Khennak Ilyes, Drias Habiba
Laboratory for Research in Artificial Intelligence, Computer Science Department, USTHB, BP 32 El Alia, 16111, Bab Ezzouar, Algiers, Algeria.
J Med Syst. 2016 Nov;40(11):240. doi: 10.1007/s10916-016-0603-5. Epub 2016 Sep 27.
The difficulty of disambiguating the sense of the incomplete and imprecise keywords that are extensively used in the search queries has caused the failure of search systems to retrieve the desired information. One of the most powerful and promising method to overcome this shortcoming and improve the performance of search engines is Query Expansion, whereby the user's original query is augmented by new keywords that best characterize the user's information needs and produce more useful query. In this paper, a new Firefly Algorithm-based approach is proposed to enhance the retrieval effectiveness of query expansion while maintaining low computational complexity. In contrast to the existing literature, the proposed approach uses a Firefly Algorithm to find the best expanded query among a set of expanded query candidates. Moreover, this new approach allows the determination of the length of the expanded query empirically. Experimental results on MEDLINE, the on-line medical information database, show that our proposed approach is more effective and efficient compared to the state-of-the-art.
搜索查询中广泛使用的不完整且不精确关键词的语义消歧困难，导致搜索系统无法检索到所需信息。克服这一缺点并提高搜索引擎性能的最强大且最有前景的方法之一是查询扩展，即通过最能表征用户信息需求的新关键词来扩充用户的原始查询，并生成更有用的查询。本文提出了一种基于萤火虫算法的新方法，以提高查询扩展的检索效果，同时保持较低的计算复杂度。与现有文献不同，该方法使用萤火虫算法在一组扩展查询候选中找到最佳扩展查询。此外，这种新方法允许凭经验确定扩展查询的长度。在在线医学信息数据库MEDLINE上的实验结果表明，与现有技术相比，我们提出的方法更有效且高效。
