摘要
在互联网迅速发展、Web信息资源急剧增长的背景下,传统的信息检索由于信息源的固有特点和检索技术的缺陷,无法找到语义上匹配的信息,导致漏检、误检现象,从质量和效率上都不能满足用户需求。人们试图将各种先进的思想和方法引入信息检索领域中,促进其理论和技术的深层次变革。
语义网的创始人——Tim Berners-Lee倡导的下一代万维网,旨在赋予Web上的信息以语义支持,通过本体技术,在资源之间建立起机器可处理的各类语义联系。语义网是当代万维网的扩展和延伸,它能够提高异构系统之间的互操作性,促进知识共享。语义网的核心——本文论,是人工智能领域的一种先进的知识表示技术,它通过明确定义概念和概念间的关系反映事物或现象的本质。基于本体的语义检索作为智能信息检索技术的一个重要领域,利用本体构建可共享、概念化的知识空间,实现对知识内容的结构化描述,具有一定的语义处理能力和较好的自然语义理解能力,而且可以处理概念关系逻辑,因而对于提高信息检索的质量和促进信息资源的利用率和共享,具有重要的研究价值。
本文主要工作如下:
分析和总结了信息检索技术概念、发展状况和存在问题与不足,介绍了基于本体的语义检索的研究现状。
通过对现有本体技术理论和语义查询技术进行分析和研究,在本小组已有研究成果的基础上,提出了一个基于本体的语义检索系统的模型,详细描述了系统的设计思想、主要功能和运行流程。
对本体查询涉及的主要技术和实现方法进行了深入的研究,探讨了本体持久化、语义推理、SPARQL语言及其通过Jena的实现。在此基础上,提出了基于查询重写和关联搜索的本体查询算法,详细论述了算法的思想和流程,分析了算法的可行性和实现难点。
本文最后开发实现了一个基于查询重写和关联搜索算法的系统原型OSea,该系统以任意本体为查询载体,具有多种检索模式,并利用基于带权语义距离的语义相关度进行结果排序,验证了本文研究的算法的有效性和可行性。
本文通过对本体技术,特别是本体检索所涉及的若干关键技术进行了深入的分析研究,对于解决信息检索效率不高、信息利用率低的问题,提出了一套有效的解决方案,该模型不仅能够处理基于本体的语义关系,能处理概念之间的关系逻辑,并且能进行语义扩展。通过实验证明应用了该算法的语义检索模型,在使用方法、查询效率和效果上相对于传统的信息检索有一定优越性。最后本文实现了该模型的原型——OSea系统,通过实践验证了系统模型的可行性。
In present web, as the high-speed increase of information resources, those search engines built on traditional query technique can not get rid of their limitation on semantic processing. One critical challenge is how to implement semantic-based information retrieval and sharing for web-scale data resource. New idea and methods are introduced into the field to reform its theory and technology.
Semantic Web is the next generation of Web, which is advocated by Tim Berners-Lee. In Semantic Web, every resource are identified by an URI (United Resource Identification), are related to each other by a formalized definition in Ontology. Semantic Web is believed to the expansion and extension of Contemporary World Wide Web, where information resources will be expressed in a clear and formal way, which can be realized easily between people and PC. Therefore sematic web can improve the interoperability between heterogeneous systems and promote knowledge sharing. Ontology is an advanced technology of representing knowledge in AI., where specified concepts and relationships are used to describe information. Ontology-based semantic retrieval as one of the important direction of intelligent information, which use ontology to build sharable and conceptual data resource, not only have semantic processing capacity and NLI, but also are able to process logic of related concepts. So ontology-based semantic retrieval has import research value.
The main achievement of this paper contains:
It first analyzes the concept of information retrieval, analyzes its current state and existing shortages. Then it introduces the new hot topics of semantic web.
By thoroughly research on ontology’s fundamentals and retrieval related techniques, based on the achievement of our team, a model for ontology-based semantic retrieval system is proposed. The design、main function and running mechanism of this model are explained.
Give thoroughly studies on many ontology retrieval related technologies, including persistence of ontology, semantic inference、SPARQL query language for ontology and how to implement them all by Jena. Based on those researches, a query rewriting and semantic association based algorithm for ontology retrieval are brought forward. This algorithm’s performance and implement are discussed in detail.
Finally a prototype of the model– OSea system is developed to implement the algorithm. The result of testing and analyze for this system indicate that the model and algorithm proposed in this paper is effective and feasible.
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