Hybrid reasoning in knowledge graphs: Combing symbolic reasoning and statistical reasoning

Hybrid reasoning in knowledge graphs: Combing symbolic reasoning and statistical reasoning
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知识图中的混合推理:符号推理和统计推理的结合

DOI:
10.3233/sw-190375
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发表时间:
2020-01
期刊:
Semantic Web Journal
影响因子:
--
通讯作者:
Qiu Ji
Qiu Ji
中科院分区:
其他
文献类型:
--
作者:
Weizhuo Li;Guilin Qi;Qiu Ji

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知识图作为许多信息系统的主干,以语义化和可视化的方式组织快速增长的知识。符号推理和统计推理是当前的主流技术,在人工智能的知识完备、自动模式构建、复杂问题回答、解释等方面发挥着重要作用。然而,两者都有其优点和局限性。因此,期望将它们联合收割机组合以在知识图中提供混合推理。在本文中,我们提出的第一项工作的调查方法的混合推理知识图。我们对现有的基于问题设置和推理任务的方法进行了分类,并介绍了它们的主要思想。最后,我们重新审视了知识图混合推理中尚待解决的问题,并展望了未来的发展方向。
Knowledge graph, as a backbone of many information systems, has been created to organize the rapidly growing knowledge in a semantical and visualized manner. Symbolic reasoning and statistical reasoning are current mainstream techniques that play important roles in knowledge completion, automatic schema constructing, complex question answering, explanation of AI. However, both of them have their merits and limitations. Therefore, it is desirable to combine them to provide hybrid reasoning in a knowledge graph. In this paper, we present the first work on the survey of methods for hybrid reasoning in knowledge graphs. We categorize existing methods based on problem settings and reasoning tasks, and introduce the key ideas of them. Finally, we re-examine the remaining research problems to be solved and outlook the future directions for hybrid reasoning in Knowledge graphs.
DOI: 10.4230/dagrep.8.9.29
发表时间: 2019-03
期刊: Dagstuhl Reports
影响因子: --
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