本体匹配中的参数和策略调谐问题研究

项目名称： 本体匹配中的参数和策略调谐问题研究

项目编号： No.61472077

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 自动化技术、计算机技术

项目作者： 汪鹏

作者单位： 东南大学

项目金额： 80万元

中文摘要： 本体匹配调谐是解决本体匹配系统自动参数配置和匹配策略选择的有效途径，在提高匹配效果的同时能明显改善复杂匹配系统对普通用户的易用性。本项目针对匹配调谐中关键问题给出一组解决方案，主要贡献为：(1)提出匹配需求动态分析和匹配模型静态分析，前者利用本体特征和匹配目标动态描述匹配需求，后者利用匹配建模和匹配模块特征静态描述匹配系统;(2)提出基于源代码数据流分析和基于高斯过程变量模型两种参数相关性分析方法，解决模块内和模块间参数相关性，以及参数与匹配目标相关性分析;(3)提出基于源代码分析的匹配策略选择和匹配器组合分析，减少调谐中无关策略数目；(4)提出待匹配本体自动采样技术和参考匹配结果自动生成技术，为调谐提供本体样本和参考匹配；(5)提出利用历史匹配解决相似匹配任务的调谐，对非相似匹配任务则采用群智能优化方法实现参数调谐，采用机器学习方法实现策略调谐，最后引入用户反馈进一步提高调谐效果和性能。

中文关键词： 人工智能；网构软件；Web服务

英文摘要： Ontology matching tuning is a promising way for solving the problem of automatic parameter configuration and matching strategy selection problem in ontology matching systems. It can improve the matching quality while greatly improving the usability of common users for complex matching systems. This project aims to solve key issues in ontology matching tuning, and the main contributions include: (1) Proposing matching requirement dynamic analysis and matching model static analysis, and the former dynamically describe the matching requirement using ontology features and matching targets, then the latter uses matching modeling and corresponding features to statically describe the matching systems; (2) Proposing two parameter correlation analysis methods: one is data flow analysis based on source code and another is the method based on Gaussian process variable model. They can solve the parameter correlation in a matching model or cross different matching models, and the correlation between parameters and matching targets; (3)Proposing matching strategy selection and matcher combination based on source code. It can reduce the number of the irrelevant matching strategies; (4) Proposing automatic sampling method for matching ontologies and generating method for reference matchings; (5) Proposing the tuning method using history matching recodes to tuning similar matching tasks. For dissimilar ones, it fist employs swarm optimization model to realize the parameter tuning, then uses machine learning techniques to tune strategies, and finally introduces user feedback to improving the tuning effect and performance.

英文关键词： Artificial Intelligence;Internet Software