An SG-CIM Model Table Classification Method Based on Multi Feature Semantic Recognition Technology

Abstract

AbstractIn this paper, based on the SG-CIM model Knowledge Graph, we introduce the semantic recognition technology in Natural Language Processing to model the information contained in the graph and mine the semantic information with multiple features according to the characteristics of the data itself. For the problem of graph domain name-related attribute complementation, this paper adopts a multi-feature semantic recognition approach to classify the given SG-CIM model table by domain name. We propose an ATT-ALE-TextRNN model for the descriptive features of the table, adding N times second-level domain name embeddings to the basic TextRNN and calculating the attention score together to capture the tendency of different contextual information for a given category. In this paper, with reference to the multidimensional discrete feature classification problem of the recommender system, an improved DeepFM model is proposed for the table discrete, class-forming features. It facilitates the discovery of semantic dependencies between class features, makes the feature distribution more diverse, and avoids the problems of low repetition between multidimensional features and low performance of combined computation. By combining the above two models, this paper achieves more accurate mining of multi-feature semantics and accurate classification of topic domains.KeywordsSemantic recognitionClassificationAttention networkDeep learning