Investigating Domain Knowledge Graph Knowledge Reasoning and Assessing Quality Using Knowledge Representation Learning and Knowledge Reasoning Algorithms

Domain Knowledge Graphs are becoming increasingly significant in a multitude of sectors, as they collate distinctive data from a plethora of disciplines. However, in some domains, generic knowledge representation is limited, resulting in problems like duplication and knowledge loss. To solve the above issues, the study first builds a Knowledge Representation Learning model for training and then constructs a Domain Knowledge Graph inference algorithm based on Knowledge Representation Learning for knowledge inference and quality evaluation. The results indicated that the effectiveness of the knowledge-inference-raised method was the best in datasets of different sizes, with the average accuracy, Hits@1, Hits@3 and Hits@10 of 0.906, 0.914, 0.942 and 0.948, respectively. In the results of the link prediction task, the study method had a poor performance for only one relation, with an average accuracy rate of 0.675. In the application results of knowledge graph quality assessment, the interpretability and data fusion ability of both Domain Knowledge Graphs were strong, with the accuracy indexes exceeding 0.95, and the various indexes of simplicity and completeness exceeding 0.88. The above results indicate that our research method is effective in filling in the missing knowledge through knowledge reasoning and ensures data reliability.