Summary of A Foundation Model For Zero-shot Logical Query Reasoning, by Mikhail Galkin et al.
A Foundation Model for Zero-shot Logical Query Reasoning
by Mikhail Galkin, Jincheng Zhou, Bruno Ribeiro, Jian Tang, Zhaocheng Zhu
First submitted to arxiv on: 10 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents UltraQuery, a foundation model for inductive reasoning that can answer logical queries on any knowledge graph (KG) without requiring substantial training time. Existing methods learn parameters bound to specific entity or relation vocabularies and are limited to the graph they were trained on. UltraQuery instead derives vocabulary-independent functions for both projections and logical operations, enabling zero-shot query answering on new KGs. The model is initialized with a pre-trained inductive KG reasoning model and fine-tuned on a single dataset. In experiments on 23 datasets, UltraQuery achieves competitive or better performance than best available baselines and sets a new state-of-the-art on 15 of them. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about building a super smart computer model that can answer complex questions about data stored in special databases called knowledge graphs. Right now, computers need to learn from each dataset separately, which takes a long time. This new model, UltraQuery, can learn once and then answer questions on any database without needing more training. It’s like a magic formula that makes the computer very good at understanding complex relationships between things. The researchers tested this model on many different datasets and found it works really well, beating other models in most cases. |
Keywords
» Artificial intelligence » Knowledge graph » Zero shot
