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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

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

Popis výsledku anglicky

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

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

—

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Advanced Engineering Informatics

ISSN

1474-0346

e-ISSN

—

Svazek periodika

68

Číslo periodika v rámci svazku

2025

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

8

Strana od-do

103655

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-105010563058