Summary of One Model, Any Conjunctive Query: Graph Neural Networks For Answering Complex Queries Over Knowledge Graphs, by Krzysztof Olejniczak et al.
One Model, Any Conjunctive Query: Graph Neural Networks for Answering Complex Queries over Knowledge Graphs
by Krzysztof Olejniczak, Xingyue Huang, İsmail İlkan Ceylan, Mikhail Galkin
First submitted to arxiv on: 21 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper proposes AnyCQ, a graph neural network model that can classify answers to any conjunctive query on any knowledge graph. The model is trained using a reinforcement learning objective to answer Boolean queries and can generalize to large queries of arbitrary structure. It outperforms existing approaches on new and challenging benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to solve the problem of query answering over incomplete knowledge graphs. By predicting answers that may not appear in the graph, but are present in its completion, it helps improve the accuracy of query results. The model, called AnyCQ, is a graph neural network that can classify and retrieve answers to queries. It’s trained on small instances and then tested on larger, more complex queries. |
Keywords
» Artificial intelligence » Graph neural network » Knowledge graph » Reinforcement learning
