Dynamic Knowledge Graph Alignment
Authors: Yuchen Yan, Lihui Liu, Yikun Ban, Baoyu Jing, Hanghang Tong4564-4572
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive evaluations are conducted on the benchmark DBP15K (Sun, Hu, and Li 2017) datasets. In the static setting, the proposed DINGAL-B model consistently outperforms 14 state-of-the-art methods. In the dynamic setting, the proposed DINGALO and DINGAL-U are (1) 10 faster and better than the existing static alignment methods; and (2) 10 to 100 faster than their static counterpart (DINGAL-B) with little alignment accuracy loss. |
| Researcher Affiliation | Academia | Yuchen Yan, Lihui Liu, Yikun Ban, Baoyu Jing, Hanghang Tong University of Illinois at Urbana-Champaign, Urbana, IL, USA {yucheny5, lihuil2, yikunb5, baoyuj, htong}@illinois.edu |
| Pseudocode | No | The paper describes the algorithms using mathematical equations and prose, but it does not include a clearly labeled pseudocode block or algorithm section. |
| Open Source Code | No | The paper states: "The implementation of the first 6 baseline methods are from EAkit, an open-source entity alignment toolkit." This refers to the baselines, not the authors' own implementation of DINGAL. |
| Open Datasets | Yes | We use DBP15K (Sun, Hu, and Li 2017) benchmark datasets, including DBP15KZH EN, DBP15KJA EN and DBP15KF R EN built on Chinese, English, Japanese and French versions of DBpedia. Each dataset provides two KGs in different languages with 15K pre-aligned entity pairs. |
| Dataset Splits | No | The paper mentions 30% for training and 70% for test splits in static and dynamic settings, but does not explicitly define a validation split or how it's used for reproduction. |
| Hardware Specification | Yes | The experiment are run on a 1080Ti GPU. |
| Software Dependencies | No | The paper does not specify the versions of any software dependencies (e.g., Python, PyTorch, TensorFlow, etc.) used for the experiments. |
| Experiment Setup | Yes | The epoch number is set as 1500. The number of negative samples for each positive sample is 125. The learning rate is 0.001. We use a two-layer DINGAL-B in the experiment. The margin hyper-parameter γ in Equation (7) is 1. The embedding dimension is 300. |