## 5 Experiments
### Ablation Studies  
In this subsection, we study following research questions: \"RQ1: How effective is KLA-SRE in capturing the knowledge of relation labels contained in KG structure and description for the sentential RE?\" This research question is further divided into two sub-research questions: RQ1.1: what is the useful contribution of each relation attribute context (description semantics, topology information) for sentential RE? RQ1.2: Is the addition of the KG information of relation labels statistically significant? \begin{table}
\begin{tabular}{c l l l l l l l} \hline \hline  & & \multicolumn{3}{c}{**Micro**} & \multicolumn{3}{c}{**Macro**} \\ \cline{3-8}  & & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline \multirow{8}{*}{Wikidata} & RECON [5] & 87.24 & 87.23 & 87.23 & 63.59 & 33.91 & 44.23 \\  & KG-Pool [6] & 88.60 & 88.59 & 88.60 & - & - & - \\  & bert-base & 85.43 & 78.37 & 81.74 & 51.42 & 37.24 & 40.02 \\  & PKG [8] & roberta-base & 85.50 & 80.30 & 82.81 & 49.33 & 36.77 & 40.02 \\  & roberta –base\({}_{\text{1e}}\) & 83.71 & 83.01 & 83.35 & 46.09 & 34.52 & 36.38 \\  & & bert-base & 88.34 & 79.19 & 83.51 & 55.72 & 39.62 & 43.24 \\  & PKGge & roberta-base & 87.66 & 82.07 & 84.77 & 54.42 & 41.64 & 44.33 \\  & & roberta –base\({}_{\text{1e}}\) & 86.83 & 83.93 & 85.36 & 50.88 & 38.52 & 40.57 \\  & & bert-base & 88.56 & 82.36 & 85.35 & 56.52 & 40.73 & 47.34 \\  & KLA-SRE & roberta-base\({}_{\text{82.72}}\) & 88.72 & 83.93 & 86.26 & 56.74 & 41.23 & 47.76 \\  & roberta –base\({}_{\text{1e}}\) & 87.49 & 86.75 & 87.12 & 51.37 & 40.04 & 45.02 \\ \hline \multirow{3}{*}{NYT} & PKG & bert-base & 47.13 & 75.57 & 57.98 & 28.35 & 45.27 & 33.05 \\  & PKGge & bert-base & 51.13 & 76.46 & 61.24 & 31.66 & 47.31 & 36.20 \\ \cline{1-1}  & KLA-SRE & bert-base & 52.71 & 77.94 & 62.89 & 32.22 & 48.67 & 38.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Table 1 summarizes the average precision (P), recall (R), and F1 scores for two standard datasets obtained from our experiments. The subscript _ge_ indicates the model with graph embeddings, and the subscript 1\(e\) indicates the model trained for one epoch in the Wikidata experiment. We include the results of other studies using the same dataset where possible; otherwise, the spaces are left empty (-). For the New York Times dataset, we only include the single reported F1 score. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Zero-Shot**} & \multicolumn{2}{c}{**Few-Shot**} & \multirow{2}{*}{**Avg.**} \\ \cline{2-2} \cline{4-5}  & _Acc. \(\pm\) ci._ & **Std.** & _Acc. \(\pm\) ci._ & **Std.** & \\ \hline Unifew & 52.5 \(\pm\) 2.0 & 9.7 & \(79.2\pm 1.5\) & 7.5 & 65.9 \\ Unifew-meta & \(79.4\pm 1.9\) & 9.2 & \(87.2\pm 1.2\) & 5.7 & 83.3 \\ KLA-SRE & \(53.7\pm 1.7\) & 8.6 & \(81.1\pm 1.1\) & 5.3 & 84.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The experimental results of the FewRel validation and test sets using zero-shot and few-shot relation extraction are presented in this table. Table 4 summarizes the performance of KLA-SRE and its configurations. The performance of w/o relation Demb outperforms that of w/o relation Gemb, indicating that knowledge tags reduce the amount of topological information required to describe the semantic contribution. This reported study on the knowledgeable label-aware relation classification module's effectiveness answers our first sub-research question (RQ1.1). w/o relation Gemb and entity Gemb continues to outperform w/o relation Gemb and relation Demb. This shows that training in a separate vector space still helps to learn more relationship labels in the relationship classification task. Expressive embeddings and the appropriate leveraging of knowledge stored in knowledge graphs for fitting tasks can resolve representation inconsistencies between sentences and relation labels.