## 4 KGs Evaluation
### Dataset and Domain-Specific Evaluation  
In studies evaluating industry and multilingual scenarios, one research work utilized the GTFS Madrid benchmark to assess the consistency and coherence of mapping tools based on the public standard for transit data [166]. Another study compared various link prediction techniques across different knowledge graph datasets under identical preprocessing conditions [167]. Some studies have evaluated entity linking performance in the Chinese domain using the FB15K237, WN18 RR, and SCWS datasets [168], as well as a study that verified model performance in multilingual knowledge graph alignment tasks--considering the language-specific characteristics--based on the DBP15K series datasets [173]. Evaluations using large-scale healthcare and medical datasets, such as Yago (ST), DBpedia (ST), and Wikidata (ST), have also been conducted [174], along with research that quantitatively assessed storage space and complex query performance in industrial settings using various RDF triple store benchmarks [189]. Lastly, a study evaluating the interpretability and alignment performance of multilingual KGs also falls under this category [190]. In the domains of medicine, law, biomedicine, agriculture, and air quality prediction, one study used the KnowAir and UrbanAir (North) datasets for an air quality prediction task to evaluate the prediction accuracy and computational efficiency [170]. In the context of patient similarity search based on medical data, research employing the MIMIC-III and MIMIC-IV datasets evaluated model performance according to domain-specific characteristics [171]. Moreover, studies evaluating link prediction and completion performance in spatiotemporal knowledge graphs using the Yago (ST), DBpedia (ST), and Wikidata (ST) datasets [172], as well as research evaluating the application performance of judgment prediction models in the legal domain using the ECHR and CAIL2018 datasets [177] have been conducted. In biomedicine, link prediction evaluations using datasets such as KEGG50k, Hetionet, SuppKG, and ADInt have been carried out [178]. Additionally, research based on Banglades agricultural data (BBS) evaluated the completeness, currency, and OLAP compatibility of fisheries, forestry, and agriculture data after KG generation [179], and a study using the NEU RSDDS-AUG dataset analyzed the extrinsic application effects in the railway defect detection field [180]. Finally, a study on the open-world KG completion task utilizing various KG benchmark datasets [134] also falls into this category. In the fields of science, commonsense, clinical, natural language processing, entity resolution, human activity monitoring, hydraulic engineering, community exploration, cybersecurity, reverse-engineering question generation, and IoT applications, research using the ICEWS and GDELT datasets evaluated the effects of temporal events and logical rules on event data [175]. Additionally, studies on graph exploration and visualization using the Les Miserables and Graph of Science datasets [181], as well as research evaluating relationship-labeling performance in product knowledge graphs using the Electronics and Instacart datasets in the e-commerce domain [182] have been conducted. There is research quantitatively evaluating the effect of SPARQL modifier handling in question-answering tasks using the MQALD dataset [183] and studies assessing data integration and code mapping performance between medical institutions using real EHR data and medical codes (RxNorm, VA codes) [184]. Other examples include a study that evaluated scientific KG generation and information extraction performance using a large-scale computer science paper dataset (6.7 million papers, 10 million entities, and 41 million triples) [185], and research that assessed the bias in commonsense knowledge models using the ConceptNet and Wikidata-CS datasets [186]. There has also been work on data privacy evaluation using DPV-based sensitive personal data [187].