一种电网领域数据的知识图谱搜索引擎构建

目前电网企业会产生海量且繁杂的数据资源，其利用率较低，无法体现价值。针对这一问题，文章提出一种电网领域的知识图谱数据搜索引擎构建思路，从知识图谱构建、设计和操作实现进行了详细分析，并设计了基于KGSS算法的搜索引擎系统。以江西省某电网企业大数据为研究对象，从查准率和召回率两方面验证了该算法的优越性。研究发现：基于KGSS知识谱图算法的搜索引擎其查准率稳定在85%～95%范围，召回率在80%～98%;而采用常规关键词搜索算法的查准率在60%～100%,召回率在0%～85%。综合来看，基于电网领域数据的知识图谱方法搜索结果更加合理，准确率和召回率更稳定，搜索性能得到有效提升。     

知识图谱在电网全业务统一数据中心的应用

针对国网公司的业务数据无法跨专业贯通，数据资源无法被智能分析与管理等问题，提出基于全业务统一数据中心的知识图谱构建方法。在全业务统一数据中心使用大数据技术采集电网多源数据的基础上，使用语义标注方法对结构化、半结构化、非结构化数据进行知识抽取获得知识实体、属性和关系，通过知识融合技术构建知识图谱，根据用户搜索内容智能推荐结果和相关信息。实验表明该方法提高了查准率和召回率，具有更好的智能搜索和分析能力。     

基于Q/A与文档库的智能课程答疑系统

评价智能答疑系统优劣的重要指标是准确率和召回率.系统结合Q/A库和文档库搜索技术的优势实现,利用成熟的Q/A技术回答常见问题,保证了系统的准确率和高效率.利用智能文档搜索技术解答非常见问题,提高了系统的召回率,又因为事先对文档作了预处理,使搜索效率明显提高.同时系统基于课程开发,关键词的词汇量少而精确,使得语义理解的处理得以简化.     

面向领域的智能搜索引擎设计与实现

传统的主题搜索技术难以适应大范围垂直领域的智能搜索需求,通过将主题搜索与语义搜索相关技术分别应用到搜索引擎的数据采集与智能查询过程中,利用基于层次分类模型的领域数据采集机器人,完成对领域信息的精准采集,基于中文百科资源自动构建领域本体,将大规模领域本体库用于搜索引擎的语义扩展推理中,实现了一个面向领域的智能搜索引擎。实验结果表明,基于层次结构的领域分类具有较高的分类准确率和召回率,与其他搜索引擎相比较,该系统具有领域专业性强、领域易于移植、检索更加智能等特点。     

知识网格中基于领域本体的智能检索

本文提出知识网格环境下基于领域本体的智能检索模型,采用OWL DL语言对领域知识进行形式化描述,支持推理和深层语义检索.＂标注＂和＂查询优化＂是检索的两个关键技术.通过规范的概念和概念间语义关系对文档片段进行标注,并针对＂一词多义＂问题提出＂主题-概念＂两阶段消歧算法.＂查询优化＂过程中,基于OWL DL推理的优化算法实现查询概念的自动扩展,提高了查全率和查准率.基于以上方法,建立航天领域本体,利用网上数据库开放资源作为测试集进行评测.实验显示,与传统基于     

水利信息知识图谱的构建与应用

近年来知识图谱技术作为一种用于描述客观世界中概念、实例及其关系的新方法,得到了人们的广泛关注,利用知识图谱可以有效拓展搜索结果的广度。目前水利行业采用的基于关键字的搜索技术难以利用对象间关系进行信息检索。为此,本文首先提出一种面向水利对象数据的知识图谱构建方法,用以实现水利信息知识图谱的构建。然后,提出基于推理规则的知识推理方法,利用隐藏在水利信息知识图谱中的知识实现智能数据检索。最后,将上述技术应用于水利领域,实现水利信息知识图谱构建与检索系统。通过该系统可以有效利用水利对象之间的关系,充分发挥水利信息资源的价值。     

基于知识图谱的项目文档智能管理与应用系统

针对企业现有招投标文档价值信息挖掘不足、文档知识难以应用等问题，设计一种基于知识图谱的招标项目文档智能管理系统。系统核心功能模块包括项目管理、模板管理、知识图谱和统计查询。项目管理和模板管理模块分别对项目文档进行分类管理和提供知识抽取模板。知识图谱模块实现文档知识抽取，并将抽取的知识与元数据构建知识图谱，实现文档的语义互联。对于文档知识抽取分别提出预训练模型结合规则配置的文字知识抽取模型和图片分类与光学字符识别融合的图片知识提取模型。统计查询模块基于构建的文档知识图谱实现多维统计分析、语义检索与智能问答等应用。该文档管理系统以智能化技术支持文档知识深度挖掘和反馈，能够实现文档价值充分利用。     

跨媒体智能关联分析与语义理解理论与技术研究进展

深入分析了跨媒体智能关联分析与语义理解理论技术的最新研究进展,包括多模态数据的统一表达、知识引导的数据融合、跨媒体关联分析、基于知识图谱的跨媒体表征技术以及面向多模态的智能应用.其中,多模态数据的统一表达是对跨媒体信息进行分析推理的先决条件,利用多模态信息间的语义一致性剔除冗余信息,通过跨模态相互转化来实现跨媒体信息统一表达,学习更全面的特征表示;跨媒体关联分析立足于图像语言、视频语言以及音视频语言的跨模态关联分析与理解技术,旨在弥合视觉、听觉以及语言之间的语义鸿沟,充分建立不同模态间的语义关联;基于知识图谱的跨媒体表征技术通过引入跨媒体的知识图谱,从跨媒体知识图谱构建、跨媒体知识图谱嵌入以及跨媒体知识推理3个方面展开研究,增强跨媒体数据表征的可靠性,并提升后续推理任务的分析效率和准确性;随着跨模态分析技术的快速发展,面向多模态的智能应用得到了更多的技术支撑,依据智能应用所需要的领域知识,选取了多模态视觉问答,多模式视频摘要、多模式视觉模式挖掘、多模式推荐、跨模态智能推理和跨模态医学图像预测等跨模态应用实例,梳理了其在多模态数据融合以及跨媒体分析推理方面的研究进展.     

一种基于领域本体的论文检索方法的研究与应用

针对传统的论文检索方法缺乏语义理解,检索结果相关度不高的缺点,采用基于语义网络的文档语义表达模型,提出一种基于领域本体的检索方法。首先结合学科分类体系构建领域本体,并对论文文档进行语义索引;然后根据本体知识和索引信息构建基于语义网络的文档语义表达模型;最后改进用户查询与语义网络的相关度算法,综合关键词和语义的方法实现结果排序。实验结果表明,该方法能有效地提高论文检索的准确率和召回率。     

基于朴素贝叶斯算法的电力数据合规风险智能监督方法

针对现有电力数据合规风险监督方法召回率、查准率较低,误报率较高等问题,研究引入朴素贝叶斯理论,提出并设计了一种电力数据合规风险智能监督方法。首先对电力数据进行采集及预处理,然后针对预处理后的电力数据完成电力数据合规风险特征提取,最后引入朴素贝叶斯算法,构建电力数据合规风险评估模型,实现对电力数据合规风险的智能监督。实验结果表明,应用所提方法后召回率在92%～95%,查准率高于93%,误报率仅为2%,优于对比方法,可对电力数据合规风险进行准确评估,具有较高的应用价值。     

基于色彩传递的图像马赛克算法研究

针对传统图像马赛克算法中的瓦片图像颜色调整问题,提出一种基于全局色彩传递的图像马赛克算法,提高瓦片图像对源图像颜色空间的拟合阶数,在此基础上采用矩形碎片图像拼贴方式实现了相应的马赛克系统。实验结果表明,该算法能够获得良好的性能。     

基于语义查询扩展的垂直搜索研究

基于关键词的搜索引擎无法满足用户需求,传统查询扩展方式又会带来许多语义理解错误,针对该问题,提出一种基于本体的垂直搜索引擎概念模型,建立基于关键词和基于概念的两层索引结构,利用行业领域本体对用户提交的关键词进行语义查询扩展,并对按照本体标注的文档进行排序优化,提高查询的查全率和查准率。     

A personalized query expansion approach for engineering document retrieval

Engineers create engineering documents with their own terminologies, and want to search existing engineering documents quickly and accurately during a product development process. Keyword-based search methods have been widely used due to their ease of use, but their search accuracy has been often problematic because of the semantic ambiguity of terminologies in engineering documents and queries. The semantic ambiguity can be alleviated by using a domain ontology. Also, if queries are expanded to incorporate the engineer’s personalized information needs, the accuracy of the search result would be improved. Therefore, we propose a framework to search engineering documents with less semantic ambiguity and more focus on each engineer’s personalized information needs. The framework includes four processes: (1) developing a domain ontology, (2) indexing engineering documents, (3) learning user profiles, and (4) performing personalized query expansion and retrieval. A domain ontology is developed based on product structure information and engineering documents. Using the domain ontology, terminologies in documents are disambiguated and indexed. Also, a user profile is generated from the domain ontology. By user profile learning, user’s interests are captured from the relevant documents. During a personalized query expansion process, the learned user profile is used to reflect user’s interests. Simultaneously, user’s searching intent, which is implicitly inferred from the user’s task context, is also considered. To retrieve relevant documents, an expanded query in which both user’s interests and intents are reflected is then matched against the document collection. The experimental results show that the proposed approach can substantially outperform both the keyword-based approach and the existing query expansion method in retrieving engineering documents. Reflecting a user’s information needs precisely has been identified to be the most important factor underlying this notable improvement.     

基于仿真的电气系统诊断技术新方法

在总结智能故障技术基础上,提出了基于仿真的电气系统诊断技术新方法。在分析CIR文件特征基础上,设计了自动生成故障知识算法。在分析OUT文件特征基础上,设计了自动搜索算法。利用最小距离法指引用户排除故障。综合利用这些技术开发了电气系统故障查询系统。     

Improving legal information retrieval using an ontological framework

A variety of legal documents are increasingly being made available in electronic format. Automatic Information Search and Retrieval algorithms play a key role in enabling efficient access to such digitized documents. Although keyword-based search is the traditional method used for text retrieval, they perform poorly when literal term matching is done for query processing, due to synonymy and ambivalence of words. To overcome these drawbacks, an ontological framework to enhance the user’s query for retrieval of truly relevant legal judgments has been proposed in this paper. Ontologies ensure efficient retrieval by enabling inferences based on domain knowledge, which is gathered during the construction of the knowledge base. Empirical results demonstrate that ontology-based searches generate significantly better results than traditional search methods.     

Can eyes reveal interest? Implicit queries from gaze patterns

We study a new research problem, where an implicit information retrieval query is inferred from eye movements measured when the user is reading, and used to retrieve new documents. In the training phase, the user’s interest is known, and we learn a mapping from how the user looks at a term to the role of the term in the implicit query. Assuming the mapping is universal, that is, the same for all queries in a given domain, we can use it to construct queries even for new topics for which no learning data is available. We constructed a controlled experimental setting to show that when the system has no prior information as to what the user is searching, the eye movements help significantly in the search. This is the case in a proactive search, for instance, where the system monitors the reading behaviour of the user in a new topic. In contrast, during a search or reading session where the set of inspected documents is biased towards being relevant, a stronger strategy is to search for content-wise similar documents than to use the eye movements.     

Querying Documents using Content, Structure and Properties

Much information is nowadays stored electronically in document bases. Users retrieve information from these document bases by browsing and querying. While a large number of tools are available nowadays, not much work has been done on tools that support queries involving all the characteristics of documents as well as the use of domain knowledge during the search for information. In this paper we propose a query language that allows for querying documents using content information, information about the logical structure of the documents as well as information about properties of the documents. Domain knowledge is taken into account during the search as well. We also present an architecture for a system supporting such a language and we describe a prototype implementation together with test results.     

An architectural design for effective information retrieval in semantic web

The current web IR system retrieves relevant information only based on the keywords which is inadequate for that vast amount of data. It provides limited capabilities to capture the concepts of the user needs and the relation between the keywords. These limitations lead to the idea of the user conceptual search which includes concepts and meanings. This study deals with the Semantic Based Information Retrieval System for a semantic web search and presented with an improved algorithm to retrieve the information in a more efficient way.This architecture takes as input a list of plain keywords provided by the user and the query is converted into semantic query. This conversion is carried out with the help of the domain concepts of the pre-existing domain ontologies and a third party thesaurus and discover semantic relationship between them in runtime. The relevant information for the semantic query is retrieved and ranked according to the relevancy with the help of an improved algorithm. The performance analysis shows that the proposed system can improve the accuracy and effectiveness for retrieving relevant web documents compared to the existing systems.     

混合P2P环境下有效的查询扩展及其搜索算法

查询扩展是解决信息获取领域中用词歧义性问题的关键技术,并被广泛应用于搜索引擎中,获得了巨大的成功.然而,由于P2P(peer-to-peer)系统是一个分散的、动态的系统,在P2P环境下进行有效的查询扩展具有一定的挑战性.首先,利用查询与文档的关联关系构建了LEM(local expansion method)查询扩展方法;然后,基于查询与文档用词的直接关联,提出了HEM(history_based expansion method)查询扩展方法.在此基础上,提出了一种基于查询扩展的混合P2P环境下的搜索算法.实验及分析结果表明,查询扩展及其搜索算法能够极大地提高搜索的效果.     

虚拟逆变装置电压故障检测系统的设计

电力电子逆变设备是工业生产中的关键设备。针对电力电子逆变设备的故障,设计了一种基于LabVIEW的逆变装置故障检测系统,构建了多功能操作界面,实现了5通道信息的实时采集及显示、频谱分析、故障检测及报警、历史数据查询等功能。所设计的检测系统经过测试,可以实时检测故障信号并记录故障信号的特征,对解决实际问题具有可行性和实用性。