Abstract
Probabilistic Soft Logic (PSL), as a declarative rule-based probability model, has strong extensibility and multi-domain adaptability and has been applied in many domains. In practice, a main difficult is that a lot of common sense and domain knowledge need to be set manually as preconditions for rule establishment, and the acquisition of these knowledge is often very expensive. To alleviate this dilemma, this paper has worked on two aspects: First, a rule automatic learning method was proposed, which combined AMIE+ algorithm and PSL to form a new reasoning model. Second, a multi-level method was used to improve the reasoning efficiency of the model. The experimental results showed that the proposed methods are feasible.
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Zhang, J., Zhang, H., Li, B., Yang, C., Zhao, X. (2019). A Probabilistic Soft Logic Reasoning Model with Automatic Rule Learning. In: Jin, H., Lin, X., Cheng, X., Shi, X., Xiao, N., Huang, Y. (eds) Big Data. BigData 2019. Communications in Computer and Information Science, vol 1120. Springer, Singapore. https://doi.org/10.1007/978-981-15-1899-7_3
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DOI: https://doi.org/10.1007/978-981-15-1899-7_3
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