Interpretable patent recommendation with knowledge graph and deep learning

Abstract

Patent transfer is a common practice for companies to obtain competitive advantages. However, they encounter the difficulty of selecting suitable patents because the number of patents is increasingly large. Many patent recommendation methods have been proposed to ease the difficulty, but they ignore patent quality and cannot explain why certain patents are recommended. Patent quality and recommendation explanations affect companies’ decision-making in the patent transfer context. Failing to consider them in the recommendation process leads to less effective recommendation results. To fill these gaps, this paper proposes an interpretable patent recommendation method based on knowledge graph and deep learning. The proposed method organizes heterogeneous patent information as a knowledge graph. Then it extracts connectivity and quality features from the knowledge graph for pairs of patents and companies. The former features indicate the relevance of the pairs while the latter features reflect the quality of the patents. Based on the features, we design an interpretable recommendation model by combining a deep neural network with a relevance propagation technique. We conduct experiments with real-world data to evaluate the proposed method. Recommendation lists with varying lengths show that the average precision, recall, and mean average precision of the proposed method are 0.596, 0.636, and 0.584, which improve corresponding performance of best baselines by 7.28%, 18.35%, and 8.60%, respectively. Besides, our method interprets recommendation results by identifying important features leading to the results.

An operational risk assessment method for petrochemical plants based on deep learning

“…Equally important 2= (1,2,3) Somewhere between equally important and slightly important 3= (2,3,4) Slightly important 4= (3,4,5) Somewhere between slightly important and more important 5= (4,5,6) More important 6= (5,6,7) Somewhere between more important and much more important 7= (6,7,8) Far more important 8= (7,8,9) Somewhere between far more important and important 9= (8,9,9) Vital The fuzzy consistent matrix is established Fuzzy consistent judgment matrix R represents the comparison of the relative importance between this level and its related elements for an element in the upper level. It is assumed that the element C in the upper level is the same as the element a1, a2,..., and is connected, then the fuzzy consistent judgment matrix can be expressed as Figure 7 (1) Establish a fuzzy consistent matrix A fuzzy consistent matrix is consistent with human decision thinking.…”

“…With the development of computer technology, "artificial intelligence" technology has become more and more mature. Through the installation and deployment of intelligent security monitoring algorithm models in mobile individual soldier equipment and edge computing equipment, real-time analysis and early warning are realized on the job site [5]- [9], and cloud-edge collaborative management platforms with visual perception, intelligent identification, and hierarchical alarm are established. Intelligent visual analysis is integrated into the job scene industry.…”