## 4 Results
### Experimental Setup  
**Benchmark datasets**. The evaluation of open-world knowledge KGC tasks often relies on subsets of Word-Net and Freebase, such as WN18 RR [10] and FB15K-237 [19]. To verify the effectiveness of Relgraph, we need to choose datasets with numerous predicates and high difficulty levels to validate our method's efficacy. Therefore, we have selected FB15K-237, WN18 RR, and UMLS [20] as our experimental datasets. UMLS is a domain-specific knowledge graph in the medical domain, containing biomedical concepts and their relationships. **Drug repurposing datasets**. To verify the efficacy of this method for knowledge extraction and logical reasoning on large-scale datasets, we conducted drug repurposing experiments on the open-source biochemical knowledge graph RTX-KG2c [21]. RTX-KG2c integrates data from 70 public knowledge sources into a comprehensive graph where all biological entities (e.g., \"ibuprofen\") are represented as nodes and all concept-predicate-concept relationships (e.g., \"ibuprofen-increased activity-GP1 BA gene\") are encoded as edges. This dataset comprises approximately 6.4 M entities across 56 distinct categories, with 39.3 M relationship edges described by 77 distinct relations. Drug repurposing, also known as drug rediscovery or drug repositioning, refers to discovering a new indication for an existing medication. The objective of this experiment is to employ the KGC model to learn the interactions between diseases and drugs from RTX-KG2c, aiming to predict potential therapeutic relationships between drugs and diseases. To identify \"new\" applications for existing drugs, therapeutic relationships were retrieved from external databases, including MyChem [22] and SemMedDB datasets [23]. To prevent information leakage during training, we excluded all existing edges connecting potential drug nodes (nodes labeled \"Drug\" or \"SmallMolecule\") with potential disease nodes (nodes labeled \"Disease\", \"PhenotypicFeature\", \"BehavioralFeature,\" or \"DiseaseOrPhenotypicFeature\") in RTX-KG2c. We then added drug-disease pairs that were confirmed true positives (pairs with the relation \"indication\" from MyChem Datasets or the predicate \"treats\" from SemMedDB Datasets). A new predicate treat was introduced to represent this therapeutic relationship in the experimental KG. We generated new triples based on these drug-disease pairs and added them to the KG, dividing them into training, validation, and testing sets in a 7:2:1 ratio. **Baselines**. To test the effectiveness and versatility of Relgraph, we conducted an extensive selection of well-established knowledge graph reasoning models. We used these models on the benchmarks as the baseline. For each baseline, we used them in conjunction with Relgraph to conduct performance testing and record their performance improvement. The chosen baseline models include: TransE, DistMult [9], RotatE and ConvE [10], CompGCN [4], etc. In the drug repurposing experiments, all models were trained using the modified RTX-KG2c training dataset. However, when tested on the test set, only the predicted results of triples associated with the predicate treat were considered. **Experiment setting details. ** We set the entity and relation embedding dimensions to 200 for our experiments. To optimize the model, we utilized the Adam optimization algorithm [24]. We experimented with different learning rates within the range of 0.002 to 0.006, as well as mini-batch sizes from 64 to 256. Additionally, we applied dropout regularization to both the entity and relation embeddings, as well as all feed-forward layers. We searched for an optimal dropout rate within the range of 0.55. In line with the common practices mentioned in [25, 26], we utilize standard evaluation metrics for the link prediction task: Hit@1, which represents the number of correctly predicted head terms among the top 1 predictions, and mean reciprocal ranking (MRR), calculated as the mean of the reciprocal rank of the correct answer in the list of predictions.