HAR: Hub, Authority and Relevance Scores in Multi-Relational Data for Query Search
HAR: Hub, Authority and Relevance Scores in Multi-Relational Data for Query Search
复制标题
DOI：
10.1137/1.9781611972825.13
复制
发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Xutao Li;M. Ng;Yunming Ye
中科院分区：
文献类型：
--
作者：
Xutao Li;M. Ng;Yunming Ye
在本文中，我们提出了一个框架HAR来研究对象的中心和权威分数，以及多关系数据中用于查询搜索的关系的相关性分数。我们框架的基本思想是考虑多关系数据中的随机游动，并在这种随机游动中研究，限制相关性分数的关系概率，以及中心分数和权威分数的对象概率。本文的主要贡献在于：(I)提出了一个框架(HAR)，该框架可以通过求解多关系数据产生的极限概率来计算中心、权威和相关性分数，并可以结合输入查询向量来处理特定于查询的搜索；(Ii)证明了这些极限概率的存在和唯一性，以便它们可以有效地用于查询搜索；(Iii)开发了一种迭代算法来求解一组张量(多元多项式)方程以获得这些概率。在TREC和DBLP数据集上的大量实验结果表明，该方法在获取与查询输入相关的结果方面是非常有效的。在比较中，我们发现HAR的性能优于HITS、SALSA
In this paper, we propose a framework HAR to study the hub and authority scores of objects, and the relevance scores of relations in multi-relational data for query search. The basic idea of our framework is to consider a random walk in multi-relational data, and study in such random walk, limiting probabilities of relations for relevance scores, and of objects for hub scores and authority scores. The main contribution of this paper is to (i) propose a framework (HAR) that can compute the hub, authority and relevance scores by solving limiting probabilities arising from multi-relational data, and can incorporate input query vectors to handle query-specific search; (ii) show existence and uniqueness of such limiting probabilities so that they can be used for query search effectively; and (iii) develop an iterative algorithm to solve a set of tensor (multivariate polynomial) equations to obtain such probabilities. Extensive experimental results on TREC and DBLP data sets suggest that the proposed method is very effective in obtaining relevant results to the querying inputs. In the comparison, we find that the performance of HAR is better than those of HITS, SALSA