Jianbo Ou

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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)
The increasing number of XML repositories has provided the impetus to design and develop systems that can store and query XML data efficiently. Non-native XML method could not adequately be customized to support XML, native XML database will be more efficient. Most of existed XML database systems claimed that their systems are schema-independent. But we(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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