DLP Learning from Uncertain DataDLP Learning from Uncertain Data
朱曼;高志强;漆桂林;季秋;
摘要(Abstract)：
Description logic programs (DLP) are an expressive but tractable subset of OWL. This paper ana-lyzes 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 uncer-tainties. Experiments on two datasets demonstrate that the approach is able to automatically learn a rule-set from uncertain data with acceptable accuracy.
关键词(KeyWords)：
基金项目(Foundation): Supported by the National Natural Science Foundation of China(Nos.60773107,60873153,and 60803061)
作者(Authors): 朱曼;高志强;漆桂林;季秋;
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