## 4 KGs Evaluation
### Dataset and Domain-Specific Evaluation
\begin{table}
\begin{tabular}{c c} \hline \hline
**Dataset Name** & **Evaluation Metrics/Considerations** \\ \hline WN18, WN18 RR & Similar ranking and classification metrics as FB15K, with WN18 RR \\  & removing duplicate relations for more reliable link prediction. \\ \hline SCWS (Stanford Contextual Word Similarities) & Apply Pearson correlation coefficient and similarity-based F1-Score to evaluate context-based word embeddings. \\ \hline New York Times, WebNLG, ADE & Use Precision, Recall, and F1-Score to assess relation extraction performance from diverse text sources. \\ \hline KnowAir, UrbanAir (North) & Utilize regression metrics (RMSE, MAE, MAPE) and efficiency measures (e.g., execution time) for air quality prediction. \\ \hline MIMIC-III, MIMIC-IV, eICU & Evaluate patient similarity search and disease prediction using Accuracy, F1-Score, and AUC-ROC, emphasizing clinical reliability. \\ \hline ICEWS, GDELT & Combine regression metrics (RMSE, MAE) with ranking metrics (MRR, Hits@K) to assess spatiotemporal event prediction and temporal inference. \\ \hline DBP15K & Evaluate cross-lingual entity alignment using Precision, Recall, and F1-Score to capture multilingual performance nuances. \\ \hline ECHR, CAIL2018 & Use Accuracy, AUC-ROC, and F1-Score to assess legal judgment prediction, ensuring reliable performance in legal applications. \\ \hline KEGG50k, Hetionet, SuppKG, ADInt & Apply MRR, Hits@K, Precision, Recall, and F1-Score for biomedical link prediction and relation extraction in large-scale biological datasets. \\ \hline Bangladesh Agricultural Data (BBS) & Measure query response time, throughput, and data completeness to evaluate OLAP queries and business intelligence analysis in agricultural contexts. \\ \hline NEU RSDDS-AUG & Use Accuracy, Precision, Recall, F1-Score, and computational efficiency metrics (execution time, memory usage) to evaluate railway defect detection performance. \\ \hline Yago (ST), DBpedia (ST), Wikidata (ST) & Employ regression metrics (RMSE, MAE) and ranking metrics (MRR, Hits@K) to assess spatiotemporal KG analysis and link prediction accuracy. \\ \hline RDF triple store benchmarks & Focus on SPARQL query response time, throughput, and scalability to evaluate RDF store performance. \\ \hline NLPCC2017, cMedQA, TREC-QA, WikiQA & Use MAP, MRR, and Accuracy to evaluate QA system performance in both Chinese and English. \\ \hline RxNorm, VA codes & Assess mapping accuracy and use Precision and Recall to evaluate drug/diagnosis code mapping and hospital data integration. \\ \hline ConceptNet, Wikidata-CS & Evaluate commonsense reasoning and bias using F1-Score and ranking-based metrics to assess the quality of everyday knowledge representations. \\ \hline DPV-based sensitive personal data & Use Precision, Recall, and F1-Score to measure the performance of sensitive information classification based on the Data Privacy Vocabulary (DPV). \\ \hline NELL, YAGO3-10 & Apply MRR, Hits@K, Precision, Recall, and F1-Score to evaluate continuous learning and link prediction in general-purpose knowledge graphs. \\ \hline Les Misérables, Graph of Science & Use clustering quality metrics and qualitative assessments to evaluate graph exploration and visualization performance. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._