Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Entity Alignment with Reliable Path Reasoning and Relation-aware Heterogeneous Graph Transformer
Authors: Weishan Cai, Wenjun Ma, Jieyu Zhan, Yuncheng Jiang
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate the performance of RPR-RHGT on three widely used benchmark datasets. The code is now available at https://github.com/cwswork/RPR-RHGT. [...] Extensive experiments on three well-known datasets show RPR-RHGT significantly outperforms 10 state-of-the-art methods, exceeding the best performing baseline up to 8.62% on Hits@1. |
| Researcher Affiliation | Academia | 1School of Computer Science, South China Normal University, China 2School of Computer and Information Engineering, Hanshan Normal University, China 3School of Artificial Intelligence, South China Normal University, China EMAIL, EMAIL, zhanjieyu,EMAIL |
| Pseudocode | Yes | Algorithm 1 Procedure of RPR Algorithm. |
| Open Source Code | Yes | The code is now available at https://github.com/cwswork/RPR-RHGT. |
| Open Datasets | Yes | Three experimental datasets contain crosslingual datasets and mono-lingual dataset: WK31-15K [Sun et al., 2020b] is from multi-lingual DBpedia and used to evaluate model performance on sparse and dense datasets, where each subset contains two versions: V1 is sparse set obtained by using IDS algorithm, and V2 is twice as dense as V1. DBP-15K [Sun et al., 2017] is the most used dataset in the literature, and is also from DBpedia. DWY-100K [Sun et al., 2018] contains two mono-lingual KGs, which serve as large-scale datasets to better evaluate the scalability of experimental models. |
| Dataset Splits | Yes | For WK31-15K and DBP-15K, the proportion of train, validation and test is 2:1:7, the same as [Sun et al., 2020b]. For DWY-100K, we adopt the same train (30%) / test (70%) split as baselines. |
| Hardware Specification | Yes | The results running on a workstation with CPU (EPYC 3975WX +256G RAM) and GPU (RTX A4000 with 16G) are shown in Table 4, which shows large differences between different methods. |
| Software Dependencies | No | We use fast Text 1 to generate entity name embeddings that are uniformly applied to baseline recurrence, including RDGCN, NMN, RAGA, Multi KE and COTSAE. 1https://fasttext.cc/docs/en/crawl-vectors.html |
| Experiment Setup | Yes | For all datasets, we use the same weight hyper-parameters: τ sim = 0.5, τ path = 20, hn =4, γ1 =γ2 =10, θ = 0.3. The embedding dimensions of 15K and 100K datasets are 300 and 200, respectively. |