基于属性权重的实体解析技术

An Entity Resolution Approach Based on Attributes Weights

实体解析是将在同一个数据源或者不同数据源中,指向现实世界同一实体的元组识别出来并合并的过程.如何高效而准确地将指向同一实体的记录识别出来一直是研究人员不懈追求的目标.在基于规则的匹配算法中,大多数算法是将所有的属性都作为匹配属性进行计算,并且各个属性的权重都是一样的,然而这样不能充分体现关键属性的重要性.主要针对关系数据库数据源中实体解析准确性的问题,采用信息增益的方法和概率统计的方法计算数据属性的权重,用来代表该属性在记录中的重要性,达到提高实体解析准确度的目的.并且在此基础上采用top-k算法,选择出最佳分类属性集合,减少匹配属性的个数,从而加快了实体解析的速度.

Entity resolution is a process of identifying and merging entity tuples pointed to the same entity in the real world during the same data source or different data sources.How efficiently and accurately to identify records pointed to the same entity has been the relentless pursuit of the goal of the researchers.During rule-based matching algorithms,most of the algorithm takes all the attributes as matching attributes to be calculated,and the weights of the various properties are the same, however,this does not fully reflect the importance of the key attributes.This paper focuses on the accuracy of entity resolution in a relational database adopting information gain and probability statistics methods to calculate the data attribute weights to represent the importance of the attributes in the record,and using top-kalgorithm,so as to achieve improve the object of the entity resolution accuracy and accelerate running time.On this basis,this paper adopts top-k algorithm to select best classified attributes,and reduce the number of matching attributes,in order to accelerate the speed of entity resolution.