【作者】 赖雅；

【导师】 徐德智；

【作者基本信息】 中南大学 ， 计算机科学与技术， 2011， 硕士

【摘要】 本体映射是解决语义Web发展瓶颈的关键技术。但是,随着语义Web的发展,出现了一类概念数目庞大,概念之间关系复杂的大规模本体。由于大规模本体和普通本体在所包含的实体数目和映射难度上存在着不同,因而应当针对它们采用不同的映射方法。本文将着重对大规模本体分块与映射进行研究。首先,简要介绍了课题的研究背景,总结了本体映射技术当前的研究现状,并给出了未来的发展方向。其次,针对传统的单个本体中语义相似度计算未充分利用本体中的语义信息等不足,提出了一种基于概念特征的语义相似度计算方法。该方法首先根据概念在本体中的所处的层次结构来确定其特征集合,并引入概念的宽度因素对各个特征赋予不同权值,然后采用计算集合相似度的方法来计算概念的相似度,最后引入深度影响因子,并对相似度计算公式进行修正,转换成一种更直观的形式。理论分析和实验结果表明,该方法计算简便,结果准确。再次,针对当前的大规模本体映射方法存在的自动化程度不高,分块大小不均匀等问题,提出一种基于图分割的大规模本体分块与映射方法。该方法首先对本体进行预处理,将待匹配的大规模本体转换成有向无环图,从而将大规模本体分块问题转换成图分割问题,然后采用基于遗传算法的GPO算法分别对这两个本体图进行分割,将本体划分成本体块集合,最后通过采用基于参考点策略和基于本体块结构策略相结合的方法识别正确的块映射。最后,根据上述研究,本文设计并实现了的大规模本体分块与映射系统LSOPM,并将其和当前的大规模本体映射系统进行了比较。实验结果表明,该系统分块结果好,块映射准确,且在查全率和查准率方面都有明显提高。更多还原

【Abstract】 Ontology mapping is the key technology of solving the bottlenecks of semantic web development. However, with the developing of semantic web, the large-scale ontologies which have a lot number of concepts and complex relationship between concepts have appeared. Since there are some difference on entity number and mapping difficulty between large-scale ontologies and general ontologies, we should use different mapping method to deal with them. This thesis will focus on the issue of large-scale ontologies mapping.Firstly, the research background of the thesis is briefly introduced, after which the state of the art of the ontology mapping technology is elaborated, as well as the development trend of the mapping technology.Secondly, aiming at the problem of current semantic similarity metric of a single ontology doesn’t make full use of the semantic information of ontologies, a new semantic similarity metric based on the feature set of concepts is proposed. It first expresses each concept as a set of features according to the hierarchy of each concept in ontology, and introduces a width influencing factor as the coefficient of each feature. Then, it obtains the concept similarity through calculating the similarity between two sets. At last, we introduce a depth influencing factor, and amend the semantic metric to a more understandable form. Theoretical analysis and experimental results show that the metric is simple, but the results close to human judgment.Thirdly, aiming at the problem of low degree of automation and not uniform in block size for the current large-scale ontology mapping, a new method for large-scale ontology partition and mapping method based on graph partitioning is proposed. It first converts the two ontologies to be matched to DAG structures by preprocessing, which convert the ontologies partition problem into graph partitioning problem, and then partition the two ontologies graphs separately to a set of blocks by using the GPO algorithm which based on genetic algorithm. At last, blocks from different ontology are matched by combining two methods of ontology blocks structure as well as predefined anchors.Finally, LSOPM system has been designed and implemented according to the works above, and compared with the current large-scale ontology mapping system. Experimental results show that this system has a good quality of partition and block mapping and an obvious improvement on both precision and recall.更多还原