数据库自然语言查询接口探讨

本文主要总结了基于自然语言理解的数据库查询接口的特点,优势及其发展情况和现状。然后设计了一个数据库自然语言汉语查询接口模型,并介绍了该接口的工作原理,较详细地探讨了该接口各个模块的设计思路及注意事项。     

基于Ontology的信息查询系统模型研究

当前,网络信息资源呈现爆炸式增长趋势,用户对信息查询的要求也越来越高,传统的各种信息查询技术已经很难满足这种要求。未来的因特网作为人类的信息库、知识库,应该支持用户以自然语言的方式来完成信息查询,并具备理解语义,进行自动扩展、联想的智能化查询系统。该文提出一个基于Ontology的信息查询系统模型,实现支持自然语言的理解和语义层面的智能化查询功能。     

ApplicationOntology本体构建及SPARQL查询研究

针对当前智能手机应用安全知识等信息的共享及复用问题,采用本体技术实现了智能手机应用本体APPO (ApplicationOntology)的建模。首先,利用本体描述语言OWL(Web Ontology Language)对APPO中的概念及概念之间的关系进行知识表示,建立了一个语义表达准确的领域本体。其次,利用本体查询语言SPARQL实现基于RDF三元组的各类相关查询。最后,在此基础上,借助本体推理机制进行了推理研究,并结合实例,验证了研究内容的可行性和实用价值。     

数据库自然语言查询界面

数据库技术的普及使得用户对数据库应用界面的要求越来越高，以往的几类接口都需要用户有较高计算机知识水平，而且必须经过一定的培训，这样就会造成人力物力的浪费而且不利于计算机的普及。本文探讨的是一种更为方便简洁不秀学习即可操作的自然语言界面。     

数据库自然语言查询技术研究

1引言 近年来,随着DB技术的日益成熟,作为与计算语言学、人工智能等技术紧密结合产物,数据库自然语言查询界面(DBNLI)的研究受到高度重视,成为新一代计算机系统研究的要课题,具有很高的     

基于Ontology的数据库自然语言查询接口的研究

提出了一种基于Ontology的关系数据库自然语言查询接口的系统模型及设计框架.采用WordNet作为基本数据库并在WordNet之上定义领域词库,可以提高语法分析的识别率;同时利用Ontlogly知识表达能力存储关系数据库概念模型,并对概论模型的内容进行扩充;另外对Ontology和WordNet的同义词集进行关联,可以提高语义的识别率.用户的输入查询语句通过语法分析、语义分析生成中间表达式语言DRS,然后通过模板技术转换成SQL,通过DBMS执行SQL并返回结果.实验证明,这种方案不但实用可行,而且通过逐步完善Ontology知识库的定义,可以大大提高查询的命中率;另外通过WordNet和Ontology定义领域词库和领域知识,提高了系统的可移植性.最后,所提供的方法可以很容易地移植到其他领域.     

基于属性相关性的SPARQL查询优化方法

目前主流的RDF存储系统都是基于关系数据库的,其查询引擎都是将SPARQL转换为SQL,然后由数据库的查询引擎来执行查询.但是,目前的数据库查询优化器对于连接查询的选择度估计都是基于属性独立假设的,这往往导致估计错误而选择了效率低的执行计划,所以属性相关性信息对于SPARQL查询优化器能否找到效率高的执行计划是非常重要的.针对SPARQL转换为SQL后,因连接操作没有优化导致查询效率不高的问题,提出了利用本体信息自动计算属性相关性的方法,从而调整连接操作的选择度估计值,调整连接顺序,提高SPARQL查询中基本图模式的连接查询效率.     

一种基于本体的军用文书理解系统设计

本文提出了一套基于本体和自然语言理解相结合的军用文书理解的解决方案.系统通过信息抽取和军标本体匹配两个模块,针对军用文书与军队标号相对应的特点,通过计算机自动处理,将军用文书转化成一种无二义性的中间格式,传递给其他系统使用,以提高指挥作战的效能.     

基于自然语言理解的智能化多媒体信息检索系统研究

多媒体信息由于维度高、数据量大、可解释性差等特征制约了其检索性能,提出了基于自然语言理解的智能化多媒体信息检索系统模型。该系统基于自然语言理解、数据挖掘、自反馈等技术的运用,在一定程度上扩大了检索范围,提高了检索准确率。     

基于数据库查询的自然语言接口研究

语音识别技术是近年来信息领域重要的科技发展技术之一.作为一门交叉学科,语音识别正逐步成为信息技术中人机接口的关键技术.探讨了为数据库查询提供自然语言接口的可能性,介绍了图书资料查询系统中语音识别的基本框架,并详细描述了采用微软Speech SDK技术实现图书资料查询的详细过程.给出了查询流程,基于状态转换图的词法分析和语法分析方法,将疑问句转换成SQL查询语句的方法,以及由查询结果生成答句的方法.     

Querying databases with a domain-oriented natural language understanding system

This paper presents the DONAU (Domain Oriented NAtural language Understanding) system. The system can extract, from a sentence expressed in natural language, the useful information that is necessary in order to provide either an appropriate command for a robot or an acceptable query to a database system. The DONAU system, being adapted for such different versions, is intended to provide a contribution of quite general significance in the field of natural language understanding and within the general area of artificial intelligence. In fact, while a first version of DONAU, which has been developed and successfully tested on UNIVAC 1108 computer, is devoted to the semantic domain of robotics, a second DONAU version for querying databases has been constructed. Thus, the DONAU architecture has been conceived and developed in order to provide an experimental and formalizable result that is of general value, and that therefore can be applied to semantic domains of a different type as well.A preliminary version of this paper was presented at the IIASA Workshop on Natural Language for Interaction with Data Bases, Laxenburg Schloss, Austria, January 1977.     

基于BaseX数据库的OWL本体存储查询研究

针对目前基于关系型数据库等存储模式的本体存储查询效率较低的情况,提出使用XML数据库BaseX进行本体的存储,并设计了相应的本体存储查询架构。在对BaseX存储结构与接口的研究基础上,实现对OWL本体的存储。利用BaseX的查询接口和XQuery查询语言对OWL本体进行检索,在建立推理规则库基础上,实现本体查询扩展与推理。实验将提出的存储查询方法与基于关系型数据库的存储查询方法进行对比,验证了提出的方法具备高效的存储查询性能,同时具备本体查询的推理能力。     

基于领域本体知识库的语义查询扩展

为了在检索过程中全面表达用户查询意图,提出了基于领域本体知识库的语义查询扩展方法。该方法借助领域本体推理出的知识,使检索系统从语义层面理解用户查询语句,并通过语义相似度来控制扩展词的规模,避免了查询过度扩展,使得新构造的查询能更准确地描述用户的检索需求,提高了检索的有效性。原型系统的实验结果表明,该方法较传统的关键字匹配法和LAC方法有明显的优势,在保障查全率的基础上,可极大地提高检索准确率。     

基于自然语言的分拣机器人解析器技术研究

工业机器人通常采用特定的机器人语言进行示教编程与控制,对于操作人员需要具有较高专业与技能要求,并且示教周期长导致工作效率降低。为了提高工业机器人使用效率与易用性,提出一种基于受限自然语言解析器的设计方法。该系统通过对受限自然语言进行词法解析、语法解析、语义解析,得到所需求的工作意图,然后与实时生成的三维空间语义地图进行匹配,结合机械臂轨迹规划,生成能够完成工作任务的机器人作业程序,并完成了机器人作业程序的解析与实际机械臂的控制。通过实验证明设计的基于受限自然语言处理的分拣机器人解析器能够正确解析自然语言命令,实现对机械臂的控制。     

A linguistic ontology of space for natural language processing

We present a detailed semantics for linguistic spatial expressions supportive of computational processing that draws substantially on the principles and tools of ontological engineering and formal ontology. We cover language concerned with space, actions in space and spatial relationships and develop an ontological organization that relates such expressions to general classes of fixed semantic import. The result is given as an extension of a linguistic ontology, the Generalized Upper Model, an organization which has been used for over a decade in natural language processing applications. We describe the general nature and features of this ontology and show how we have extended it for working particularly with space. Treaitng the semantics of natural language expressions concerning space in this way offers a substantial simplification of the general problem of relating natural spatial language to its contextualized interpretation. Example specifications based on natural language examples are presented, as well as an evaluation of the ontology's coverage, consistency, predictive power, and applicability.     

自然语言理解在Web数据挖掘中的应用

Internet的迅猛发展,使其日益成为人们查找有用数据的重要来源。一般的搜索引擎是基于关键字的查询,命中率较低,且不能针对特定用户给出特定服务。提出了将自然语言理解技术与Web数据挖掘相结合,根据用户的特殊需求定制个性化的Web数据挖掘系统,给出了面向新闻挖掘这一特定领域的Web挖掘系统News-Miner的应用方案及设计实现。初步实验结果表明该方案是可行的。该方法可方便地扩展到其它专业应用领域。     

An automatic literature knowledge graph and reasoning network modeling framework based on ontology and natural language processing

With the advancement of scientific and engineering research, a huge number of academic literature are accumulated. Manually reviewing the existing literature is the main way to explore embedded knowledge, and the process is quite time-consuming and labor intensive. As the quantity of literature is increasing exponentially, it would be more difficult to cover all aspects of the literature using the traditional manual review approach. To overcome this drawback, bibliometric analysis is used to analyze the current situation and trend of a specific research field. In the bibliometric analysis, only a few key phrases (e.g., authors, publishers, journals, and citations) are usually used as the inputs for analysis. Information other than those phrases is not extracted for analysis, while that neglected information (e.g., abstract) might provide more detailed knowledge in the article. To tackle with this problem, this study proposed an automatic literature knowledge graph and reasoning network modeling framework based on ontology and Natural Language Processing (NLP), to facilitate the efficient knowledge exploration from literature abstract. In this framework, a representation ontology is proposed to characterize the literature abstract data into four knowledge elements (background, objectives, solutions, and findings), and NLP technology is used to extract the ontology instances from the abstract automatically. Based on the representation ontology, a four-space integrated knowledge graph is built using NLP technology. Then, reasoning network is generated according to the reasoning mechanism defined in the proposed ontology model. To validate the proposed framework, a case study is conducted to analyze the literature in the field of construction management. The case study proves that the proposed ontology model can be used to represent the knowledge embedded in the literatures’ abstracts, and the ontology elements can be automatically extracted by NLP models. The proposed framework can be an enhancement for the bibliometric analysis to explore more knowledge from the literature.     

Improved fuzzy weighted-iterative association rule based ontology postprocessing in data mining for query recommendation applications

The usage of association rules is playing a vital role in the field of knowledge data discovery. Numerous rules have to be processed and plot based on the ranges on the schema. The step in this process depends on the user's queries. Previously, several projects have been proposed to reduce work and improve filtration processes. However, they have some limitations in preprocessing time and filtration rate. In this article, an improved fuzzy weighted-iterative concept is introduced to overcome the limitation based on the user request and visualization of discovering rules. The initial step includes the mix of client learning with posthandling to use the semantics. The above advance was trailed by surrounding rule schemas to fulfill and anticipate unpredictable guidelines dependent on client desires. Preparing the above developments can be imagined by the use of yet another clever method of study. Standards on guidelines are recognized by the average learning professionals.