knowledge fusion ↔ Large Language Models

Relations (1)

related 2.00 — strongly supporting 3 facts

Large Language Models are utilized as a core methodology for knowledge fusion to construct unified knowledge skeletons and align instance-level data as described in [1]. Furthermore, Large Language Models are shifting the paradigm of knowledge graph construction, which includes knowledge fusion, toward generative frameworks [2], and knowledge fusion serves as a primary technical paradigm when integrating these models with knowledge graphs for complex tasks [3].

Facts (3)

Sources

LLM-empowered knowledge graph construction: A survey - arXiv arxiv.org arXiv 2 facts

claimLarge Language Models are transforming Knowledge Graph construction by shifting the paradigm from rule-based and modular pipelines toward unified, adaptive, and generative frameworks across ontology engineering, knowledge extraction, and knowledge fusion.

claimMethodologies leveraging Large Language Models for knowledge fusion address challenges at two fundamental levels: constructing a unified and normalized knowledge skeleton at the schema layer, and integrating and aligning specific knowledge at the instance layer.

Large Language Models Meet Knowledge Graphs for Question ... arxiv.org arXiv 1 fact

claimKnowledge graphs typically function as background knowledge when synthesizing large language models for complex question answering, with knowledge fusion and retrieval-augmented generation (RAG) serving as the primary technical paradigms.