基于知识图谱的智能决策支持技术及应用研究

知识图谱是把复杂的领域知识通过数据挖掘、信息处理、知识计量和图形绘制而显示出来,解释知识领域的动态发展规律。知识图谱把所有不同种类的信息(heterogeneous information)连接在一起得到一个关系网络并从"关系"的角度去分析问题。知识图谱目前被广泛应用于智能搜索、智能问答等领域。提出了一种基于知识图谱的智能决策支持框架,用于解决传统决策支持系统存在的问题。通过大数据、知识图谱等海量知识分析和模型构建技术,结合决策支持系统,增强对问题的分解与处理、形成具有关系型网络的知识系统。最后结合电信领域中的经典决策案例,搭建基于知识图谱的欺诈电话智能决策支撑平台。和传统的决策支持系统比较,该研究方法的优点在于结合大数据处理方法提升了知识建模的算力和决策支持的效率,使实时处理大规模信息数据成为现实;基于知识图谱的关系型网络,提升了决策模型的准确性和关联相关性。     

DECISION SUPPORT WITH LOGICAL AND FUZZY PETRI NETS

Modern knowledge representation is a very dynamic domain because of continuous research and development. This paper presents Logical Petri Nets (LPNs) and Fuzzy Petri Nets (FPNs) as models for knowledge representation. It is shown that knowledge propagation, described using logical and fuzzy Petri nets, terminates in a unique stable state. Based on this result, the paper introduces an algorithm for knowledge propagation in decision support systems.     

粗糙集理论及其在数据挖掘中的应用

粗糙集理论是一种研究不完整、不确定知识处理的数学工具,近几年来已经在诸多领域得到应用,在机器学习、知识发现、算法研究、决策支持系统以及模式识别等应用中取得了较好的成果.本文在阐述粗糙集理论概念的基础上,介绍了粗糙集理论在数据挖掘中的两个应用.     

综合决策支持系统框架的研究

文中在对现有的几种决策支持系统框架结构进行分析研究的基础上,将数据仓库(DW)、数据挖掘(DM)、联机分析处理(OLAP)、数据库(DB)、模型库(MB)和知识库(KB)集成,引入了综合决策支持系统(SDSS)框架的概念。文中详细阐述了该框架各组成部分的功能及相互关系,总结了此种框架的基本特征及开发实现的关键技术。     

Context-prediction performance by a dynamic Bayesian network: Emphasis on location prediction in ubiquitous decision support environment

Ubiquitous decision support systems require more intelligent mechanism in which more timely and accurate decision support is available. However, conventional context-aware systems, which have been popular in the ubiquitous decision support systems field, cannot provide such agile and proactive decision support. To fill this research void, this paper proposes a new concept of context prediction mechanism by which the ubiquitous decision support devices are able to predict users’ future contexts in advance, and provide more timely and proactive decision support that users would be satisfied much more. Especially, location prediction is useful because ubiquitous decision support systems could dynamically adapt their decision support contents for a user based on a user’s future location. In this sense, as an alternative for the inference engine mechanism to be used in the ubiquitous decision support systems capable of context-prediction, we propose an inductive approach to recognizing a user’s location by learning a dynamic Bayesian network model. The dynamic Bayesian network model has been evaluated with a set of contextual data from undergraduate students. The evaluation result suggests that a dynamic Bayesian network model offers significant predictive power in the location prediction. Besides, we found that the dynamic Bayesian network model has a great potential for the future types of ubiquitous decision support systems.     

A general knowledge mediation infrastructure for multi-agent systems

Intelligent decision making needs to be equipped with broader knowledge in order to enhance the decision quality. Knowledge for decision making can be categorized as domain specific and general. Applying domain knowledge in intelligent systems is not new, but applying general knowledge to support business decision making is a possible way to obtain an edge over competitors. For this reason, the paper focuses primarily on designing a general knowledge mediation infrastructure (GKMI) which supports the use of general knowledge from multiple heterogeneous sources, and provides an unified access point for typical multi-agent systems (MAS) to access that knowledge. The finite state automaton (FSA) is used to model and analyze the commonsense inference ability of GKMI. By carrying out two use cases of GKMI for MAS development and operation the effectiveness of this infrastructure is examined.     

Knowledge integration using problem spaces: A study in resource-constrained project scheduling

The need to develop schedules for projects with resource constraints and cash flows arises in organizational settings ranging from construction planning to research and development. Given the intractable nature of the problem, a variety of knowledge sources relevant to the project scheduling task have been identified in the Operations Management literature. These include a large number of heuristic procedures that can be used to generate feasible project schedules as well as recent neural network-based approaches that can select appropriate heuristic procedures to apply to a specific instance of the project scheduling problem. While integrated application of these knowledge sources is required to effectively support scheduling, previous work has focussed on developing and implementing them in isolation. The problem space computational model presented in this paper addresses this shortcoming by integrating these various knowledge sources, thus enabling the development of decision support systems for resource constrained project scheduling. More generally, the modeling approach used in this paper can be applied to create systems to assist knowledge intensive tasks that arise in many organizational settings.     

Parallel inference in dynamic decision support systems of a semiotic type

The organization of parallel inference in dynamic decision support systems (DDSS) of a semiotic type, oriented towards a solving of ill-formed problems in dynamic applied domains, is considered. As a knowledge representation model, there are used production rules reflecting expert knowledge about a problem domain, an environment and decision making processes. The main concepts and assertions defining possibility and impossibility of parallel executing the production rules are given. Several types of parallelism in an inference process are introduced. The corresponding algorithm of parallel inference is described. Thus, the purpose of this paper is to develop and to research parallel inference methods and procedures that provide efficient processing a large amount of production rules for DDSS of a semiotic type.     

Traceability and management of dispersed product knowledge during design and manufacturing

Product development processes comprise highly creative and knowledge-intensive tasks that involve extensive information exchange and communication among geographically distributed teams. Due to the geographical and institutional separation between the different systems involved in the product lifecycle, product knowledge sharing is becoming a key issue in the information systems of extended enterprises. This paper addresses the issue and challenges of product knowledge traceability during the product development. The aim of this research effort is to enhance the sharing and use of product knowledge acquired during the development process using traceability information.A standardized approach is proposed to trace and share product knowledge and key constructs to support traceability during the product development process are identified and formalized. This research effort is based on the premise that an important step towards achieving product knowledge sharing is providing traceability across various product knowledge elements that are used in product development phases, i.e. design and manufacturing. Two disjointed but complementary case studies illustrating the benefit of traceability are presented. The potential role of traceability is described, first to support the decision making process during engineering change management (ECM), and second to support product-oriented modelling for knowledge sharing and exchanging to meet the quality requirements. The proposed approach has been implemented using the MEGA Suite tool and applied to each of the case studies and could be integrated to PLM systems currently in use.     

A paradigm for active decision support

Active decision support is concerned with developing advanced forms of decision support where the support tools are capable of actively participating in the decision making process, and decisions are made by fruitful callaboration between the human and the machine. It is currently an active and leading area of research within the field of decision support systems. The objective of this paper is to share the details of our research in this area. We present our overall research strategy for exploring advanced forms of decision support and discuss in detail our research prototype called JANUS that implements our ideas. We establish the contributions of our work and discuss our experiences and plans for future.     

DSS development and applications in China

This paper provides an overview of the application and development of decision support systems in China. The curriculum and research in decision support systems in major Chinese universities is introduced first, then selected recent applications of decision support systems in China is described. Finally, the main research topics and direction on decision support systems in China are discussed.     

Knowledge management component in managing human resources for enterprises

Both human resources and knowledge are valuable assets for enterprises; therefore, effective management of these assets becomes inevitably critical for business success. While human resource management systems have been a research topic for more than two decades, knowledge management systems applied to human resource management are relatively new to both academia and industry. This study examines the use of knowledge management in a business environment such as human resource management. The evolution of information systems and information processing in the human resource management domain is presented, and a knowledge-based decision support system for human resource management is proposed.     

Knowledge Discovery in Texts for Constructing Decision Support Systems

This paper presents a Text Mining approach for discovering knowledge in texts to later construct decision support systems. Text mining can take advantage of knowledge stored in textual documents, reducing the effort for knowledge acquisition. The approach consists in performing a mining process on concepts present in texts instead of working with words. The assumption is that concepts represent real world events and characteristics better than words, allowing the understanding and the explanation of the reasoning used in decision processes. The proposed approach extracts concepts expressed in natural phrases, and then analyzes their distributions and associations. Concepts distributions and associations are used to characterize classes or situations. After the discovery process, the obtained knowledge can be embedded in automated systems to classify elements or to suggest actions or solutions to problems. In this paper, experiments using the approach in a psychiatric domain are discussed. Concepts extracted from textual medical records represent patients' symptoms, signals and social/behavior characteristics. An automatic system was constructed with the approach: a classifier whose goal is to help physicians in disease diagnoses. Results from this system show that the approach is feasible for constructing decision support systems with satisfactory performance.     

An experimental evaluation of comprehensibility aspects of knowledge structures derived through induction techniques: a case study of industrial fault diagnosis

Machine induction has been extensively used in order to develop knowledge bases for decision support systems and predictive systems. The extent to which developers and domain experts can comprehend these knowledge structures and gain useful insights into the basis of decision making has become a challenging research issue. This article examines the knowledge structures generated by the C4.5 induction technique in a fault diagnostic task and proposes to use a model of human learning in order to guide the process of making comprehensive the results of machine induction. The model of learning is used to generate hierarchical representations of diagnostic knowledge by adjusting the level of abstraction and varying the goal structures between 'shallow' and 'deep' ones. Comprehensibility is assessed in a global way in an experimental comparison where subjects are required to acquire the knowledge structures and transfer to new tasks. This method of addressing the issue of comprehensibility appears promising especially for machine induction techniques that are rather inflexible with regard to the number and sorts of interventions allowed to system developers.     

基础信息层经验规则的一种表达方法

该文讨论在复杂的大型辅助决策系统中,构造智能决策规则模型的一种方法。这是一种基于决策表的知识表示方法。它在传统决策表的基础上,吸收了产生式规则、框架表示法、模糊理论、关系模型等多种方法的思想和技术,把传统决策表加以扩展,得到了一种结构性好、表达能力强、可操作性较好的智能决策表达工具,用来表示大型辅助决策系统中的复杂领域知识,将其中松散的经验规则形式化成智能决策规则模型,从而增强其结构性和可操作性,有效支持对其它信息的操作。     

Structured modeling group support systems: a product design theory

Structured modeling is critical to the design, development, and implementation of many systems including computer software, business processes, and data networks. Since the creation of structured models relies on the knowledge of many organizational stakeholders, groups often accomplish this task. Group support systems (GSS) focus on the support of group processes and would appear to be useful for structured modeling; however, GSS usually only provide textual or decision related output rather than the structured models needed for many design processes. This paper proposes a class of systems, structured modeling GSS (smGSS), which adds support for the development of structured models to standard GSS. Since past research has shown that research results may be difficult to compare across studies when the system under investigation is not well defined, this paper develops a product design theory that defines the required characteristics of and testable design propositions for an smGSS as derived from existing theory and empirical investigations.     

The Importance of Active Exploration,Optical Flow,and Task Alignment for Spatial Learning in Desktop 3D Environments

The Decision Support Systems (DSS) field has grown rapidly, drawing technology from many disciplines and pursuing applications in a variety of domains but developing little underlying theoretical structure and poor linkage between research and practice. This article presents a classification scheme for DSS techniques that provides a common theoretical framework for DSS research and also structures and simplifies the process of designing application systems. The classification system is functional, grouping DSS techniques according to their ability to provide similar kinds of support (i.e., functions) to a human decision maker. It is also cognitively based, defining the kinds of support that decision makers need in terms of architectural features and procedural aspects of human cognition. The classification is expressed as a taxonomy, encompassing six primary classes of decision support techniques representing the six general kinds of cognitive support that human decision makers need. The six classes are process models, which assist in projecting the future course of complex processes; choice models, which support integration of decision criteria across aspects and/or alternatives; information control techniques, which help in storage, retrieval, organization, and integration of data and knowledge; analysis and reasoning techniques, which support application of problem-specific expert reasoning procedures; representation aids, which assist in expression and manipulation of a specific representation of a decision problem; and judgment amplification/refinement techniques, which help in quantification and debiasing of heuristic judgments. Additional distinctions are provided to distinguish the individual techniques in each of these primary categories. The taxonomy also has practical use as a design aid for decision support systems. The kinds of decision support needs represented by the taxonomy are general and can be used to guide the analysis and decomposition of a given decision prior to decision aid design. Specific needs for assistance can then be tied to specific computational techniques in the taxonomy. Methodological suggestions for using the taxonomy as a design aid are given.     

A design for a common‐sense knowledge‐enhanced decision‐support system: Integration of high‐frequency market data and real‐time news

According to efficient markets theory, information is an important factor that affects market performance and serves as a source of first‐hand evidence in decision making, in particular with the rapid rise of Internet technologies in recent years. However, a lack of knowledge and inference ability prevents current decision support systems from processing the wide range of available information. In this paper, we propose a common‐sense knowledge‐supported news model. Compared with previous work, our model is the first to incorporate broad common‐sense knowledge into a decision support system, thereby improving the news analysis process through the application of a graphic random‐walk framework. Prototype and experiments based on Hong Kong stock market data have demonstrated that common‐sense knowledge is an important factor in building financial decision models that incorporate news information.