TempoQR: Temporal Question Reasoning over Knowledge Graphs
Authors: Costas Mavromatis, Prasanna Lakkur Subramanyam, Vassilis N. Ioannidis, Adesoji Adeshina, Phillip R Howard, Tetiana Grinberg, Nagib Hakim, George Karypis5825-5833
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that Tempo QR improves accuracy by 25 45 percentage points on complex temporal questions over state-of-the-art approaches and it generalizes better to unseen question types. |
| Researcher Affiliation | Collaboration | 1University of Minnesota 2University of Massachusetts Amherst 3Amazon Web Services 4Intel Labs |
| Pseudocode | No | The paper describes its methods using prose and mathematical equations but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | For reproducibility, our code is available at: https://github.com/cmavro/Tempo QR. |
| Open Datasets | Yes | Cron Questions (Saxena, Chakrabarti, and Talukdar 2021) is a temporal QA benchmark based on the Wikidata TKG proposed in (Lacroix, Obozinski, and Usunier 2020). |
| Dataset Splits | Yes | Cron Questions consists of 410k unique question-answer pairs, 350k of which are for training and 30k for validation and for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU/GPU models or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions software like PyTorch, TComplEx, BERT, and RoBERTa, but does not specify their version numbers, which is required for reproducible software dependencies. |
| Experiment Setup | Yes | We learn TKG embeddings with the TCompl Ex method, where we set their dimensions D = 512. During, QA the pre-trained LM s parameters and the TKG embeddings are not updated. We set the number of transformer layers of the encoder f( ) to l = 6 with 8 heads per layer. We also observed the same performance when setting l = 3 with 4 heads per layer. The model s parameters are updated with Adam (Kingma and Ba 2014) with a learning rate of 0.0002. The model is trained for 20 maximum epochs and the final parameters are determined based on the best validation performance. |