CORE-KG reduces node duplication by 33.28% and legal noise by 38.37% versus a GraphRAG baseline on 20 human smuggling court cases, through type-aware LLM coreference resolution and domain-filtered extraction prompts.
Duplication Detection in Knowledge Graphs: Literature and Tools
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abstract
In recent years, an increasing amount of knowledge graphs (KGs) have been created as a means to store cross-domain knowledge and billion of facts, which are the basis of costumers' applications like search engines. However, KGs inevitably have inconsistencies such as duplicates that might generate conflicting property values. Duplication detection (DD) aims to identify duplicated entities and resolve their conflicting property values effectively and efficiently. In this paper, we perform a literature review on DD methods and tools, and an evaluation of them. Our main contributions are a performance evaluation of DD tools in KGs, improvement suggestions, and a DD workflow to support future development of DD tools, which are based on desirable features detected through this study.
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CORE-KG: An LLM-Driven Knowledge Graph Construction Framework for Human Smuggling Networks
CORE-KG reduces node duplication by 33.28% and legal noise by 38.37% versus a GraphRAG baseline on 20 human smuggling court cases, through type-aware LLM coreference resolution and domain-filtered extraction prompts.