Liangjun Zang

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Collecting massive commonsense knowledge (CSK) for commonsense reasoning has been a long time standing challenge within artificial intelligence research. Numerous methods and systems for acquiring CSK have been developed to overcome the knowledge acquisition bottleneck. Although some specific commonsense reasoning tasks have been presented to allow(More)
Commonsense acquisition is one of the most important and challenging topics in Artificial Intelligence. Comparative commonsense, such as ”In general, a man is stronger than a woman”, denotes that one entity has a property or quality greater or less in extent than that of another. This paper presents an automatic method for acquiring comparative commonsense(More)
To explore the association relations among disease, pathogenesis, physician, symptoms and drug, we adapt a variational Apriori algorithm for discovering association rules on a dataset of the Qing Court Medical Records. There are five types of semantic associations we intend to discover, including Disease-Pathogenesis-Drug set(DPaD), Disease-Symptoms-Drug(More)
Mining frequent itemsets or patterns is a fundamental and essential problem in many data mining application. Because of the inherent computational complexity, mining the complete set of frequent patterns remains to be a difficult task. Mining closed patterns is a good solution to the problem. And previous study has show that mining frequent patterns with(More)
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