DLP Learning from Uncertain Data

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• 作者：Man Zhu ; Zhiqiang Gao ; Guilin Qi ; Qiu Ji
• 关键词：description logic programs ; inductive logic programming ; Markov logic networks
• 刊名：Tsinghua Science Technology
• 出版年：2010
• 出版时间：December 2010
• 年：2010
• 卷：15
• 期：6
• 页码：650-656
• 全文大小：437 K

文摘

Description logic programs (DLP) are an expressive but tractable subset of OWL. This paper analyzes the important under-researched problem of learning DLP from uncertain data. Current studies have rarely explored the plentiful uncertain data populating the semantic web. This algorithm handles uncertain data in an inductive logic programming framework by modifying the performance evaluation criteria. A pseudo-log-likelihood based measure is used to evaluate the performance of different literals under uncertainties. Experiments on two datasets demonstrate that the approach is able to automatically learn a rule-set from uncertain data with acceptable accuracy.