摘要翻译：
提出了一种基于贝叶斯分析技术和Gallois格的模糊语义网络优化方法。我们使用的技术系统通过解释一个未知的单词来学习，使用这个新单词和已知单词之间创建的链接。主链接由查询的上下文提供。当新手的查询与应用于表示理想用户网络中的对象或用户网络中的对象的已知名词的未知动词（目标）混淆时，系统推断该新动词对应于已知目标之一。该系统以学习自然语言中的新词作为解释，并与用户达成一致，在每次与新用户的实验中改进其表示方案，此外，还利用了以前与用户的讨论。通过这种学习获得的用户对象语义网并不总是最优的，因为用户对象之间的一些关系可以根据表征它们的力的值来概括，而另一些关系可以被抑制。实际上，为了简化所得到的网络，我们建议在从Gallois格得到的网络上进行归纳贝叶斯分析。这种分析的目的可以看作是对所得到的描述性图进行过滤的操作。
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英文标题：
《Fuzzy Knowledge Representation, Learning and Optimization with Bayesian
  Analysis in Fuzzy Semantic Networks》
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作者：
Mohamed Nazih Omri
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  This paper presents a method of optimization, based on both Bayesian Analysis technical and Gallois Lattice, of a Fuzzy Semantic Networks. The technical System we use learn by interpreting an unknown word using the links created between this new word and known words. The main link is provided by the context of the query. When novice's query is confused with an unknown verb (goal) applied to a known noun denoting either an object in the ideal user's Network or an object in the user's Network, the system infer that this new verb corresponds to one of the known goal. With the learning of new words in natural language as the interpretation, which was produced in agreement with the user, the system improves its representation scheme at each experiment with a new user and, in addition, takes advantage of previous discussions with users. The semantic Net of user objects thus obtained by these kinds of learning is not always optimal because some relationships between couple of user objects can be generalized and others suppressed according to values of forces that characterize them. Indeed, to simplify the obtained Net, we propose to proceed to an inductive Bayesian analysis, on the Net obtained from Gallois lattice. The objective of this analysis can be seen as an operation of filtering of the obtained descriptive graph.
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PDF链接：
https://arxiv.org/pdf/1206.1794