## 4 Experiment and Discussion
### Dataset  
We conducted experiments on the connection prediction of TBRm on four benchmark data sets related to the industrial Internet.At the same time, in order to see the performance of the proposed method on the Industrial Internet of Things, we used another Industrial Internet of Things data set to test the representation effect of TBRm. Table 2 summarizes the statistics of the dataset. The Bosch production line internal fault data set (BPLP) [42] describes the measurement results of parts as they move in the Bosch production line. Each part has a unique ID and contains a large number of anonymous features. Features are named according to the agreement that tells you the production line, the workstations on the production line, and the feature number. We separate the files according to the types of features contained in the files: numbers, categories, parts and workstations on the production line as subject entities and object entities, and fault types as relational predicates in the triplet. Finally, there is a file with a date feature, and the date function provides a timestamp of each measurement time. MOOC Platform User Behavior Data Set (MOOC-Ub) [43] includes the learning activities of all users on the school's online platform from August 2015 to August 2017. User information is the information of users of the school online, including: gender, year of birth and education level. Course information includes the course start date, course end date, course category, and course type. Extract and filter the user ID as the subject entity, the course category and type as the object entity, and the user information and course information as the relational predicate. Combining the start and end times of the course, the model of the time series knowledge graph is used for experiments. Each json file contains user tracking logs for a specific period of time. The NFT Ethereum transaction data set (NFT) [44] contains the transaction activities of the Ethereum non-homogeneous currency (NFT) from 1 April 2021 to 25 September 2021. It is purely constructed from on-chain data and represents the activities of 9292 NFT smart contracts on the Ethereum blockchain during the period. The Information Exposure from Consumer IoT Devices (IE-IoTD) [45] dataset processed and analyzed the information leakage of 81 devices located in laboratories in the United States and the United Kingdom. We filtered out a total of 23,475 triplet relationships with timestamps.  
The Edge-IiOTest [46] data set identifies and analyzes 14 types of network attack methods. These attacks can be summarized as five threats, namely, DoS/DDoS attacks, Information gathering, Man in the middle attacks, Injection attacks, and Malware attacks. At the same time, the data set also indicates the start time and end time of an attack (or the time point of the attack). Edge-IiOTest's IOT data is generated from various IOT devices (more than 10 types), such as Low-cost digital sensors for sensing temperature and humidity, Ultrasonic sensor, Water level detection sensor, pH Sensor Meter, Soil Moisture sensor, Heart Rate Sensor, Flame Sensor, etc.). The Edge-IiOTest dataset also records the IP address of the attacker and the sensor being attacked.