Augmenting Knowledge Graph Hierarchies Using Neural Transformers (2404.08020v1)
Abstract: Knowledge graphs are useful tools to organize, recommend and sort data. Hierarchies in knowledge graphs provide significant benefit in improving understanding and compartmentalization of the data within a knowledge graph. This work leverages LLMs to generate and augment hierarchies in an existing knowledge graph. For small (<100,000 node) domain-specific KGs, we find that a combination of few-shot prompting with one-shot generation works well, while larger KG may require cyclical generation. We present techniques for augmenting hierarchies, which led to coverage increase by 98% for intents and 99% for colors in our knowledge graph.
- Iterative Zero-Shot LLM Prompting for Knowledge Graph Construction. arXiv:2307.01128 [cs.CL] https://arxiv.org/abs/2307.01128
- Knowledge Vault: A Web-Scale Approach to Probabilistic Knowledge Fusion. In The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’14, New York, NY, USA, August 24-27, 2014. 601–610. http://www.cs.cmu.edu/~nlao/publication/2014.kdd.pdf
- Google. [n. d.]. Google Product Type Taxonomy. ([n. d.]). https://www.google.com/basepages/producttype/taxonomy.en-US.txt 2021-09-21 version.
- LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT. arXiv:2307.06917 [cs.AI] https://arxiv.org/abs/2307.06917
- OpenAI. 2023. GPT-4 Technical Report. arXiv:2303.08774 [cs.CL] https://arxiv.org/abs/2303.08774
- Unifying Large Language Models and Knowledge Graphs: A Roadmap. arXiv:2306.08302 [cs.CL] https://arxiv.org/abs/2306.08302
- Archit Parnami and Minwoo Lee. 2022. Learning from Few Examples: A Summary of Approaches to Few-Shot Learning. arXiv:2203.04291 [cs.LG]
- A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities. arXiv:2205.06743 [cs.LG] https://arxiv.org/abs/2205.06743
- Frans Stokman and Pieter Vries. 1988. Structuring Knowledge in a Graph. In Human-Computer Interaction, Gerrit C. van der Veer and Gijsbertus Mulder (Eds.). Springer, 186–206. https://doi.org/10.1007/978-3-642-73402-1_12
- Llama 2: Open Foundation and Fine-Tuned Chat Models. arXiv:2307.09288 [cs.CL] https://arxiv.org/abs/2307.09288
- The Anatomy of the Facebook Social Graph. CoRR abs/1111.4503 (2011). arXiv:1111.4503 http://arxiv.org/abs/1111.4503
- Graph Convolutional Neural Networks for Web-Scale Recommender Systems. CoRR abs/1806.01973 (2018). arXiv:1806.01973 http://arxiv.org/abs/1806.01973
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