Abstract:

In the process of constructing the knowledge graph, the existing method ignores the processing of semi-structured data, which leads to the inaccuracy and time-consuming in construction of the knowledge graph. Therefore, an automatic knowledge graph construction algorithm based on massive text data is proposed. A triplet extractor is used to extract massive text data sources, and to extract semi-structured data, while eliminating redundant data. According to the data processing results, the appropriate data objects are selected using the data collection function as the text data source constructed by the knowledge map. The data source is subjected to standardized processing such as text format conversion, word segmentation and feature extraction. The underlying semantics of the data are analyzed and an XTM visualization map is drawn to form a preliminary knowledge map. The triples of users, ratings and items are composed by mining the existing knowledge in this knowledge map, applying potential vectors to information recommendation, and the graph evolution algorithm is used to predict the ratings, users and items, constructing latent vector models Domain recommendation to realize the automatic evolution of the knowledge graph. Experimental results show that the algorithm has higher construction accuracy and less time consumption, which shows that the algorithm is reliable and practical.

Key words: massive text data, knowledge map, triples extractor, format conversion, feature extraction