本体学习

本文所提出的基于Web的本体学习的方法可组件式地无缝集成到该体系结构中。（2）多策略领域概念获取。

Our proposed web-based ontology learning methods in this dissertation can be seamlessly integrated into the architecture . ( 2 ) Multi-strategy domain concept acquisition .

基于Web的本体学习方法通常包括术语抽取，语义解释，和创建领域本体。对于基于Web的空间本体学习也包括这三个方面。

Web-based ontology learning methods typically include terminology extraction , semantic interpretation , and create domain ontology , as well as Web-based spatial ontology learning includes those three aspects .

基于两层向量空间模型和模糊FCA本体学习方法

An Ontology Learning Method Based on Double VSM and Fuzzy FCA

面向语义网的本体学习技术和系统研究

Research on Techniques and Systems of Ontology Learning for Semantic Web

种子概念方法及其在基于文本的本体学习中的应用

Seed Concept Method and Its Application in Texts-based Ontology Learning

本体学习技术的研究目前还处于探索阶段。

Ontology learning technology is still in the exploratory stage .

本体学习：原理、方法与相关进展

Ontology Learning : Principle , Method and Related Research Progress

基于森林资源数据结构的本体学习探索

Ontology Learning Based on Data Structure of Forest Resource

基于叙词表的领域本体学习系统分为叙词表转换模块以及非分类关系学习模块。

This system includes thesaurus-conversion module and relation-learning module .

中文领域本体学习中术语的自动抽取

Automatic domain-specific term extraction in Chinese domain ontology learning

面向数字图书馆的本体学习研究

A study on Ontology Learning for Digital Library

一种本体学习模型的设计与实现

Design and Implementation for Ontology Learning Model

提出一种本体学习模型，分析了模型实现中的关键步骤。

This paper proposes an ontology learning model with several key steps in implementing the model .

从现有的文献资料看很少专门介绍基于半结构化数据源的本体学习方法与技术的阐述。

The existing literature seldom covers ontology learning methods and technologies based on semi-structured data sources .

对于数据源类型不同的，采用的本体学习技术也是不同的。

According to different types of data , it is necessary to adopt different ontology study technology .

本体学习是利用机器学习和统计等技术半自动或自动地从已有的数据资源中获取期望的本体，主要任务是从数据源中提取术语、概念及其关系。

Its main task is to extract terms , concepts and their relations from the data source .

最后，本文实现了一个中文本体学习系统用以试验本文提出的方法的可行性。

Finally , Chinese ontology learning system was realized to test the feasibility of the methods proposed .

论文主要研究内容如下：（1）通用本体学习系统的体系结构。

The detailed work is as follows : ( 1 ) General architecture of ontology learning system .

相比国外较多本体学习研究而言，中文环境下本体学习刚刚拉开序幕。

Compared to many ontology studies abroad , ontology learning in the Chinese environment is just started off .

本体学习作为（半）自动构建本体的重要途径，近年来得到快速发展。

Ontology learning which developing rapidly recently is a major way of ( semi - ) automatically building ontology .

基于结构化数据的本体学习技术可以从数据库系统中自动提取本体，在基于关系数据库的本体学习方法中，对关系模式的本体建模是一项基础工作。

In the ontology learning based on relational databases , ontology modeling of relational schema is the first step .

介绍了基于以上体系结构的本体学习的处理过程，并讨论了领域概念抽取，概念之间关系的抽取等关键技术。

Introduces the processing cycle , and discusses several key technologies such as domain concepts extraction , concepts relation extraction .

空间本体学习即从现有的知识源获取地理领域知识、以（半）自动方式构建或更新空间本体。

Spatial ontology learning from existing knowledge sources to obtain geography knowledge in order to ( semi ) automatic construction or renovation of spatial ontology .

本体学习是在已有的各类数据模型中进行语义提取，自动组织并生成本体的一个过程，而关系数据模型是当前数据的存取与组织的主要模型。

Ontology learning was to extract semantic from the data model now existing in varies organization , generate new ontology by data reorganization and definition .

经过适当的调整和整合之后，这些本体学习方法还可应用于语义检索、文本摘要等其他诸多领域。

If changed slightly , the methods and approaches may also be used in many other areas , such as semantic retrieval , text summarization , etc.

本章最后构建了一个本体学习框架，通过术语抽取、本体创建、本体修剪三大功能模块来自动创建领域本体。

In the final chapter , author constructs an ontology learning framework which can automatically create domain ontology by extracting terminologies , creating ontology and pruning ontology .

为了抽取项目的语义信息，通过本体学习建立特定领域本体并利用包装器代理从网站中抽取本体类的实例和项目属性。

We built domain-special ontology by ontology learning and used wrapper agents to automatically extracting instances of the ontology classes and semantic properties about Items from web site .

本文针对目前基于模板方法在面向中文文本的本体学习中的缺陷，提出了基于知网和模式自举的中文概念间分类关系获取方法。

For the defects of template-based method applied in Chinese ontology learning at present , this paper presents a taxonomic relation extraction method based on HowNet and bootstrapping .

本体学习技术是利用本体工程技术和机器学习技术等众多学科技术来实现本体的（半）自动构建。

Ontology learning aims at the integration of a multitude of disciplines such as ontology engineering techniques and machine learning techniques to construct the ontology ( semi ) automatically .

实验结果表明，改进的模型能够有效的提高概念、关系的查准率，证实了基于语义过滤的本体学习模型的有效性。

Experimental result shows that the improved model can effectively improve precision of concept extraction and relation extraction , and verify ontology learning method based on semantic filtering is effective .