Authors: Mikhail Galkin, Jincheng Zhou, Bruno Ribeiro, Jian Tang, Zhaocheng Zhu

Published on: April 10, 2024

Impact Score: 7.4

Arxiv code: Arxiv:2404.07198

Summary

• What is new: UltraQuery introduces a method for answering complex logical queries on knowledge graphs without needing retraining for each new graph.
• Why this is important: Current complex logical query answering methods are tightly bound to specific knowledge graphs and require extensive training for each new graph.
• What the research proposes: UltraQuery, an inductive reasoning model that generalizes to any knowledge graph using vocabulary-independent functions for projections and logical operations, requiring only fine-tuning on a single dataset.
• Results: When tested across 23 datasets in zero-shot inference mode, UltraQuery either matched or surpassed the performance of existing baselines, establishing a new state of the art on 14 datasets.

Technical Details

Technological frameworks used: nan

Models used: Inductive reasoning model with vocabulary-independent functions for CLQA

Data used: 23 diverse datasets for evaluating query answering performance

Potential Impact

Data analytics firms, scholars using knowledge graphs, AI-driven query answering services, and companies reliant on dynamic knowledge graph updating could all benefit or face disruption.

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