## 4 Experiment
### Comparison with Baseline Model Performance  
CAKGC (BERT-tiny) and CAKGC (BERT-base) were compared with existing structurally based and textually based methods in the link prediction task. The structurally based methods included TransE, RotatE, DistMult, and AutoBLM. The textually based methods included Pretrain-KGE, KG-BERT, StAR, MEM-KGC, C-LMKE, and SimKGC.  
Two versions of the CAKGC model, based on BERT-tiny with 10 million parameters and BERT-base with 110 million parameters, were employed for link prediction on two popular publicly available datasets, FB15k-237 and WN18 RR. Tables 2 and 3 present the results, showing that both versions of the CAKGC model achieved state-of-the-art performance on the FB15k-237 dataset and demonstrated competitive performance on the WN18 RR dataset. Specifically, the CAKGC (BERT-tiny) model achieved MRR, Hits@1, Hits@3, and Hits@10 scores of 0.416, 0.335, 0.451, and 0.571, respectively, on the FB15k-237 dataset. Compared to the previous best-performing model C-LMKE (BERT-tiny), the CAKGC (BERT-tiny) model improved the scores by 1%, 1.6%, and 0.6%, respectively. On the WN18 RR dataset, the CAKGC (BERT-base) model performed second best, trailing the SOTA model SimKGC. Compared to the C-LMKE (BERT-base) model, the CAKGC (BERT-base) model achieved improvements of 3.8%, 6.4%, and 2.3% in MRR, Hits@1, and Hits@3, respectively. Furthermore, compared to C-LMKE (BERT-tiny), the CAKGC (BERT-base) model demonstrated improvements of 3.4%, 3.5%, 2.7%, and 1.2% in MRR, Hits@1, Hits@3, and Hits@10, respectively.  
From the results presented in Table 3, it can be observed that the performance improvement of our model on the FB15k-237 dataset is not as significant as that on the WN18 RR dataset. We attribute this to two main reasons. (1) Compared to the WN18 RR dataset, the FB15k-237 dataset has a higher average degree for each entity, indicating richer  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Entities** & **Relations** & **Train** & **Valid** & **Test** \\ \hline FB15k-237 & 14,151 & 237 & 272,115 & 17,535 & 20,466 \\ WN18 RR & 40,943 & 11 & 86,835 & 3034 & 3134 \\ Thangka & 1399 & 13 & 2793 & 399 & 399 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset statistics.  
neighborhood information for entities in the FB15k-237 dataset. We can see that even advanced structure-based methods like AutoBLM achieve good performance on FB15k-237, indicating that modeling only textual descriptions is insufficient, and the internal structural information of the knowledge graph is equally important. (2) The FB15k-237 dataset contains a large number of two-hop relations, while the WN18 RR dataset only consists of one-hop relations. Therefore, link prediction on FB15k-237 is more challenging, as the information obtained solely from textual descriptions is limited and cannot support better reasoning for two-hop or even multi-hop relations.  
In the link prediction experiments conducted on the Tangka dataset we constructed, we compared our model with C-LMKE (BERT-base). According to Table 4, our model achieved MRR, Hits@1, Hits@3, and Hits@10 scores of 0.612, 0.566, 0.635, and 0.713, respectively, on the Tangka knowledge graph completion dataset. Compared to C-LMKE (BERT-base), our model demonstrated improvements of 1.9%, 3.2%, 1.2%, and 2% in MRR, Hits@1, Hits@3, and Hits@10, respectively.