An ontology reasoning architecture for data mining knowledge management

In order to realize the intelligent management of data mining （DM） domain knowledge, this paper presents an architecture for DM knowledge management based on ontology. Using ontology database, this architecture can realize intelligent knowledge retrieval and automatic accomplishment of DM tasks by means of ontology services. Its key features include：①Describing DM ontology and meta-data using ontology based on Web ontology language （OWL）.② Ontology reasoning function. Based on the existing concepts and relations, the hidden knowledge in ontology can be obtained using the reasoning engine. This paper mainly focuses on the construction of DM ontology and the reasoning of DM ontology based on OWL DL（s）.     

Query expansion based on semantics and statistics in Chinese question answering system

In Chinese question answering system, because there is more semantic relation in questions than that in query words, the precision can be improved by expanding query while using natural language questions to retrieve documents. This paper proposes a new approach to query expansion based on semantics and statistics Firstly automatic relevance feedback method is used to generate a candidate expansion word set. Then the expanded query words are selected from the set based on the semantic similarity and seman- tic relevancy between the candidate words and the original words. Experiments show the new approach is effective for Web retrieval and out-performs the conventional expansion approaches.     

基于ontology的智能答疑系统的研究

在受限领域自动问答系统中,用户问句的语义理解是提高查询效率的关键。提出了一种基于本体的自动问答系统模型,对领域知识本体的构建进行了研究。利用本体中丰富的语义关系,对用户的问题查询请求进行语义扩展,提高问答系统的准确性。     

Design and Implementation of FAQ Automatic Return System Based on Similarity Computation

FAQ （frequently asked question） is widely used on the Internet, but most FAQ＇s asking and answering are not automatic. This paper introduces the design and imple mentation of a FAQ automatic return system based on semantic similarity computation, including computation model choo sing, FAQ characters analyzing, FAQ data formal expressing, feature vector indexing, and weight computing and so on. According to FAQ features of sentence length short, two mapping, strong domain characteristics etc. Vector Space Model with special semantic process was selected in system, and corresponding algorithm of similarity computation was proposed too. Experiment shows that the system has a good performance for high frequent and common questions.     

Automatic Question Answering from Web Documents

A passage retrieval strategy for web-based question answering （QA） systems is proposed in our QA system. It firstly analyzes the question based on semantic patterns to obtain its syntactic and semantic information and then form initial queries. The queries are used to retrieve documents from the World Wide Web （WWW） using the Google search engine. The queries are then rewritten to form queries for passage retrieval in order to improve the precision. The relations between keywords in the question are employed in our query rewrite method. The experimental result on the question set of the TREC-2003 passage task shows that our system performs well for factoid questions.     

一种改进的概念相似度计算方法

本体在信息集成、语义网和知识管理等领域中被认为是重要的理论基础.本体映射的目的就是通过计算概念相似度找到本体中概念之间的对应关系,并制定出相应的映射规则.通过研究本体映射方法,设计出一种综合概念相似度计算模型,该模型充分考虑了概念的实例、定义以及结构等信息对相似度计算的影响,可以通过改变3个相似度分量的权值来适应不同本体间的映射,使模型具有通用性.结果表明,该模型能更有效地体现概念间的关系,使本体映射方法更加全面,并有更好的适用性.     

基于模糊本体的智能交通系统知识建模

为有效处理交通知识建模中的模糊与不确定现象,提出包括领域本体与顶层本体的模糊本体模型.在领域本体中引进模糊概念属性值,并将普通的概念关系扩展为模糊关系与直觉模糊关系.顶层本体基于模糊语言变量的形式化表示,考虑了模糊概念间的实质性语义关系:集合关系、序关系与等价关系.以交通事故分析与预测系统为例,建立交通知识本体模型与基于语义的信息检索系统.该模型可有效进行智能交通系统的知识建模,有利于语义Web环境下智能交通的信息管理与知识管理.     

基于语义Web的多功能情报信息自适应检索技术

为了解决传统依据关键字、概念和属性值等检索技术没有考虑不同主体中情报信息间的语义关系,无法提供令用户满意的情报检索结果问题,通过语义Web方法研究了多功能情报信息自适应检索技术。设计基于语义Web的多功能情报信息自适应检索模型,检索模型包括人机交互层、知识处理层以及知识存储层。在知识存储层建立本体语义模型。在知识处理层,从检索的多功能情报信息关键词中推断和其语义有关的概念,依据概念有关词汇对原始查询进行扩展,对语义相似度进行计算。在人机交互层,将用户反馈情况看作判断检索结果优劣的衡量因素,依据相似关键词获取相似结果集,按照反馈因子完成对所有相似结果的排序,把排序结果发送给用户,实现多功能情报信息自适应检索。结果表明,所提方法能够同时保证召回率与精度;排序误差率低;检索结果和用户查询结果最相符,检索结果准确,可令用户满意。可见所提方法检索性能强。     

一种改进的本体概念相似度计算算法

概念相似度研究是知识表示以及信息检索领域中的一个重要内容。根据概念之间错综复杂的关系,可将本体结构看成一个有向无环图。从多个角度出发考虑各种影响有向边权重的因素,通过计算有向边在本体层次图中的相对重要性计算本体中任意两概念的语义距离,由此得到它们的语义相似度。实验结果表明:该方法具有很高的效率。     

Construction of domain Web classification ontology based on suggested upper merged ontology

Aiming at the problem of merging heterogeneous semantic taxonomy emerged in Web information integration, a method of building Web classification ontology （WCO） has been proposed. A WCO that is logically consistent with the suggested upper merged ontology （SUMO） is defined, together with axioms needed to classify Web pages. WCO can be used as a foundation of merging heterogeneous semantic taxonomy, and could be used to support Web information integration and classification based Web information retrieval.     

本体中概念间语义相似度和相关度混合计算方法

语义相似度计算广泛应用于自然语言处理中,但现有语义相似度计算方法没有充分挖掘本体结构中概念间的关系.笔者针对当前概念相似度计算的片面性和不完善性等不足,提出一种计算概念间语义相似度和相关度的混合方法.从有向边包含的特征属性对语义距离进行扩展,结合概念深度,将语义距离转换成语义相似度,通过引入概念间相关度计算,使最终概念语义相似度计算更加精确.并将该方法与人的主观判断结果进行比较,验证了该方法的可行性和有效性.     

基于训练模型改进的语音问句信息抽取方法

为进一步提高客户语音问句实体信息抽取的准确性,增强智能问答系统知识图谱中信息抽取技术的整体效果,首先对语义标注进行优化,随后在BiLSTM-CRF(bidirectional long short-term memory conditional random filed)基础上加入BERT(bidi-rectional encoder representation from transformers)模型对句子进行实体抽取学习.在具体实验中,以语音问句事件文本为数据来源,对其进行语义标注和实体抽取实验.结果 表明,在语义标注优化的基础上同时加入BERT改进模型,信息抽取结果均高于BiLSTM-CRF方法,且改进模型的调和平均值达到91.53％,即可为增强事件实体信息抽取提供实践意义.     

基于概念和统计的问答系统实现机制

问答系统与搜索引擎相比答案精练准确、“噪音”较少．为此给出了基于概念和统计的问答系统实现机制．首先确定焦点类型和答案类型,对于不同类型的焦点实行分级概念扩充;以段落为基本单元,利用潜在语义分析技术获取相关文本段落;给出了问题与句子的匹配算法和基于问题相似度的答案抽取算法,通过对语料进行潜在语义索引和增加FAQ反馈,不断增强系统的回答能力．依此研制的大学概况问答系统获得了较好的效果．     

煤矿本体的语义相似性研究

为了在信息检索中,从语义层面全面理解用户查询意图,提高信息的查全率和查准率,提出了基于本体的语义相似性和相关性计算方法。该算法充分考虑了本体模型结构的特点和本体间语义距离、本体密集度等因素对语义相似性的影响,提高了信息检索性能。以煤炭矿井工程的知识体系为例,建立了本体库模型,并设计了本体库的树状内存数据结构。通过实验仿真计算验证了本体相似性算法的有效性。     

基于本体的语义相似性研究

为了在信息检索中,从语义层面全面理解用户查询意图,提高信息的查全率和查准率,提出了基于本体的语义相似性和相关性计算方法。该算法充分考虑了本体模型结构的特点和本体间语义距离、本体密集度等因素对语义相似性的影响,提高了信息检索性能。以煤炭矿井工程的知识体系为例,建立了本体库模型,并设计了本体库的树状内存数据结构。通过实验仿真计算验证了本体相似性算法的有效性。     

一种面向形式概念分析的语义相似度计算框架

为了计算形式概念分析的形式概念或概念格的语义相似度,利用描述逻辑概念作为形式背景的特征属性对形式概念分析的形式背景进行语义扩展,即利用领域本体(描述逻辑知识库)提出了形式背景的一种语义表示方法．在此基础上,基于形式背景的语义表示,利用描述逻辑推理(即本体推理)给出了一种面向形式概念分析的形式概念或概念格语义相似度计算框架,并且证明了实例化该架构所得到的语义相似度计算方法满足对称性和等价不变性等基本性质．     

Automatic Web services composition based on SLM

Motivated by the problem of simplifying the manual operation of the composition process, we propose an approach to automatically compose available Web services to fulfill user＇s goal based on the assumption that there are a set of alternative Web services with similar functionality and different QoS properties. A formal model （i.e. semantic links matrix, SLM for short ） is proposed to store semantic links values for the Web services with semantic relationship and QoS of Web services. The SLM provides a search place for a backward-search planning algorithm, at the same time; the QoS criteria make a rational and effective decision among a number of similar Web services. The function and some properties of the algorithm are analyzed. The approach can improve the correctness and flexibility for Web services composition and satisfy the local QoS attribute.     

Towards a smallest scale of context in ontology integration

In order to solve the ambiguity problems in the semantic context （structure, granularity or scale） emerging in the process of ontology integration application, this paper analyzes the essential characters of context structure, proposes a novel semantic context generating algorithm, which is implemented over VO-Editor（visual ontology editor）, from the satisfiability-based point of view, and proves that the context entity generated by this algorithm is smallest in scale and unique. It offers a feasible means for developers to handle context problems for ontology integration application.