Information space data modeling with context-awareness and complex semantic association

Data model in information space is the basis of effectively managing heterogeneous and interrelated data sources. However, traditional data modeling approaches fall short when representing context-dependent heterogeneous information and complex semantic associations, and when supporting semantic association reasoning. To overcome these drawbacks, in this paper, we propose a semi-structured graph model named context-aware and complex semantic association network model. In particular, we introduce the notion of context-aware interpreted object which encapsulates heterogeneous information about the underlying data and context information. We descript model complex semantic associations in terms of a set of constraint components such as order constraints, aggregation constraints, and attribute constraints. Formally, we define the semantic association rule in favor of semantic association reasoning. Also, we propose a snapshot generation algorithm for our model and further illustrate the snapshot of the data graph according to our model.