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  4. Vol 10, Issue 2, 2026

Construction of a Performance Arts Knowledge Graph Based on Large Language Models and Its Geospatial Distribution Analysis

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DOI: 10.23977/acss.2026.100212 | Downloads: 0 | Views: 64

Author(s)

Hualing Gao 1, Pingping Jiao 1, Jiaojiao Lu 1

Affiliation(s)

1 School of Information and Intelligence Engineering, University of Sanya, Sanya, 572022, China

Corresponding Author

Hualing Gao

ABSTRACT

Performance arts data is characterized by unstructured formats, multi-source heterogeneity, and sparse spatio-temporal distribution, rendering traditional management methods insufficient for revealing the underlying complex social networks and resource allocation patterns. This paper presents a framework for the construction and analysis of a performance arts knowledge graph powered by Large Language Models (LLMs). The approach utilizes LLMs to extract named entities and relational structures from unstructured performance records, integrated with an entity resolution algorithm to address naming redundancy and inconsistencies. A knowledge graph is constructed via the Neo4j graph database, where degree centrality and spatial radiation indices are applied to analyze core patterns in the performance market regarding subject distribution and geographical circulation. Experimental results indicate that the performance arts market exhibits a significant power-law distribution, with core leading entities demonstrating a strong spatial correlation between their total performance frequency and geographical reach.

KEYWORDS

Knowledge Graph, Large Language Models (LLM), Performance Arts, Entity Resolution, Visual Analysis

CITE THIS PAPER

Hualing Gao, Pingping Jiao, Jiaojiao Lu. Construction of a Performance Arts Knowledge Graph Based on Large Language Models and Its Geospatial Distribution Analysis. Advances in Computer, Signals and Systems (2026). Vol. 10, No. 2, 111-116. DOI: http://dx.doi.org/10.23977/acss.2026.100212.

REFERENCES

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[3] Shen W, Wang J, Han J. Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions[J]. Knowledge & Data Engineering IEEE Transactions on, 2015, 27(2):443-460.
[4] Pan S, Luo L, Wang Y, et al. Unifying Large Language Models and Knowledge Graphs: A Roadmap[J]. IEEE Transactions on Automatic Control, 2024, 36(7):20.
[5] Barabási, Albert-László. Scale-Free Networks: A Decade and Beyond [J]. Science, 2009. 

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