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InductWave: Inductive Multi-Hop Logical Query Answe... | AI Research

Key Takeaways

  • InductWave: Inductive Multi-Hop Logical Query Answering on Knowledge Graphs Knowledge Graphs (KGs) are essential for extracting information across diverse fi...
  • 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.
Paper AbstractExpand

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 InductWave, a wavelet-based inductive embedding method for logical query answering on large KGs. Here, the training graph consists of fewer nodes than the test graph. Our model performs on par with the baseline models while having half the number of message-passing layers. It outperforms all of them in most cases, with 75% of the layers. These fewer resource requirements enable us to evaluate InductWave on massive graphs, such as Wiki-KG. We test our model using extensive experiments across varying train-test graph proportions of the FB15k-(237) dataset, comparing it with the state-of-the-art models. The code and datasets for the model are available at this https URL .

InductWave: Inductive Multi-Hop Logical Query Answering on Knowledge Graphs
Knowledge Graphs (KGs) are essential for extracting information across diverse fields like healthcare and finance, but they often struggle with incomplete data. While many existing AI models can answer complex logical queries over these graphs, they are typically "transductive," meaning they can only reason about nodes they have already seen during training. This makes them impractical for massive, real-world graphs where training on every node is resource-prohibitive. InductWave addresses this by providing an inductive approach that can reason over unseen nodes, allowing it to scale to massive datasets like Wiki-KG while using fewer computational resources than current state-of-the-art models.

The Challenge of Inductive Reasoning

Most current neural methods for logical query answering require the model to be trained on the entire graph. If a new node is added, these models fail because they lack the necessary training. Inductive methods aim to solve this by training on a smaller subgraph and then applying that knowledge to larger, unseen graphs. While previous inductive models exist, they often rely on memory-intensive processes that make them difficult to scale. InductWave bridges this gap by using a more efficient architecture that maintains high performance while significantly reducing the number of message-passing layers required.

How InductWave Works

The core of InductWave is a novel message-passing algorithm called WAVBFNet. This method combines two key technologies:

  • Graph Wavelets: These capture the structural context of a node—essentially how it fits into its neighborhood—using heat-kernel-based information flow. By incorporating these wavelets, the model gains a deeper understanding of the graph's structure.

  • Neural Bellman-Ford Network (NBF-Net): This is used for link prediction, which helps the model determine the likelihood of connections between nodes.
    By integrating graph wavelet embeddings with NBF-Net, the model can perform relation projections more effectively. Additionally, the researchers extended a method called GE-SpMM to make these computations compatible with GPU hardware, which drastically reduces memory usage and allows the model to handle graphs with millions of nodes.

Performance and Efficiency

InductWave demonstrates that it can perform on par with—and in many cases outperform—existing baseline models while using only 75% of the message-passing layers. By requiring fewer layers and less memory, the model achieves a more efficient balance between accuracy and resource consumption. The researchers validated these findings through extensive experiments on the FB15k-237 dataset, testing the model across various proportions of training and test data, and successfully applying it to the massive Wiki-KG dataset.

Key Considerations

InductWave is designed specifically for logical query answering, which involves processing First Order Logic (FOL) operators like conjunction, disjunction, and negation. It treats these queries as operations on "fuzzy sets" of entities, which helps improve the interpretability of the results. Because the model is inductive, it is particularly well-suited for real-world scenarios where knowledge graphs are constantly expanding and it is not feasible to retrain a model every time new information is added.

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