Jian Tang, Zhaocheng Zhu, Zuobai Zhang, Mikhail Galkin
We lifted 4 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| DeepGraphLearning/GNN-QE | canonical | 1 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| computation_graph_layout | Ran | DeepGraphLearning/GNN-QE/script/visualize.py code served (permissive licence) · get_code("6b999fe6d54461d0") |
| detect_variables | Not yet run | DeepGraphLearning/GNN-QE/gnnqe/util.py code served (permissive licence) · get_code("eab8b6a6ced6d93e") |
| get_root_logger | Not yet run | DeepGraphLearning/GNN-QE/gnnqe/util.py code served (permissive licence) · get_code("70a68fd646c6e4a7") |
| get_topk_prediction | Not yet run | DeepGraphLearning/GNN-QE/script/visualize.py code served (permissive licence) · get_code("0fdf31f60a2c67ea") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Answering complex first-order logic (FOL) queries on knowledge graphs is a fundamental task for multi-hop reasoning. Traditional symbolic methods traverse a complete knowledge graph to extract the answers, which provides good interpretation for each step. Recent neural methods learn geometric embeddings for complex queries. These methods can generalize to incomplete knowledge graphs, but their reasoning process is hard to interpret. In this paper, we propose Graph Neural Network Query Executor (GNN-QE), a neural-symbolic model that enjoys the advantages of both worlds. GNN-QE decomposes a complex FOL query into relation projections and logical operations over fuzzy sets, which provides interpretability for intermediate variables. To reason about the missing links, GNN-QE adapts a graph neural network from knowledge graph completion to execute the relation projections, and models the logical operations with product fuzzy logic. Experiments on 3 datasets show that GNN-QE significantly improves over previous state-ofthe-art models in answering FOL queries. Meanwhile, GNN-QE can predict the number of answers without explicit supervision, and provide visualizations for intermediate variables. 1
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2205.10128")
get_code_for_paper("2205.10128")
have("2205.10128")
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