共查询到18条相似文献，搜索用时 265 毫秒
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知识表示规范比较研究 总被引：1，自引：0，他引：1
提出一个比较和评估各种知识表示规范的框架。该框架有4个维度:表示充分性、表示属性、支持的推理方法、推理属性。然后使用此框架对逻辑、语义网、产生式规则及框架规范这4种常用的知识表示规范进行了比较。最后认为此框架可有效地帮助问题求解中知识表示规范的比较和选择。 相似文献
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早期开发工具仅仅提供了一种基于规则的知识表示方法,这种方法简单且推理能力很有限。开发者在选择知识表示的元基、搜索策略,专用推理机时感到困难。我们研制KNOWBEL(简记KNB)是一种新型开发工具,它提供了一个知识表示和推理相应的框架,增加知识获取机制,还具有解释推理结论。有效地维护知识库的功能。 商用的专家系统外壳(例如KEE)专门用来建造专家系统结构,它基于框架、推理机、产生规则、过程语言如Lisp,从而使开发者避开壳的知识表示和推理。KNB提供了一个框架过滤器,并保持了商业壳的柔性,其底部两层支持建造专家系统结构,并在事实分类中予以强化。其顶层采用知识表示语言,用以表示和组织描述应用实例的概念,Telos依据演绎数 相似文献
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专家系统开发工具ESDT—HKD是一种常用的工程计算机知识语言,它具有适用知识的表示结构、在计算机开发环境和使用环境中,可以采用规则+框架+黑板作为系统的知识结构。本文详细地介绍了它的知识描述结构及应用方式。 相似文献
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框架表示是一种广泛地应用于人工智能系统中的知识表示方法,而目录服务器则是一种层次型数据库,以分层的方式将各种资源信息在目录树结构中分层存储。根据目录服务的层次结构特性,将目录服务用于基于框架的知识表示,可达到事半功倍的效果。并可利用LDAP所提供的开发包,实现对知识库的操作。 相似文献
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基于本体的新产品开发领域知识库模型研究 总被引：1，自引：0，他引：1
讨论了知识表示方法选择所应考虑的因素,综合比较了几种常用的知识表示方法的优缺点。分析了利用本体来表示新产品开发领域知识的优势及其国内外研究现状。阐述了利用本体实现新产品开发领域知识共享和知识重用的机理,提出一种基于本体的新产品开发领域知识库结构模型,并对其中知识库和本体库的关系进行了讨论。 相似文献
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专家系统开发工具ESDT—HKD是一种常用的工程计算机知识语言，它具有适用知识的表示结构、在计算机开发环境和使用环境中，可以采用规则 框架 黑板作为系统的知识结构。本文详细地介绍了它的知识描述结构及应用方式。 相似文献
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内隐知识的递阶拓展:组织智力的一个分析框架 总被引：2，自引：0，他引：2
在智力资本的框架下,探讨个人知识的流动、存贮并形成组织知识的过程。并通过知识存贮和整合两种类型的认知能力,将个人层面的知识如何转化为组织层面的知识,并形成组织的竞争优势,从而在智力资本,知识管理和战略人力资源管理建立一个逻辑一致的分析框架。 相似文献
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【目的/意义】爆发式增长的文献资源为传统的阅读活动带来了困难,也给STM图书的组织与服务提出了
新的要求。图书标注框架是图书内容与形式的规范化表示,对于解决图书的深度标引和知识关联具有重要意义。
【方法/过程】首先从用户需求出发明确了STM图书资源标注框架的构建原则,而后从图书的物理特征、内容特征、
增强特征和使用特征4个维度设计了本文框架包含的实体对象及其语义关系,最后展现了利用该框架进行STM图
书知识建模表示的过程,并通过应用案例验证了本文框架的可用性。【结果/结论】该标注框架具有需求驱动、多维
揭示、深度描述和语义关联等特征,对于支持STM图书资源的上层服务具有参考价值。【创新/局限】本文设计了
STM图书资源的标注框架,并选取应用实例探究其知识揭示与表达能力,但未能从知识服务视角对标注结果开展
进一步的应用研究。 相似文献
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《Information processing & management》2023,60(5):103447
Learning a continuous dense low-dimensional representation of knowledge graphs (KGs), known as knowledge graph embedding (KGE), has been viewed as the key to intelligent reasoning for deep learning (DL) and gained much attention in recent years. To address the problem that the current KGE models are generally ineffective on small-scale sparse datasets, we propose a novel method RelaGraph to improve the representation of entities and relations in KGs by introducing neighborhood relations. RelaGraph extends the neighborhood information during entity encoding, and adds the neighborhood relations to mine deeper level of graph structure information, so as to make up for the shortage of information in the generated subgraph. This method can well represent KG components in a vector space in a way that captures the structure of the graph, avoiding underlearning or overfitting. KGE based on RelaGraph is evaluated on a small-scale sparse graph KMHEO, and the MRR reached 0.49, which is 34 percentage points higher than that of the SOTA methods, as well as it does on several other datasets. Additionally, the vectors learned by RelaGraph is used to introduce DL into several KG-related downstream tasks, which achieved excellent results, verifying the superiority of KGE-based methods. 相似文献
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以清晰表达知识内涵、促进信息资源语义共享为目的,以卫生监督调查信息为研究案例,尝试利用顶级本体属性元素构建具有通用语义特征的领域本体。通过领域知识本体的构建实证,详尽阐述依托“七步法”构筑领域本体的方法,探讨使用领域术语构建本体知识表达的途径,实现了卫生监督调查信息知识的本体化,为该领域知识的信息资源语义整合提供研究基础。 相似文献
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This paper aims at developing a framework to analyse the structure and the dynamics of the knowledge base in the education sector. The main purpose is to explain why the creation and circulation of knowledge do not work well and what kind of transformations should be implemented at the system level to improve the process of knowledge advances. Three main issues are at stake: the interface between science (in this case, the educational research) and technology (the practical knowledge used by teachers), the low level of codification, which hampers the access to and the expansion of the professional knowledge base and the incentive problems posed by the need for knowledge and innovation diffusion across classrooms and schools. 相似文献
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成员的知识流动意愿是团队知识流动水平和合作创新绩效的主要影响因素之一.基于知识网络分析框架,围绕虚拟科技创新团队知识流动意愿的影响因素和作用路径展开研究,构建了包括制度机制、网络氛围、节点特征以及网络结构4个关键影响因素的理论模型,选取我国东北三省多所高校内有代表性的24个具有虚拟性质的科技创新团队为实证研究对象,运用结构方程模型对理论模型进行了分析和验证,并根据研究结果提出了提高成员知识流动意愿的对策建议,为相关管理人员提供借鉴和指导. 相似文献
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Automatic text summarization attempts to provide an effective solution to today’s unprecedented growth of textual data. This paper proposes an innovative graph-based text summarization framework for generic single and multi document summarization. The summarizer benefits from two well-established text semantic representation techniques; Semantic Role Labelling (SRL) and Explicit Semantic Analysis (ESA) as well as the constantly evolving collective human knowledge in Wikipedia. The SRL is used to achieve sentence semantic parsing whose word tokens are represented as a vector of weighted Wikipedia concepts using ESA method. The essence of the developed framework is to construct a unique concept graph representation underpinned by semantic role-based multi-node (under sentence level) vertices for summarization. We have empirically evaluated the summarization system using the standard publicly available dataset from Document Understanding Conference 2002 (DUC 2002). Experimental results indicate that the proposed summarizer outperforms all state-of-the-art related comparators in the single document summarization based on the ROUGE-1 and ROUGE-2 measures, while also ranking second in the ROUGE-1 and ROUGE-SU4 scores for the multi-document summarization. On the other hand, the testing also demonstrates the scalability of the system, i.e., varying the evaluation data size is shown to have little impact on the summarizer performance, particularly for the single document summarization task. In a nutshell, the findings demonstrate the power of the role-based and vectorial semantic representation when combined with the crowd-sourced knowledge base in Wikipedia. 相似文献
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知识流动机理的三维分析模式 总被引：24，自引：2，他引：24
知识流量可以用来描述知识分配和扩散的基本状况,在此,借鉴流体力学分析思想,从知识区位、动机水平、环境等方面提出一个分析知识流动动力机制的基本框架。 相似文献
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《Information processing & management》2020,57(3):102185
Emotional recognition contributes to automatically perceive the user’s emotional response to multimedia content through implicit annotation, which further benefits establishing effective user-centric services. Physiological-based ways have increasingly attract researcher’s attention because of their objectiveness on emotion representation. Conventional approaches to solve emotion recognition have mostly focused on the extraction of different kinds of hand-crafted features. However, hand-crafted feature always requires domain knowledge for the specific task, and designing the proper features may be more time consuming. Therefore, exploring the most effective physiological-based temporal feature representation for emotion recognition becomes the core problem of most works. In this paper, we proposed a multimodal attention-based BLSTM network framework for efficient emotion recognition. Firstly, raw physiological signals from each channel are transformed to spectrogram image for capturing their time and frequency information. Secondly, Attention-based Bidirectional Long Short-Term Memory Recurrent Neural Networks (LSTM-RNNs) are utilized to automatically learn the best temporal features. The learned deep features are then fed into a deep neural network (DNN) to predict the probability of emotional output for each channel. Finally, decision level fusion strategy is utilized to predict the final emotion. The experimental results on AMIGOS dataset show that our method outperforms other state of art methods. 相似文献