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HStar is implemented to support large scale OWL documents management. Physical storage model is designed on file system based on semantic model of OWL data. Inference and query are implemented on such physical storage model. Now HStar supports characters of OWL Lite and we try to adopt strategy of partial materializing inference data, which is different(More)
Abox inference is an important part in OWL data management. When involving large scale of instance data, it can not be supported by existing inference engines. In this paper, we propose efficient Abox inference algorithms for large scale OWL-Lite data. The algorithms can be divided into two categories: initial inference and incremental inference. Initial(More)
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