Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs
Authors: Hongyu Ren, Jure Leskovec
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experiments on standard KG datasets and compare BETAE to prior approaches [9, 10] that can only handle EPFO queries. Experiments show that our model BETAE is able to achieve state-of-the-art performance in handling arbitrary conjunctive queries (including , ) with a relative increase of the accuracy by up to 25.4%. |
| Researcher Affiliation | Academia | Hongyu Ren Stanford University hyren@cs.stanford.edu Jure Leskovec Stanford University jure@cs.stanford.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. It describes methods using mathematical formulas and prose. |
| Open Source Code | Yes | Project website with data and code can be found at http://snap.stanford.edu/betae. |
| Open Datasets | Yes | We use three standard KGs with official training/validation/test edge splits, FB15k [4], FB15k-237 [35] and NELL995 [27] and follow [10] for the preprocessing. |
| Dataset Splits | Yes | We use three standard KGs with official training/validation/test edge splits, FB15k [4], FB15k-237 [35] and NELL995 [27] and follow [10] for the preprocessing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact CPU/GPU models, memory, or cloud computing specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions that hyperparameters, architectures, and more details are in Appendix D, which is not provided. No specific software names with version numbers are mentioned in the main text provided. |
| Experiment Setup | Yes | We ran each method for 3 different random seeds after finetuning the hyperparameters. We list the hyperparameters, architectures and more details in Appendix D. |