The research of clinical temporal knowledge graph based on deep learning

Abstract

Temporal knowledge base exists on various fields. Take medical medicine field as example, diabetes is a typical chronic disease which evolves slowly. This paper starts from actual EMR data of hospitals by combination of experience and knowledge of clinical doctors. Link prediction on clinical knowledge base such as diabetic complication requires the analysis on temporal characteristic of temporal knowledge base, which is a great challenge for traditional link prediction models. This paper proposes temporal knowledge graph link prediction model based on deep learning. This model selects the TransR transformation model suitable for big data and makes entity projection in relation space containing different semantic meanings, so as to vector the entities and complex semantic relations in graph. Then it adopts LSTM recursive neural network and adds the top-bottom relational information of the graph for sequential learning. Finally it constantly carries out deep learning through incremental calculation and LSTM recursive network to improve the accuracy of prediction. The incremental LSTM model highlights the hidden semantic and clinical temporal information and effectively utilizes sequential learning to mining forward-backward dependent information. It compensates the deficiency of lower prediction accuracy on timely knowledge graph caused by the traditional link prediction models. Finally, it is proved that the new model has better performance over temporal knowledge graph link prediction.

Design knowledge graph-aided conceptual product design approach based on joint entity and relation extraction

“…Knowledge graph is a semantic network that connects different data using entities, relations and attributes [6]. It has been used widely in digital industrial products and services, intelligent education, digital clinical database [7][8][9] and other areas. In the knowledge graph-based knowledge acquisition methods for product development, Hao et al [10] proposed a knowledge graph-based integration and navigation method for engineering design decision knowledge.…”