摘要翻译：
语义网络限定任何两个顶点相关的边的含义。确定哪个顶点在语义网络中最“中心”是困难的，因为一种关系类型可能被主观地认为比另一种关系类型更重要。因此，对语义网络度量的研究主要集中在基于上下文的排名（即用户指定的上下文）上。此外，许多现有的语义网络度量对语义关联（即两个顶点之间的有向路径）进行排序，而不是对顶点本身进行排序。本文提出了一种基于Markov链分析的随机walker模型的语义网络中的特征向量中心度和PageRank等有语义意义的主要特征向量度量的计算框架。在本文的上下文中，随机步行者受到语法的约束，语法是用户定义的数据结构，它决定最终顶点排序的意义。本文中的思想是在语义Web倡议的资源描述框架(RDF)的上下文中提出的。
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英文标题：
《Grammar-Based Random Walkers in Semantic Networks》
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作者：
Marko A. Rodriguez
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最新提交年份：
2008
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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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一级分类：Computer Science        计算机科学
二级分类：Data Structures and Algorithms        数据结构与算法
分类描述：Covers data structures and analysis of algorithms. Roughly includes material in ACM Subject Classes E.1, E.2, F.2.1, and F.2.2.
涵盖数据结构和算法分析。大致包括ACM学科类E.1、E.2、F.2.1和F.2.2中的材料。
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英文摘要：
  Semantic networks qualify the meaning of an edge relating any two vertices. Determining which vertices are most "central" in a semantic network is difficult because one relationship type may be deemed subjectively more important than another. For this reason, research into semantic network metrics has focused primarily on context-based rankings (i.e. user prescribed contexts). Moreover, many of the current semantic network metrics rank semantic associations (i.e. directed paths between two vertices) and not the vertices themselves. This article presents a framework for calculating semantically meaningful primary eigenvector-based metrics such as eigenvector centrality and PageRank in semantic networks using a modified version of the random walker model of Markov chain analysis. Random walkers, in the context of this article, are constrained by a grammar, where the grammar is a user defined data structure that determines the meaning of the final vertex ranking. The ideas in this article are presented within the context of the Resource Description Framework (RDF) of the Semantic Web initiative.
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PDF链接：
https://arxiv.org/pdf/0803.4355