arXiv:2607.07422v1 Announce Type: new Abstract: Logical Multi-Hop Query Answering over Knowledge Graphs (KGs) can be formulated as querying, with an implicit completeness assumption. Current works mainly focus on Existential First Order Logic (EFO) queries. These EFO queries contain conjunction, disjunction, and negation operators. Most existing works employ transductive reasoning, meaning they are not capable of reasoning over entities unseen during training. In the real world, there is a resource scarcity, and we cannot train a model with all the nodes of a large KG. Hence, we propose Induct
The continuous growth of knowledge graphs in AI applications necessitates more robust and scalable reasoning methods, especially for large and dynamically evolving datasets.
Improving inductive reasoning in AI allows models to generalize to unseen data, which is critical for real-world deployment in resource-constrained or constantly updating environments.
This research introduces a novel approach that enables AI systems to perform logical query answering on knowledge graphs without retraining on new entities, moving beyond current transductive limitations.
- · AI researchers and developers
- · Companies using large knowledge graphs
- · Sectors requiring dynamic AI inference (e.g., healthcare, finance)
- · AI models reliant solely on transductive reasoning
- · Systems with high retraining costs for new data
More efficient and adaptable AI models for complex data structures like knowledge graphs.
Accelerated development of AI agents capable of reasoning over evolving information landscapes.
Enhanced operational autonomy for AI systems in scenarios where data is constantly updating and partially observable.
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Read at arXiv cs.AI