本文的主要工作是面向农业领域的本体知识协同建构和语义信息检索。
In this paper, the main work focuses on the collaborative construction of agricultural ontology and sematic retrieval for agricultural resources.
结果建立了一种五元组的语义信息检索模型，并给出了该模型的关键实现算法。
Results Model of semantic information search based on five-element-array is built, and realization arithmetic is given.
首先，它调用企业(核心)语义服务来检索企业级组织信息。
First, it invokes the enterprise (core) semantic service to retrieve the enterprise-level organization information.
前者有助于搜索、语义上下文和信息检索，而后者让用户能够在高级超文本环境中进行高效的编辑。
The former allows easy searches, semantic context and information retrieval, whereas the latter enables efficient editing in an advanced hypertext environment.
通过语义分析和语义推理，可以充分利用信息资源之间的关系实现相关信息资源检索与语义融合。
By semantic analysis and semantic inference, the relationship among resource could be fully used to implement the relevant information resource retrieval and semantic fusion.
最后分析了SIR的可用性，证明了SIR可极大地提高语义网上信息检索的查全率和查准率。
SIR can significantly enhance the recall and precision of the information retrieval by the semantic inference. Finally, the practicability of the SIR model is analyzed.
实验表明本文提出的图像颜色特征提取算法可成功应用于海量图像库检索和图像语义信息的自动提取。
Experiments show that the new algorithm proposed can be successfully used in retrieving the image from good-sized image database and extracting semantic information from image automatically.
语义检索能克服传统的基于关键词匹配检索的缺点，是信息检索的发展趋势。
Semantic retrieval can be used to overcome the shortcomings of traditional retrieval techniques based on lexical matching, and is the trend of information retrieval.
提出一种结合图像分块纹理特征和语义信息的医学胸片图像检索方法。
This paper presented a method of medical images retrieval about sternums based on texture features combining with semantic information.
提出了一种基于本体语义模型的信息检索方法。
A methodology is presented to retrieve information based on semantic ontology model.
许多面向应用的自然语言处理相关任务，如信息抽取、机器翻译和语义检索都对句法浅析浅析提出了迫切的需求。
Many applied tasks related to NLP, such as Information Extraction, Machine Translation and Information Retrieve, all have the urgent requirement of parsing.
语义网是近年来提出的用于解决万维网在信息描述、信息表达和信息检索等方面一系列问题的新技术。
Semantic web is a new technique to be put forward to solve a series of technical problems in information description, expression and searching.
为同时提高信息检索的查全率和查准率，提出一种基于语义依存度的句子相似度改进算法。
It is a difficulty problem that how to improve the recall and the accuracy ratio simultaneously on information searching.
为解决上述问题，我们提出了一种基于偏最小二乘理论的中间语义的跨语言信息检索方法。
To solve the above problems, we propose a cross-language information retrieval method using the interlingua semantics based on Partial Least-Squares (PLS) theory.
这种图形符号的语义联想方法可以用来改善信息检索系统的人机交互效率以及用于数据挖掘领域中的信息可视化技术。
The method of icon semantics association can be applied to the improving of the information retrieve system based on icon, and can be applied to information visualization technology in data mining.
情报科学(IS)主要是一门研究文本及其它类型信息检索的科学，而信息检索(IR)问题与含义和语义学理论有关。
Information science (is) is a science which studies searching of text and other information. Information retrieval (IR) problems are related to meaning and semantics theory.
本文从一种新的检索方式——语义检索的定义出发，讨论了对检索入口、信息组织和结果输出赋予语义的基本原理。
Proceeding from the definition of semantic search, this paper discusses the basic principles of how to endow information input, information organization and searching result with semantic meaning.
传统的构件描述与检索方式，由于缺乏构件的语义信息描述，用户难以精确检索到与需求匹配的构件资源，所以不能很好地实现资源共享和复用的目的。
The traditional component description and retrieval way lacks of semantic description of the information, it is hard to find the exact component matching to the requirements, and therefor.
只有结合图像的多种信息，特别是语义信息，才能使检索系统的能力尽可能接近人的理解水平。
None but combine the multi-character, especially semantic information, can the capability of retrieval system approach the human mentally level.
提出一个基于二元语义的信息检索模型。
This paper proposes, a 2-tuple linguistic model of information retrieval.
论文首先从传统信息检索技术的现状入手，分析其主要问题，阐述语义检索如何解决这些问题。
The paper starts from the status quo of traditional IR technic, to analyse its main problems, then adequately expatiates how semantic information retrieval solves these problems.
随着语义网的出现，基于语义也逐渐成为提高信息检索能力的一个有效途径。
With the emergence of semantic web, semantic-based information retrieval has become an effective way to improve retrieval ability.
实验结果表明，该模型能够进行本体的语义推理，在一定程度上增强信息检索系统的语义处理能力，检索效率得到了改善。
The model can carry out the ontology reasoning. And the experimental results show that information retrieval efficiency to a certain extent is improved.
传统信息检索方式下，由于信息缺少统一的语义描述，用户很难找到与需求相关的信息。
In traditional information retrieval way, because the information lacks the semantic description, users are difficult to find the information they need.
本论文主要研究如何将本体技术应用到信息检索系统中，从而实现语义检索。
This paper mainly researches how to apply ontology to IR systems, to implement semantic retrieval.
基于领域本体的语义检索被认为是解决目前信息检索领域中所面临的困难的途径之一。
A semantic retrieval method based on domain ontology is a useful way to resolve the problems in the information retrieval area.
用蚁群算法的思想，利用用户的反馈信息建立图像的语义网络，并依据该语义网络用迭代的方法来检索图像。
It establishes a semantic network of images according to users' relevant feedback based on the ant colony algorithm, and then retrieves images by using the semantic network iteratively.
如何有效利用用户的相关反馈信息来进行基于语义的图像检索，是一个具有重要意义并且极具挑战性的问题。
It is a significant and challenging issue to utilize relevant feedback of users effectively to implement the semantic-based image retrieval.
本文提出了两个新的图上关键字搜索算法，使用了现代信息检索技术中的向量空间模型和随机游走模型来解决以上缺陷，使得查询结果更具语义信息。
In this paper, two novel algorithms are introduced, which employ the vector space model and random walk model to address the drawbacks above and make the results more semantical.
本文提出了两个新的图上关键字搜索算法，使用了现代信息检索技术中的向量空间模型和随机游走模型来解决以上缺陷，使得查询结果更具语义信息。
In this paper, two novel algorithms are introduced, which employ the vector space model and random walk model to address the drawbacks above and make the results more semantical.
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