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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.25724 |
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| _version_ | 1866908619213111296 |
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| author | Arikutharam, Vanya Ukolov, Arkadiy |
| author_facet | Arikutharam, Vanya Ukolov, Arkadiy |
| contents | Retrieval-Augmented Generation allows LLMs to access external knowledge, reducing hallucinations and ageing-data issues. However, it treats retrieved chunks independently and struggles with multi-hop or relational reasoning, especially across documents. Knowledge graphs enhance this by capturing the relationships between entities using triplets, enabling structured, multi-chunk reasoning. However, these tend to miss information that fails to conform to the triplet structure. We introduce BambooKG, a knowledge graph with frequency-based weights on non-triplet edges which reflect link strength, drawing on the Hebbian principle of "fire together, wire together". This decreases information loss and results in improved performance on single- and multi-hop reasoning, outperforming the existing solutions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_25724 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BambooKG: A Neurobiologically-inspired Frequency-Weight Knowledge Graph Arikutharam, Vanya Ukolov, Arkadiy Artificial Intelligence Retrieval-Augmented Generation allows LLMs to access external knowledge, reducing hallucinations and ageing-data issues. However, it treats retrieved chunks independently and struggles with multi-hop or relational reasoning, especially across documents. Knowledge graphs enhance this by capturing the relationships between entities using triplets, enabling structured, multi-chunk reasoning. However, these tend to miss information that fails to conform to the triplet structure. We introduce BambooKG, a knowledge graph with frequency-based weights on non-triplet edges which reflect link strength, drawing on the Hebbian principle of "fire together, wire together". This decreases information loss and results in improved performance on single- and multi-hop reasoning, outperforming the existing solutions. |
| title | BambooKG: A Neurobiologically-inspired Frequency-Weight Knowledge Graph |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2510.25724 |