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Data space is a semi-structural data model for the management of large-scale heterogeneous data objects. In a data space, each data object consists of a set of attribute-value pairs to describe the internal properties of the object and their relationships to other objects. The data space model provides a flexible query language that supports attribute queries and semantic link queries. In this paper we introduce a Resource Space Model that extends the concepts of data space to make it more flexible by incorporating classification semantics. We model a data space as a set of Resource Spaces (RS) with each RS representing a kind of resources that share common attributes and can be put into the same category, either according to a user's knowledge of the world or his query preferences. Our model is suitable for designing a user-tailored semantic view of a data space while at the same time without losing the schema-later data-centric nature. A practical query language is introduced to implement flexible query operations on the resource spaces of the data space. Examples show how the proposed model and language support both the schema query and the data query.