Concept-based retrieval and interpretation for large data sets

基于概念的大型数据集检索和解释。在计算机技术和通信技术进步的推动下，在线信息的获取正以指数级速度增长。目前的信息检索方法由于依赖于简单的基于术语的方法而变得无效，导致大量的匹配，其中只有很小一部分是相关的。我们通过开发新的匹配算法来解决这个问题，该算法通过半自动地构建语义结构来理解数据库存储库中文档的潜在含义，从而为查询提供更多相关信息。这个项目将对医学、历史、法律和许多其他学科的众多最终用户大有裨益

Concept-based retrieval and interpretation for large data sets. Access to on-line information is growing at an exponential rate, fuelled by advances in computing and \r\ncommunications technologies. Current information retrieval methods are becoming ineffective due to \r\ntheir reliance on simple term-based methods, resulting in a massive number of matches, of which only \r\na small proportion are relevant. We address this problem by developing new matching algorithms which \r\nunderstand the underlying meaning of documents in database repositories - by building semantic \r\nstructures semi-automatically - and thus provide more relevant information to queries. \r\nThis project will be of great benefit to a multitude of end-users in medicine, history, law and many other disciplines.\r\n