Semantic Context Enrichment and Shifting Based on Multi-dimensional Association

Contexts are appreciated as localized background knowledge for operations in knowledge management and applications. However, current context representations ignore the characters that contexts have different size, have multi-layer contextual dimensions, and contain both metadata and instances. In order to improve context representation, this paper proposes a novel context representation approach based on multi-dimensional association, and further proposes context enrichment and shifting approaches for context operations. This paper first defines multi-dimensional association in knowledge and association-based context in which core Entity is a set for key concepts, relations and properties, and then introduces three context enrichment operations including context supplement, context expansion, and context contraction, and introduces three context shifting operations including lifting and lowering, core Entity changing, and merging. The representation can be used in various knowledge application scenarios. The context enrichment and shifting operations can ensure the consistency of context and value the connections between contextual entities in association.