CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Mining Inference Formulas by Goal-Directed Random Walks
Wei Zhuoyu; Zhao Jun; Liu Kang
Conference NameConference on Empirical Methods in Natural Language Processing
Conference Date2016.11.1-2016.11.5
Conference PlaceAustin, Texas, USA
AbstractDeep inference on a large-scale knowledge base (KB) needs a mass of formulas, but it is
almost impossible to create all formulas manually. Data-driven methods have been proposed to mine formulas from KBs automatically, where random sampling and approximate calculation are common techniques to handle big data. Among a series of methods, Random Walk is believed to be suitable for knowledge graph data. However, a pure random walk without goals still has a poor efficiency of mining useful formulas, and even introduces lots of noise which may mislead inference. Although several heuristic rules have been proposed to direct random walks, they do not work well due to the diversity of formulas. To this end, we propose a novel goaldirected inference formula mining algorithm, which directs random walks by the specific inference target at each step. The algorithm is more inclined to visit benefic structures to infer the target, so it can increase efficiency of random walks and avoid noise simultaneously. The experiments on both WordNet and Freebase prove that our approach is has a high efficiency and performs best on the task.
Indexed ByEI
Document Type会议论文
Corresponding AuthorLiu Kang
AffiliationNational Laboratory of Pattern Recognition,Institute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wei Zhuoyu,Zhao Jun,Liu Kang. Mining Inference Formulas by Goal-Directed Random Walks[C],2016:1379-1388.
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