Answering Complex Logical Queries on Knowledge Graphs via Query Computation Tree Optimization
Authors: Yushi Bai, Xin Lv, Juanzi Li, Lei Hou
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on 3 datasets show that QTO obtains state-of-the-art performance on complex query answering, outperforming previous best results by an average of 22%. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Technology, BNRist; KIRC, Institute for Artificial Intelligence; Tsinghua University, Beijing 100084, China. Correspondence to: Lei Hou <houlei@tsinghua.edu.cn>. |
| Pseudocode | Yes | Algorithm 1 Forward Propagation Function Algorithm 2 Backward Propagation Function Algorithm 3 Query Computation Tree Optimization |
| Open Source Code | Yes | The code of our paper is at https://github.com/bys0318/QTO. |
| Open Datasets | Yes | We experiment on three knowledge graph datasets, including FB15k (Bordes et al., 2013), FB15k237 (Toutanova & Chen, 2015), NELL995 (Xiong et al., 2017). |
| Dataset Splits | Yes | Specifically, in valid/test set, the easy answers are the entities that can be inferred by edges in training/valid graph, while hard answers are those that can be inferred by predicting missing edges in valid/test graph. Table 6 summarizes the statistics of the three datasets in our experiments. Dataset #Entities #Relations #Training edges #Valid edges #Test edges |
| Hardware Specification | Yes | Table 5. Inference time (ms/query) on each type of query on FB15k-237, evaluated on one RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions software components like 'Compl Ex', 'N3 regularizor', and points to a KGE implementation ('https://github.com/facebookresearch/ssl-relation-prediction'), but it does not provide specific version numbers for these software dependencies (e.g., PyTorch version, specific library versions). |
| Experiment Setup | Yes | We provide the best hyperparameters of the pretrained KGE 5 and QTO in Table 7. The hyperparameters for KGE, which is a Compl Ex model (Trouillon et al., 2016) trained with N3 regularizor (Lacroix et al., 2018) and auxiliary relation prediction task (Chen et al., 2021), include embedding dimension d, learning rate lr, batch size b, regularization strength λ, auxiliary relation prediction weight w, and the number of epochs. We recall that the hyperparameters in our QTO method include the threshold ϵ and the negation scaling coefficient α. |