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The Domain HowNet is used to assist Domain Ontology to express the domain knowledge.
领域知网是某领域内的知识库。
With the elicitation of ontology and Hownet, a bank ontology database and bank Hownet are built up.
借鉴本体和知网思想，构建银行领域本体库和银行知网。
The strategy we presented is the modified similarity computation from the HowNet for structure disambiguation.
据此提出利用改进的知网相似度计算的歧义消解策略。
First we put forward the general idea about this method and give a brief introduce to its semantic knowledge resource the Hownet Dictionary.
本文首先提出了这种方法的总体思路，并对其语义知识资源—《知网》作了简要的介绍。
Word graphs, phrase graphs are concluded in Knowledge Graph, Word graphs are automatic generate according to Hownet, phrase graphs are combined by Word graphs.
在本文中，知识图中的词图是根据知网中的语义词典自动生成的，短语图是在短语分析过程中由词图合并得来的。
This paper presents an extending inside-outside algorithm by combining the knowledge of HowNet. And then the extending algorithm is used for Chinese semantic analysis.
提出结合知网的知识对内部-外部算法予以扩展，并利用扩展的内部-外部算法实现汉语的语义依存分析。
The idea is to extract the core words of class first, then use HowNet to map key words space to concept space based on core words, finally finish the text classification pr.
在此提出一种基于类别核心词的概念映射方法，首先从文本中抽取类别核心词，借助《知网》将特征词映射到基于类别核心词的概念空间，然后在概念空间上完成文本分类工作。
According to the users navigation history and the concept relations living in the HowNet, a user interest forest model based on the relative relations of concepts is constructed.
根据用户的访问历史，利用知网建立基于概念关系的用户兴趣森林模型。
Text inference is central to natural language applications. This paper presents an inference method based on HowNet, which organizes knowledge with semantic net and infers with marker passing.
文本推理在自然语言处理的应用中占有极为重要的位置，本文介绍了基于知网的一种推理方法，该方法以语义网络的形式表示知网中的知识，利用“标记传递”实现推理。
On the basis of the theory of Chinese message structure, we have developed HowNet-based Chinese Chunk Extractor, using HowNet and HowNet-Chinese Message Structure Database as the main resources.
我们根据“中文信息结构”的理论，以《知网》和《知网-中文信息结构库》为主要资源，开发了中文语块抽取器。
Therefore, this article on the basis of first in HowNet, improve the method of word sentiment, then according to linguistic knowledge of the various factors that defines the sentiment quantitative.
因此，本文首先在知网的基础上，对词汇语义倾向计算方法予以改进，并且根据语言学知识扩展了影响情感的各种因素。
Taking the semantic information of questions as a criterion of question classification, a new classification system is proposed, and then a HowNet based automatic classification approach is presented.
以问题的语义信息为分类依据，建立了一个新的问题分类体系，并提出了一种基于《知网》的自动分类方法。
Taking the semantic information of questions as a criterion of question classification, a new classification system is proposed, and then a HowNet based automatic classification approach is presented.
以问题的语义信息为分类依据，建立了一个新的问题分类体系，并提出了一种基于《知网》的自动分类方法。
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