一种基于领域知识的XML数据模糊查询

为了解决普通用户对XML数据的模糊查询问题,提出了一种基于领域知识的XML数据模糊查询方法.以模糊集理论为基础,首先介绍了XML数据模糊查询的构成形式;然后提出了将领域知识和模糊集的隶属函数相结合的方法实现XML数据的模糊查询条件转换,转换过程考虑了查询谓词的重要程度和用户偏好;最后按结果元素对模糊查询的满足程度对模糊查询结果进行排序.该方法无需改变传统的XML查询语言和XDBMS就能够实现模糊查询,从而提高了用户与系统之间的交互能力.实验结果表明,提出的模糊查询方法具有较高的查全率和准确率.     

模糊描述逻辑知识库查询蕴涵的判定方法

大规模领域本体的快速发展对语义Web领域的数据访问提出了更高的要求,而基本的本体推理服务已不能满足数据密集型应用中处理复杂查询(主要是合取查询)的迫切需要.为此,大量的研究工作集中在本体和描述逻辑知识库合取查询算法的设计实现上,并开发出了很多知识库存储和查询的实用工具.近来模糊本体和模糊描述逻辑的研究,特别是它们在处理语义Web中模糊信息方面,得到了广泛关注.文中重点研究了模糊SH这一族极富表达能力的描述逻辑知识库的合取查询问题,提出了相应的基于推演表的算法,证明了算法对于f-SHOIQ的真子逻辑的可靠性、完备性和可终止性.证明了算法对于f-SHOIQ是可靠的,并分析了导致算法不可终止的原因.对于该问题的数据复杂度,证明了当查询中不存在传递角色时其严格的CONP上限.对于联合复杂度,汪明了算法关于知识库和查询大小的CO3NEXPTIME时间复杂度上限.     

多层次多粒度采煤机创新设计知识表达方法

针对现有知识表达方法难以全面有效地表达采煤机创新设计知识、采煤机专题知识库对知识的表达不够全面和检索效率不高等问题,为实现采煤机创新设计知识和经验的重用,激发设计者进行知识类比迁移的能力,提出了多层次多粒度采煤机创新设计知识表达方法。提取参数、结构、功能、原理、效应和领域等6种属性,用不同抽象粒度的知识对属性进行描述,每一个粒度又分别对应着概念层、语义关系层、实例层等抽象层面。通过特征属性描述生成知识条目,构建了多层次多粒度采煤机创新设计知识库,并通过本体推理和语义扩展从本地知识库和互联网资源检索得到不同层次不同粒度的知识,从而实现采煤机创新设计知识的多层次多粒度表达。以采煤机滚筒结构改进设计为例验证了该方法的可行性。     

融合文本概念化与网络表示的观点检索

观点检索是自然语言处理领域中的一个热点研究课题.现有的观点检索模型在检索过程中往往无法根据上下文将词汇进行知识、概念层面的抽象,在语义层面忽略词汇之间的语义联系,观点层面缺乏观点泛化能力.因此,提出一种融合文本概念化与网络表示的观点检索方法.该方法首先利用知识图谱分别将用户查询和文本概念化到正确的概念空间,并利用网络表示将知识图谱中的词汇节点表示成低维向量,然后根据词向量推出查询和文本的向量并用余弦公式计算用户查询与文本的相关度,接着引入基于统计机器学习的分类方法挖掘文本的观点.最后利用概念空间、网络表示空间以及观点分析结果构建特征,并服务于观点检索模型,相关实验表明,本文提出的检索模型可以有效提高多种检索模型的观点检索性能.其中,基于统一相关模型的观点检索方法在两个实验数据集上相比基准方法在MAP评价指标上分别提升了6.1%和9.3%,基于排序学习的观点检索方法在两个实验数据集上相比于基准方法在MAP评价指标上分别提升了2.3%和14.6%.     

模糊询问的查询策略及其精确回答

本文在“模糊知识库的查询策略”一文的基础上,提出了模糊知识库中的查询类型3和类型4的查询策略。其基本思想是基于推理树DT,从模糊知识库中构造出询问命题语句的参照语句,然后求出在给定参照语句下的询问命题语句的真值。本文还提出了如何将基于真值概念所得出的模糊回答转化为精确回答的算法或称之为过程。     

存储容量可扩展区块链系统的高效查询模型

区块链技术是目前计算机领域的研究热点,其实现了去中心化,并且能够安全地存储数字信息,有效降低现实经济的信任成本.提出一种区块链存储容量可扩展模型的高效查询方法——ElasticQM.此查询模型由用户层、查询层、存储层和数据层这4个模块组成.在用户层,模型将查询结果缓存,加快再次查询相同数据时的查询速度;在查询层,模型采用容量可扩展区块链模型的全局查询优化算法,增加了查询超级节点、查询验证节点和查询叶子节点这3种节点角色,提高了查询效率;在存储层,模型改进了区块链的容量可扩展模型ElasticChain的数据存储过程,实现了存储的可扩展性,并减少了占用的存储空间;在数据层,提出一种基于B-M树的区块链存储结构,并给出了B-M树的建立算法和基于B-M树的查找算法,基于B-M树的存储结构,区块链会在进行块内局部查找时提高区块链的查询速度.最后,通过在多节点不同数据量的区块链中查询的实验结果表明,ElasticQM查询方法具有高效的查询效率.     

基于知识图谱的老年教育知识库系统设计与实现

在老年教育建设过程中,将信息技术与老年教育特点相结合,为提高老年教育过程中大量的数据、知识存储和查询效率,设计了一种基于知识图谱的老年教育知识库系统.系统以知识图谱为核心,通过对老年教育不同角色的业务进行分析并设计本体,将多领域和多类型的数据通过本体映射,完成知识图谱构建,将繁杂无序的数据形成具有内在多种关联的知识库,并为学员和教师提供在线的知识管理和知识服务功能.与传统的数据管理方式相比,知识检索和信息获取效率得到有效提高.     

基于模式集成语义的查询处理

在采用面向对象模型作为公共数据模型的多数据库系统中，基于模式集成语义的查询处理不仅要实现针对集成模式查询到针对输出模式查询的转换，而且要从语义上尽可能减少回答用户查询所需数据，保证对象引用的正确性.为了达到这个目标,提出了一些新的概念及基于模式集成语义的查询处理规则和路径表达式的查询处理方法.     

智能教学系统NKI-Tutor的知识查询设计

NKI-Tutor是国家知识基础设施面向应用的智能教学系统，其功能是将NKI丰富的知识传授给用户。知识查询是NKI-Tulor给用户提供的一种查询式学习模式。该文研究如何通过NKI-Tutor的知识查询界面实现用户与智能教学系统的交互而获得所需知识，提出了基于自然语言理解的知识查询方法，用于构造和实现NKI-Tutor中用户查询知识的交互界面。该方法包括3方面内容：(1)分析用户的知识查询形式，采集用户查询句型模板；(2)根据用户查询的知识特征，把知识查询映射到概念．关系模型上；(3)通过智能分词和模糊匹配构造查询结果。     

面向复杂产品的多层智能推理框架

案例推理是基于知识的问题求解方法.克服复杂产品进行单层案例推理所面临的粗粒度问题,考虑到复杂产品案例表达多层次的特点,根据领域知识和部件功能相似性,基于XML描述的部件层次互换约束规则,实现深层次的案例细节调整和修改,从而建立了多层智能推理方法框架,为多层次复杂产品案例推理问题提供可行的解决方案.并以某类复杂产品采办全生命周期中的概念设计为例说明多层智能推理框架问题求解的有效性和面向用户服务的支持效率.     

基于改进多层次模糊关联规则的定量数据挖掘算法

针对单一层次结构实现规则提取具有规则提取准确性不高、算法运行时间长、难以满足用户使用需求的问题,提出一种基于改进多层次模糊关联规则的定量数据挖掘算法。采用高频项目集合,通过不断深化迭代的方法形成自顶向下的挖掘过程,整合模糊集合理论、数据挖掘算法以及多层次分类技术,从事务数据集中寻找模糊关联规则,挖掘出储存在多层次结构事务数据库中定量值信息的隐含知识,实现用户的定制化信息挖掘需求。实验结果表明,提出的数据挖掘算法在挖掘精度和运算时间方面相较于其他算法具有突出优势,可为多层次关联规则提取方法的实际应用带来新的发展空间。     

A fuzzy knowledge-based system for intelligent retrieval

For many knowledge-intensive applications, it is important to develop an environment that permits flexible modeling and fuzzy querying of complex data and knowledge including uncertainty. With such an environment, one can have intelligent retrieval of information and knowledge, which has become a critical requirement for those applications. In this paper, we introduce a fuzzy knowledge-based (FKB) system along with the model and the inference mechanism. The inference mechanism is based on the extension of the Rete algorithm to handle fuzziness using a similarity-based approach. The proposed FKB system is used in the intelligent fuzzy object-oriented database (IFOOD) environment, in which a fuzzy object-oriented database is used to handle large scale of complex data while the FKB system is used to handle knowledge of the application domain. Both the fuzzy object-oriented database system and the fuzzy knowledge-based system are based on the object-oriented concepts to eliminate data type mismatches. The aim of this paper is mainly to introduce the FKB system of the IFOOD environment.     

Personalized Concept-Based Clustering of Search Engine Queries

The exponential growth of information on the Web has introduced new challenges for building effective search engines. A major problem of web search is that search queries are usually short and ambiguous, and thus are insufficient for specifying the precise user needs. To alleviate this problem, some search engines suggest terms that are semantically related to the submitted queries so that users can choose from the suggestions the ones that reflect their information needs. In this paper, we introduce an effective approach that captures the user's conceptual preferences in order to provide personalized query suggestions. We achieve this goal with two new strategies. First, we develop online techniques that extract concepts from the web-snippets of the search result returned from a query and use the concepts to identify related queries for that query. Second, we propose a new two-phase personalized agglomerative clustering algorithm that is able to generate personalized query clusters. To the best of the authors' knowledge, no previous work has addressed personalization for query suggestions. To evaluate the effectiveness of our technique, a Google middleware was developed for collecting clickthrough data to conduct experimental evaluation. Experimental results show that our approach has better precision and recall than the existing query clustering methods.     

角色模型的粗集-模糊聚类分析方法

角色分析可以满足产品个性化设计系统中对于用户模型构建的需要。提出了基于粗集的模糊聚类角色分析与模型构建方法,通过构造基于粗集的模糊相似矩阵、确定角色属性的模糊相似聚类分析方法,从用户调研数据中提取典型用户属性特征,构建角色模型。该方法完善了角色分析在产品设计中的应用方法,有助于快速生成概念产品设计模型与方案。     

KGDB:统一模型和语言的知识图谱数据库管理系统

知识图谱是人工智能的重要基石,其目前主要有RDF图和属性图两种数据模型,在这两种数据模型之上有数种查询语言.RDF图上的查询语言为SPARQL,属性图上的查询语言主要为Cypher.10年来,各个社区开发了分别针对RDF图和属性图的不同数据管理方法,不统一的数据模型和查询语言限制了知识图谱的更广泛应用.KGDB(kno...     

Extended event–condition–action rules and fuzzy Petri nets based exception handling for workflow management

Exception handling plays a key role in dynamic workflow management that enables streamlined business processes. Handling application-specific exceptions is a knowledge-intensive process involving different decision-making strategies and a variety of knowledge, especially much fuzzy knowledge. Current efforts in workflow exception management are not adequate to support the knowledge-based exception handling. This paper proposes a hybrid exception handling approach based on two extended knowledge models, i.e., generalized fuzzy event–condition–action (GFECA) rule and typed fuzzy Petri net extended by process knowledge (TFPN-PK). The approach realizes integrated representation and reasoning of fuzzy and non-fuzzy knowledge as well as specific application domain knowledge and workflow process knowledge. In addition, it supports two handling strategies, i.e., direct decision and analysis-based decision, during exception management. The approach fills in the gaps in existing related researches, i.e., only providing the capability of direct exception handling and neglecting fuzzy knowledge. Based on TFPN-PK, a weighted fuzzy reasoning algorithm is designed to address the reasoning problem of uncertain goal propositions and known goal concepts by combining forward reasoning with backward reasoning and therefore to facilitate cause analysis and handling of workflow exceptions. A prototype system is developed to implement the proposed approach.     

Knowledge-based image retrieval with spatial and temporalconstructs

A knowledge-based approach to retrieve medical images by feature and content with spatial and temporal constructs is developed. Selected objects of interest in an image are segmented and contours are generated. Features and content are extracted and stored in a database. Knowledge about image features can be expressed as a type abstraction hierarchy (TAH), the high-level nodes of which represent the most general concepts. Traversing TAH nodes allows approximate matching by feature and content if an exact match is not available. TAHs can be generated automatically by clustering algorithms based on feature values in the databases and hence are scalable to large collections of image features. Since TAHs are generated based on user classes and applications, they are context- and user-sensitive. A knowledge-based semantic image model is proposed to represent the various aspects of an image object's characteristics. The model provides a mechanism for accessing and processing spatial, evolutionary and temporal queries. A knowledge-based spatial temporal query language (KSTL) has been developed that extends ODMG's OQL and supports approximate matching of features and content, conceptual terms and temporal logic predicates. Further, a visual query language has been developed that accepts point-click-and-drag visual iconic input on the screen that is then translated into KSTL. User models are introduced to provide default parameter values for specifying query conditions. We have implemented the KMeD (Knowledge-based Medical Database) system using these concepts     

An integrated semantic-based approach in concept based video retrieval

Multimedia content has been growing quickly and video retrieval is regarded as one of the most famous issues in multimedia research. In order to retrieve a desirable video, users express their needs in terms of queries. Queries can be on object, motion, texture, color, audio, etc. Low-level representations of video are different from the higher level concepts which a user associates with video. Therefore, query based on semantics is more realistic and tangible for end user. Comprehending the semantics of query has opened a new insight in video retrieval and bridging the semantic gap. However, the problem is that the video needs to be manually annotated in order to support queries expressed in terms of semantic concepts. Annotating semantic concepts which appear in video shots is a challenging and time-consuming task. Moreover, it is not possible to provide annotation for every concept in the real world. In this study, an integrated semantic-based approach for similarity computation is proposed with respect to enhance the retrieval effectiveness in concept-based video retrieval. The proposed method is based on the integration of knowledge-based and corpus-based semantic word similarity measures in order to retrieve video shots for concepts whose annotations are not available for the system. The TRECVID 2005 dataset is used for evaluation purpose, and the results of applying proposed method are then compared against the individual knowledge-based and corpus-based semantic word similarity measures which were utilized in previous studies in the same domain. The superiority of integrated similarity method is shown and evaluated in terms of Mean Average Precision (MAP).