摘要
准确有效地集成海量Web信息,是Web信息动态聚合、市场情报分析、舆情分析、商业智能等分析型应用的重要基础.针对数据集成过程中不同实体指代同一实体的问题,利用搜索引擎返回的页面摘要信息,设计并实现了一种基于搜索引擎的同义实体识别算法FSE,并提出了一种基于同义实体识别的Web信息集成框架.在医院信息集成测试数据集上的实验结果表明,FSE算法效果优于基于Varient Dice、Varient Cosine、Varient Jaccard、Varient Overlap相似度计算的同义实体识别算法.
Integrating massive information on the Web accurately and effectively is the important basis of developing analytic applications, such as Web information dynamic aggregation tools, market information analysis tools, public opinion analysis tools, and business intelligence tools, etc. To solve the problem that different presentations refer to the same entity during the integrating process, this paper proposes an algorithm to recognize the synonymous entities by using the snippets from the search engine and a frame of Web information integration based on synonymous entities recognition. The experimental results on hospital information integration testing data sets show that the proposed method outperforms the synonymous entities recognition based on Varient Dice, Varient Cosine, Varient Jaccard and Varient Overlap.
出处
《计算机系统应用》
2015年第9期35-42,共8页
Computer Systems & Applications
基金
国家高技术研究发展计划(863)(2012AA011005)
国家自然科学基金(61273297)
关键词
WEB信息集成
同义实体识别
相似度计算
搜索引擎
Web information integration
synonymous entities recognition
similarity computation
search engine
