Research of Contextual Semantic Reasoning Model Based on Domain Ontology

Abstract

With the development of mobile network technology and the popularization of mobile terminals, traditional information recommendation systems are gradually changing in the direction of real-time and mobile information recommendation. Information recommendation brings the problem of user contextual sensitivity within the mobile environment. For this problem, first, this paper constructs a domain ontology, which is applicable to the contextual semantic reasoning model. Second, based on the “5W + 1H” method, this paper constructs a context pedigree of the mobile environment using a model framework of a domain ontology. The contextual factors of the mobile environment are divided into six categories: the What-object context, the Where-place context, the When-time context, the Who-subject context, the Why-reason context, and the How-effect context. Then, considering the degree of influence of each contextual factor from the mobile context pedigree to the user is different, this paper uses contextual conditional entropy to calculate the contextual weight of each contextual attribute in the recommendation process. Based on this, a contextual semantic reasoning model based on a domain ontology is constructed. Finally, based on the open dataset provided by GroupLens, this paper verifies the validity and efficiency of the model through a simulation experiment.

Analyzing the Beginning and End of the Rebellion of Shi Zhou Tusi by Lanyu’s Suppression in the Early Ming Dynasty in the Perspective of Algorithm Criticism

“…Semantic representation and reasoning are the basis of event history text extraction, and here, the event power series representation model is used to accomplish semantic representation and reasoning [17]. The computer's representation of the semantics of historical events can be likened to human learning concepts from network events, where each concept is represented as a collection of association relations.…”

“…Jiangkun Qin and Xia Hong. Applied Mathematics and Nonlinear Sciences, 9(1) (2024)[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] …”