsevier Ltd

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

—

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Advanced Engineering Informatics

ISSN

1474-0346

e-ISSN

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Volume of the periodical

68

Issue of the periodical within the volume

2025

Country of publishing house

US - UNITED STATES

Number of pages

8

Pages from-to

103655

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-105010563058