# A Novel Time Constraint-Based Approach for Knowledge Graph Conflict Resolution
## 4 Experimental Results and Analysis  
The experiment compared the effect of Kgedl and baseline methods on detecting knowledge conflicts in knowledge graphs on two datasets, and it added noise data in the dataset to verify the robustness of Kgedl. We measured the performance of Kgedl using accuracy, recall, and F1 values. Experiments were carried out on a Central Processing Unit (CPU) with Intel Celeron E5-2620 V3, NVIDIA Tesla K80 GPU, 128G main memory, and CentOS6.4 Linux operating system, and the algorithms in the experiment were implemented in TensorFlow with C++. We used accuracy, recall, and F1 values to evaluate the proposed method of detecting knowledge conflicts. The accuracy rate (P) indicates the fraction of the detected conflict facts that are indeed conflict facts. The recall rate (R) indicates the ratio of the detected conflict facts to all the conflict facts, and F1 is the harmonic mean of P and R, F1 = 2P \(\times\) R/(P + R). The x-axis is the recall rate, the y-axis is the accuracy, and the composition curve is the Receiver Operating Characteristic (ROC) curve. The area under the ROC curve is named as Area Under the ROC Curve (AUC). And the larger value of AUC, the better the method's performance. Since there is no public time series knowledge graph dataset, this experiment extracted the timing facts from the IMDB and YAGO datasets: the movie data from 2007 to 2017 were extracted from the IMDB website, including the release time of the movie and the birth time of the actor and related facts. We also extracted time series facts from YAGO's yagoMetaFacts dataset, which contains time and space information from YAGO meta facts. For example, birth time and graduation time are time-sensitive relationships, and location relationships are not time-sensitive information. The timing fact format in YAGO is \(<\)factID,occurSince,b\(>\) and \(<\)factID,occurUntil,e\(>\), indicating that this fact is true within the time period \([b,e]\). Here factID represents a specific fact \(<\)\(s\), \(p\), \(o\)\(>\). This article directly uses \(<\)\(s\), \(p\), \(o\), \(w\), \([b,e]\)\(>\) to represent a tuple. In the experiment, 12 commonly used time-sensitive relationships were selected as time series datasets, and then a subset of entities where both entities exist in the time series were selected. This produced 36,754 tuple facts, which were then randomly divided according to a certain ratio to generate training sets and test sets. The statistics of the datasets YAGO and IMDB are shown in Table 1. All experiments were repeated five times using a re-divided training/validation/test set, and the average results of the five experimental results were finally given. We selected three current mainstream knowledge graph evolution methods, i.e., Path Ranking Algorithm (PRA) [16], Translation in the corresponding Relation space (TransR) [17] and nPSL [18], as the baseline methods to compare with Kgedl. PRA is a path-sorting reasoning method based on the random walk method. It mainly uses the graph structure features of knowledge graphs to infer the potential relationship between entities. PRA learns the relationship between entities and classifies the relationship using the logistic regression method. Relationship features are the main features of the classifier. The TransR method maps two relationships in a tuple to the same vector space. It can cluster different entity nodes, learn the vector representation of the relationship in different clusters, and judge the entity relationship according to the inconsistency of the vector, i.e., conflicts that result in consistent entity relationships in knowledge graphs. nPSL is a time series inference method based on the probabilistic soft logic. It numerically expands the probabilistic soft logic to detect the fact that there is time conflict in knowledge graphs. In the experiment, we used Kgedl and the baseline methods to perform conflict detection in the YAGO and IMDB datasets with uncertain timing facts.