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LMGFuse: Language Models and Graph reasoning Fuse deeply for question answering

Research article (Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering, 2023) · cited 11× · AI/ML
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LMGFuse: Language Models and Graph reasoning Fuse deeply for question answering

Summary

LMGFuse: Language Models and Graph reasoning Fuse deeply for question answering is a scholarly article[1].

Key Facts

  • LMGFuse: Language Models and Graph reasoning Fuse deeply for question answering's instance of is recorded as scholarly article[2].

References

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Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). LMGFuse: Language Models and Graph reasoning Fuse deeply for question answering. Retrieved May 24, 2026, from https://4ort.xyz/entity/lmgfuse-language-models-and-graph-reasoning-fuse-deeply-for-question-answering
MLA “LMGFuse: Language Models and Graph reasoning Fuse deeply for question answering.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/lmgfuse-language-models-and-graph-reasoning-fuse-deeply-for-question-answering.
BibTeX @misc{4ortxyz_lmgfuse-language-models-and-graph-reasoning-fuse-deeply-for-question-answering_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{LMGFuse: Language Models and Graph reasoning Fuse deeply for question answering}}, year = {2026}, url = {https://4ort.xyz/entity/lmgfuse-language-models-and-graph-reasoning-fuse-deeply-for-question-answering}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): LMGFuse: Language Models and Graph reasoning Fuse deeply for question answering — https://4ort.xyz/entity/lmgfuse-language-models-and-graph-reasoning-fuse-deeply-for-question-answering (retrieved 2026-05-24)

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