The purpose of this paper is to provide an integrated semantic information retrieval (IR) solution based on an ontology-improved vector space model for situations where a digital collection is established or curated. It aims to create a retrieval approach which could return the results by meanings rather than by keywords.,In this paper, the authors propose a semantic term frequency algorithm to create a semantic vector space model (SeVSM) based on ontology. To support the calculation, a multi-branches tree model is created to represent the ontology and a set of algorithms is developed to operate it. Then, a semantic ontology-based IR system based on the SeVSM model is designed and developed to verify the effectiveness of the proposed model.,The experimental study using 30 queries from 15 different domains confirms the effectiveness of the SeVSM and the usability of the proposed system. The results demonstrate that the proposed model and system can be a significant exploration to enhance IR in specific domains, such as a digital library and e-commerce.,This research not only creates a semantic retrieval model, but also provides the application approach via designing and developing a semantic retrieval system based on the model. Comparing with most of the current related research, the proposed research studies the whole process of realizing a semantic retrieval.

中文翻译：

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一种用于语义检索的本体改进向量空间模型

本文的目的是为建立或策划数字馆藏的情况提供基于本体改进的向量空间模型的集成语义信息检索 (IR) 解决方案。旨在创造一种可以通过意义而不是关键词来返回结果的检索方法。,在本文中，作者提出了一种基于本体的语义词频算法来创建语义向量空间模型(SeVSM)。为了支持计算，创建了一个多分支树模型来表示本体，并开发了一组算法来操作它。然后，设计并开发了基于 SeVSM 模型的基于语义本体的 IR 系统，以验证所提出模型的有效性。使用来自 15 个不同领域的 30 个查询的实验研究证实了 SeVSM 的有效性和所提出系统的可用性。结果表明，所提出的模型和系统可以成为在数字图书馆和电子商务等特定领域增强 IR 的重要探索。本研究不仅创建了语义检索模型，还通过设计提供了应用方法并基于该模型开发语义检索系统。与目前大多数相关研究相比，本研究对实现语义检索的全过程进行了研究。本研究不仅创建了语义检索模型，还通过设计和开发基于该模型的语义检索系统提供了应用途径。与目前大多数相关研究相比，本研究对实现语义检索的全过程进行了研究。本研究不仅创建了语义检索模型，还通过设计和开发基于该模型的语义检索系统提供了应用途径。与目前大多数相关研究相比，本研究对实现语义检索的全过程进行了研究。