因素空间理论与知识表示的数学框架(XⅢ)──描述架中概念的结构(2)

本文是［１，１２］的继续，研究描述架中概念的结构；本文讨论后半部分，内容涉及概念内涵与外延的转换，清晰关系的内投影与内变换，概念的结构＊以及有关问题的注记。     

“电脑医学博学士”普及机版的若干设计方法

“电脑医学博学士”（ＤＮＹＢ）是一个巨大的计算机辅助诊断咨询系统。它的核心是“疾病诊断标准的医学知识库”。程序的研制应用了我们设计的高密度知识表达方法，它包含了１０００１个诊断单元的诊断标准，描述了近５０００条诊断指标。这一巨型知识库首先在低价的普及型微机上实现，它能帮助提示典型疾病和辅助诊断学教学。这一知识库将不断完善和扩大。     

数字教育技术在民族声乐教学中的应用

数字技术的快速发展,以及其在民族声乐教学中的应用,为改变传统民族声乐教学方法,提高教学水平提供了很大的便利。将民族声乐教学与数字教育技术进行结合,能够确保民族声乐教学内容更加丰富、形象更加多样、教学更加具体,进而能够有效提高民族声乐教学的教学质量。     

Improving Knowledge Graph Completion Using Soft Rules and Adversarial Learning

Knowledge graph is a useful resources and tools for describing entities and relationships in natural language processing tasks.However,the existing knowledge graph are incomplete.Therefore,knowledge graph completion technology has become a research hotspot in the field of artificial intelligence,but the traditional knowledge graph embedding method does not fully take into account the role of logic rules and the effect of false negative samples on knowledge embedding.Based on the logic rules of knowledge and the role of adversarial learning in knowledge embedding,we proposes a model to improve the completion of knowledge graph:soft Rules and graph adversarial learning (RUGA).Firstly,the traditional knowledge graph embedding model is trained as generator and discriminator by using adversarial learning method,and high-quality negative samples are obtained.Then these negative samples and the existing positive samples together constitute the label triple in the injection rule model.The whole model will benefit from both high-quality samples and logical rules.In addition,we evaluated the performance of link prediction task and triple classification task on Freebase and Yago datasets respectively.Finally,the experimental results show that the model can effectively improve the effect of knowledge graph completion.     

智能学科知识检索系统中的本体构建及推理

为了充分发挥知识的效能,智能性的分析和满足用户的对隐性和显性的学科知识检索需求,文中针对学科知识的特点,使用本体技术给学科知识进行建模.并详细分析和阐释了如何从本体知识库构建及扩展和本体推理两个方面让检索系统更具有智能性,本文的研究成果丰富了知识检索领域的研究内容,提供了智能化检索系统的技术和方法指导.     

数据专业设计标准化若干问题的思考与实践

以数据专业设计标准化为案例,对设计流程、设计模板及加速知识共享等进行了分析,提出了“聚焦设计标准化,提升设计服务水平”的理念,并探讨了在设计服务商内部推广的可行性。     

Analyzing the effects of technological,organizational and competition factors on Web knowledge exchange in SMEs

Internet technologies are increasingly being used within all knowledge management processes, including knowledge acquisition, knowledge exchange and knowledge use. Although technological issues are key drivers for Internet technologies adoption and use, organizational and environmental aspects have been found to be equally important. This paper extends previous studies on the use of Internet technologies and knowledge management by analyzing factors affecting Web knowledge exchange in small and medium-sized enterprises (SMEs). More specifically, by drawing on the technology–organization–environment framework, a model to examine how distinct contextual factors influence Web knowledge exchange in SMEs is developed. The hypotheses are tested by using structural equation modelling on a large sample of Spanish SMEs from different industries. Results suggest that IT expertise and commitment-based human resource practices positively affect Web knowledge exchange, with the latter being the strongest factor in our proposed model. In contrast, a negative relationship is found between competition and Web knowledge exchange.     

Intensional dynamic programming. A Rosetta stone for structured dynamic programming

We present intensional dynamic programming (IDP), a generic framework for structured dynamic programming over atomic, propositional and relational representations of states and actions. We first develop set-based dynamic programming and show its equivalence with classical dynamic programming. We then show how to describe state sets intensionally using any form of structured knowledge representation and obtain a generic algorithm that can optimally solve large, even infinite, MDPs without explicit state space enumeration. We derive two new Bellman backup operators and algorithms. In order to support the view of IDP as a Rosetta stone for structured dynamic programming, we review many existing techniques that employ either propositional or relational knowledge representation frameworks.     

An argument-based approach to reasoning with clinical knowledge

Better use of biomedical knowledge is an increasingly pressing concern for tackling challenging diseases and for generally improving the quality of healthcare. The quantity of biomedical knowledge is enormous and it is rapidly increasing. Furthermore, in many areas it is incomplete and inconsistent. The development of techniques for representing and reasoning with biomedical knowledge is therefore a timely and potentially valuable goal. In this paper, we focus on an important and common type of biomedical knowledge that has been obtained from clinical trials and studies. We aim for (1) a simple language for representing the results of clinical trials and studies; (2) transparent reasoning with that knowledge that is intuitive and understandable to users; and (3) simple computation mechanisms with this knowledge in order to facilitate the development of viable implementations. Our approach is to propose a logical language that is tailored to the needs of representing and reasoning with the results of clinical trials and studies. Using this logical language, we generate arguments and counterarguments for the relative merits of treatments. In this way, the incompleteness and inconsistency in the knowledge is analysed via argumentation. In addition to motivating and formalising the logical and argumentation aspects of the framework, we provide algorithms and computational complexity results.     

An intercausal cancellation model for Bayesian-network engineering

When constructing Bayesian networks with domain experts, network engineers often use the noisy-OR model, and causal interaction models more generally, to alleviate the burden of probability elicitation: the use of such a model serves to reduce the number of probabilities to be elicited on the one hand, and on the other hand forestalls experts having to give assessments for probabilities with compound conditions which they feel are hard to envision. Recently, we have shown that ill-considered use of the noisy-OR model specifically can substantially decrease a network's performance, especially in domains in which causal mechanisms include cancellation effects. Motivated by this observation, we designed a new causal interaction model, with the same engineering advantages as the noisy-OR model, to describe such effects. We detail properties of our intercausal cancellation model, and compare it against existing causal interaction models. We further illustrate the application of our model in the real-world domain of pharmacology.