Tensor-Based Knowledge Fusion and Reasoning for Cyberphysical-Social Systems: Theory and Framework

Abstract

Cyberphysical-social systems (CPSS) integrate human, machine, and information into large-scale automated systems and generate complex heterogeneous big data from multiple sources. Knowledge graphs play a pivotal role in energizing the data with huge volume and uneven quality to drive CPSS intelligent applications and services, thus attracting intense research interests from scholars. The Resource Description Framework (RDF) describes knowledge in the form of subject-predicate-object triples and interpreted as directed labeled graphs. However, the graph structure doesn’t have flexible operability and direct computability in the theoretical framework, although it can be understood intuitively. Therefore, we proposed a tensor-based knowledge analysis framework in this article, which supports the representation, fusion, and reasoning of knowledge graphs. First, we employ Boolean tensors to represent heterogeneous knowledge graphs completely. Then, we present a series of graph tensor operations for the modification, extraction, and aggregation of high-order knowledge graphs. Furthermore, we perform tensor 1-mode product operation between the knowledge graph representation tensor and the entity representation tensor to obtain the relation path tensor, so as to infer the relationship between any two entities. Finally, we demonstrate the practicality and effectiveness of the proposed model by implementing a case study.