# Efficient and Effective Unsupervised Entity Alignment in Large Knowledge Graphs
## 5 Experiments  
### Experiment Settings  
**Datasets.** To comprehensively evaluate the performance of _MPGT-Align_, we conduct experiments on two widely recognized benchmark datasets: (1) _DBP-15K_[53] is the most commonly used dataset in the literature, comprises three cross-lingual subsets from DBpedia: Chinese-English (_ZH-EN_), Japanese-English (_JA-EN_), and French-English (_FR-EN_), each containing 15,000 pairs of aligned entities; and (2) _DWY-100K_[54] is a large monolingual dataset derived from DBpedia, Wikidata, and YAGO3 that allows for a more robust evaluation of model scalability, while _DWY-100K_ consists of two subsets: DBpedia-Wikidata (_DBP-WD_) and DBpedia-YAGO3 (_DBP-YG_), each containing 100,000 pairs of aligned entities and nearly 1 million triples. Detailed statistics are presented in Table 1.  
**Evaluation Metrics.** In the experimental evaluation, we employ two standard metrics: Hits@_k_ and Mean Reciprocal Rank (MRR). Hits@_k_ represents the proportion of correctly aligned entities ranked among the top \(k\) candidates. Mean Reciprocal Rank (MRR) calculates the average of the reciprocal ranks. Higher scores in both Hits@_k_ and MRR indicate superior EA performance.  
**Implementation Settings.** For the supervised model, we adhere to the original splits of _DBP-15K_[53] and _WK31-15K_[55], utilizing 10% of link pairs as a validation set and 70% as a test set. The dimensions for input embeddings, batch size, number of epochs, momentum \(m\), and temperature \(\tau\) are configured to 768, 1024, 800, 0.999, and 0.08, respectively. We utilize  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Datasets**} & **KGs** & **Entities** & **Rel.** & **Rel. Triples** \\ \hline \multirow{4}{*}{_DBP-15K_} & JA-EN & Japanese & 65,744 & 2043 & 164,373 \\  & & English & 95,680 & 2096 & 233,319 \\  & & French & 66,858 & 1379 & 192,191 \\  & & English & 105,889 & 2209 & 278,590 \\  & & Chinese & 66,469 & 2830 & 153,929 \\  & & English & 98,125 & 2317 & 237,674 \\ \hline \multirow{4}{*}{_DWY-100K_} & DBP-WD & DBpedia & 100,000 & 330 & 463,294 \\  & & Wikidata & 100,000 & 220 & 448,774 \\ \cline{1-1}  & & DBpedia & 100,000 & 302 & 428,952 \\ \cline{1-1}  & & YAGO3 & 100,000 & 21 & 502,563 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of datasets.  
[PERSON] as our optimizer, with a learning rate of \(1\times 10^{-4}\), executing tasks on a workstation equipped with an NVIDIA A5000 GPU (24 GB) and 128 GB RAM (the source code is available at [[https://github.com/cwswork/MPGT-Align/](https://github.com/cwswork/MPGT-Align/)]([https://github.com/cwswork/MPGT-Align/](https://github.com/cwswork/MPGT-Align/)) and accessed on 1 January 2025).  
**Baselines.**_MPGT-Align_ relies solely on the relation structures of KGs. To evaluate _MPGT-Align_, we compare it with three types of advanced EA methods, encompassing both supervised and unsupervised method:  
* **Supervised methods with pure relation structures**: MTransE [56], IPTransE [57], BootEA [54], RDGCN [58], AliNet [59], EMEA [60], RPR-RHGT [7], STEA [61], PEEA [9], RANM [8], and KAGNN [62]. These methods are based on the original structures of KGs.
* **Supervised methods with auxiliary information**: JAPE [53], GCN-Align [63], MRAEA [24], AttrGNN [27], MHNA [29], MMEA-cat [64], and GEEA [39]. These methods leverage both relation structures and auxiliary information, such as attribute data and images. MMEA-cat [64] and GEEA [39] incorporate image data, while others rely solely on attribute or descriptive data.
* **Unsupervised methods**: MultiKE [10], AttrE [65], UDCEA [44], ICLEA [13], UPLR [12], EVA [11], SEU [43], and SelfKG [14]. Among these methods, SEU [43], ICLEA [13], and SelfKG [14] rely solely on the original relation structure. In contrast, others incorporate auxiliary information, including attribute data (e.g., MultiKE [10], AttrE [65], UDCEA [44], ICLEA [13]), descriptive information (e.g., ICLEA [13]), and image data (e.g., EVA [11]).