Data Mining for Generating Predictive Models of Local Hydrology

The problem of downscaling the effects of global scale climate variability into predictions of local hydrology has important implications for water resource management. Our research aims to identify predictive relationships that can be used to integrate solar and ocean-atmospheric conditions into forecasts of regional water flows. In recent work we have developed an induction technique called second-order table compression, in which learning can be viewed as a process that transforms a table consisting of training data into a second-order table (which has sets of atomic values as entries) with fewer rows by merging rows in consistency preserving ways. Here, we apply the second-order table compression technique to generate predictive models of future water inflows of Lake Okeechobee, a primary source of water supply for south Florida. We also describe SORCER, a second-order table compression learning system and compare its performance with three well-established data mining techniques: neural networks, decision tree learning and associational rule mining. SORCER gives more accurate results, on the average, than the other methods with average accuracy between 49% and 56% in the prediction of inflows discretized into four ranges. We discuss the implications of these results and the practical issues in assessing the results from data mining models to guide decision-making.     

Different roles and mutual dependencies of data, information, and knowledge — An AI perspective on their integration

The unclear distinction between data, information, and knowledge has impaired their combination and utilization for the development of integrated systems. There is need for a unified definitional model of data, information, and knowledge based on their roles in computational and cognitive information processing. An attempt to clarify these basic notions is made, and a conceptual framework for integration is suggested by focusing on their different roles and frames of reference within a decision-making process. On this basis, ways of integrating the functionalities of databases, information systems and knowledge-based systems are discussed by taking a knowledge level perspective to the analysis and modeling of systems behaviour. Motivated by recent work in the area of case-based reasoning related to decision support systems, it is further shown that a specific problem solving episode, or case, may be viewed as data, information, or knowledge, depending on its role in decision making and learning from experience. An outline of a case-based system architecture is presented, and used to show that a focus on the retaining and reuse of past cases facilitates a gradual and evolutionary transition from an information system to a knowledge-based system.     

An end-to-end tabular information-oriented causality event evolutionary knowledge graph for manufacturing documents

Industrial tabular information extraction and its semantic fusion with text (ITIESF) is of great significance in converting and fusing industrial unstructured data into structured knowledge to guide cognitive intelligence analysis in the manufacturing industry. A novel end-to-end ITIESF approach is proposed to integrate tabular information and construct a tabular information-oriented causality event evolutionary knowledge graph (TCEEKG). Specifically, an end-to-end joint learning strategy is presented to mine the semantic information in tables. The definition and modeling method of the intrinsic relationships between tables with their rows and columns in engineering documents are provided to model the tabular information. Due to this, an end-to-end joint entity relationship extraction method for textual and tabular information from engineering documents is proposed to construct text-based knowledge graphs (KG) and tabular information-based causality event evolutionary graphs (CEEG). Then, a novel NSGCN (neighborhoods sample graph convolution network)-based entity alignment is proposed to fuse the cross-knowledge graphs into a unified knowledge base. Furthermore, a translation-based graph structure-driven Q&A (question and answer) approach is designed to respond to cause analysis and problem tracing. Our models can be easily integrated into a prototype system to provide a joint information processing and cognitive analysis. Finally, the approach is evaluated by employing the aerospace machining documents to illustrate that the TCEEKG can considerably help workers strengthen their skills in the cause-and-effect analysis of machining quality issues from a global perspective.     

Knowledge Discovery with Second-Order Relations

This paper presents an induction technique that discovers a set of classification rules, from a set of examples, using second-order relations as a representational model. Second-order relations are database relations in which tuples have sets of atomic values as components. Using sets of values, which are interpreted as disjunctions, provides compact representations that facilitate efficient management and enhance comprehensibility. The second-order relational framework is based on theoretical foundations that link relational database theory, machine learning, and logic synthesis. The rule induction technique can be viewed as a second-order relation compression problem in which the original relation, representing training data, is transformed into a second-order relation with fewer tuples by merging tuples in ways that preserve consistency with the training data. This problem is closely related to two-level Boolean function minimization in logic synthesis. We describe a rule-mining system, SORCER, and compare its performance to two state-of-the-art classification systems: C4.5 and CBA. Experimental results based on the average of error rates over 26 data sets show that SORCER, using a simple compression scheme, outperforms C4.5 and is competitive to CBA. Using a slightly more sophisticated compression scheme, SORCER outperforms both C4.5 and CBA. Received 5 October 1999 / Revised 15 February 2001 / Accepted in revised form 10 August 2001 Correspondence and offprint requests to: R. Hewett, Institute for Human and Machine Cognition, University of West Florida, 40 South Alcaniz Street, Pensacola, FL 32501, USA. Email: rhewett@ai.uwf.eduau     

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。     

From decision tables to expert system shells

Building and maintaining high quality knowledge based systems is not a trivial task. Decision tables have sometimes been recommended in this process, mainly in verification and validation. In this paper, however, it is shown how decision tables can also be used to generate, and not just to validate, knowledge bases and how the transformation process from decision tables to knowledge bases can be organized. Several options to generate rules or other knowledge representation from decision tables are described and evauluated.

The proposed generation strategy enables the knowledge engineer to concentrate on the acquisition and modelling issues and allows him to isolate the knowledge body from its implementation. The generation process has been implemented for two commercial tools, AionDS and KBMS and has been applied to real world applications.     

基于人工神经网络和专家系统Shel的城市发展水平综合评价专家系统的研究

基于人工神经网络（ＡＮＮ）和专家系统Ｓｈｅｌ（ＥＳＳ），提出一种城市发展水平综合评价专家系统（ＣＣＥＥＳ）的基本结构模式，并对ＣＣＥＥＳ中评价指标规范化方法、基于ＡＮＮ的知识获取方法、基于决策表的知识库自动生成方法以及推理机的工作原理作了描述。ＣＣＥＥＳ现已在ＩＢＭＰＣ３８６／４８６微机上用ＥＳＳ—ＶＰ－Ｅｘｐｅｒｔ和ＴｕｒｂｏＣ、ＦＯＸＢＡＳＥ＋语言实现，并取得了较好的应用效果     

A framework for knowledge-based temporal abstraction

A new domain-independent knowledge-based inference structure is presented, specific to the task of abstracting higher-level concepts from time-stamped data. The framework includes a model of time, parameters, events and contexts. A formal specification of a domain's temporal abstraction knowledge supports acquisition, maintenance, reuse and sharing of that knowledge.

The knowledge-based temporal abstraction method decomposes the temporal abstraction task into five subtasks. These subtasks are solved by five domain-independent temporal abstraction mechanisms. The temporal abstraction mechanisms depend on four domain-specific knowledge types: structural, classification (functional), temporal semantic (logical) and temporal dynamic (probabilistic) knowledge. Domain values for all knowledge types are specified when a temporal abstraction system is developed.

The knowledge-based temporal abstraction method has been implemented in the RÉSUMÉ system and has been evaluated in several clinical domains (protocol-based care, monitoring of children's growth and therapy of diabetes) and in an engineering domain (monitoring of traffic control), with encouraging results.     

The role of decision support systems in an indeterminate world

Decision making involves processing or applying information and knowledge, and the appropriate information/knowledge mix depends on the characteristics of the decision making context. Information (or its absence) is central to decision making situations involving uncertainty and complexity, while knowledge (or its absence) is associated with problems of ambiguity and equivocality. This paper proposes that computer-based decision support technologies are appropriate to supporting decision making under conditions of uncertainty and complexity, while human-centric approaches may be more appropriate under conditions of ambiguity or equivocality. Both approaches, however, must be tightly integrated for organizational learning to occur. The framework is illustrated with a case study of the implementation of a decision support system used for price quoting in a leasing company.     

知识获取的粗分析方法

粗分析方法是从决策表中挖掘规则,克服知识获取瓶颈的一种有效方法。基于粗糙集理论,文章讨论了粗分析获取规则的几个问题,提出了相应的解决方法。如实用中决策表的约简会导致部分信息丢失,而规则适当的冗余可以解决这个问题。其次,研究了不完全决策表的规则粗分析,为不完全决策提供了基础。     

启发式知识获取方法研究

归纳学习是解决知识自动获取的有效方法，针对ID3算法、基于粗集的归纳学习以及其它一些归纳学习方法存在的问题，提出了一种新的归纳学习算法ITIL。此算法用信息增益为启发式，选择尽量少的重要属性或组合，以可分辨性为依据提取规则，许多实例表明，这些规则不仅简单，而且冗余小，作为知识获取模块的一部分，ITIL已被集成到一个“基于知识发现的医疗诊断辅助系统”动态知识库子系统中。     

A decision-making model for knowledge collaboration and reuse through scientific workflow

Past, present and future, to realize the aim of product CTQS (i.e., lower cost, faster time to market, higher quality and better service) with manufacturing intelligence, few manufacturers have no longer engaged in product related production decision support problem (P-DSP). However, P-DSP solving (P-DSPS) is a multi-criteria decision-making problem, which is context sensitive in solution objects-attributes and chaos in the decision process of manufacturing knowledge collaboration and reuse. To alleviate these limitations, this paper presents a novel triple deep workflow model for P-DSPS. Driven by a wicked task query, the proposed workflow of P-DSPS (WP-DSPS) has the function to retrieve similarity-based alternatives from domain knowledge driven solution flow (KSF) and to evaluate with expert knowledge collaboration from knowledge driven decision flow (KDF) based on utility theory under the task event driven control flow (ECF) strategy and operation logic. In the view of alternative adaption, a domain knowledge ontology-based degree of similarity (DoS) determines the P-DSPS alternatives width, a utility function-based degree of decision (DoD) determines alternatives quality, and a belief-based knowledge fusion technique is used to synthesize decision conflicts with a consensus degree (CD). To support the proposed models, a workflow-based system prototype is proposed and validated in two case studies.     

Introducing functional classification theory to land use planning by means of decision tables

This paper contributes to the conceptualisation and analysis of double-sided matching problems, taking the land use planning problem as an example. It does so by introducing functional classification theory at the knowledge level, the symbol level and the system level of a DSS. This theory explicitly expresses the methodological viewpoint of relational realism. At the knowledge level this implies defining knowledge on the basis of matching the intension and extension of concepts. At the symbol level it deals with knowledge representation and here decision tables are advanced and formally introduced. At the system level the formalism used at the symbol level is implemented to develop a relational matching DSS.     

A mix method of knowledge capitalization in maintenance

This paper deals with knowledge capitalization in maintenance especially in diagnosis and repair of industrial equipments. The goal is to propose a method of knowledge capitalization in order to develop a decision support system for maintenance operators. The knowledge capitalization cycle was adopted as the underlying principle. It consists of four principal steps: detect, preserve, capitalize and actualize the strategic knowledge. Different knowledge management tools and methods that can be used in the cycle are reviewed. We propose a mix method of knowledge capitalization in maintenance. This method integrates a representation and a reasoning model both completing each other and suitable to represent and manipulate the domain knowledge. The knowledge representation model using unified modelling language (UML) diagram proposes different domain models based on maintenance analysis to guide the domain expertise. The reasoning model uses the case-based reasoning which allows the manipulation of represented domain knowledge. Finally, the method is implemented on the pallet transfer system Sormel in the context of Proteus e-maintenance platform.     

基于模糊聚类的粗糙集决策表简化方法研究

决策表是一种特殊而重要的知识系统,在决策支持和数据挖掘等领域有着重要的应用。该文给出了一种基于模糊聚类的粗糙集决策表分析方法。该方法结合模糊集和粗糙集理论,由模糊聚类得出模糊决策表,并可以方便地构造决策表和对决策规则表进行简化。     

基于智能主体的决策资源管理

随着Internet的广泛普及,人们可以随时随地获取自己想要的信息,然而海量信息却带来了一场新的知识危机,也就是说,人们被知识的海洋所淹没。知识管理技术正是解决这种知识危机的重要而有效的手段,是知识工程和信息系统中的重要研究内容。论文介绍了该技术在决策资源管理中的应用,提出了一个基于智能主体的决策支持系统的体系结构,并且详细描述了基于本体与RDF的决策资源的知识表示,最后用一个实例阐述了这种决策资源管理方法的实现过程。     

Measures for evaluating the decision performance of a decision table in rough set theory

As two classical measures, approximation accuracy and consistency degree can be employed to evaluate the decision performance of a decision table. However, these two measures cannot give elaborate depictions of the certainty and consistency of a decision table when their values are equal to zero. To overcome this shortcoming, we first classify decision tables in rough set theory into three types according to their consistency and introduce three new measures for evaluating the decision performance of a decision-rule set extracted from a decision table. We then analyze how each of these three measures depends on the condition granulation and decision granulation of each of the three types of decision tables. Experimental analyses on three practical data sets show that the three new measures appear to be well suited for evaluating the decision performance of a decision-rule set and are much better than the two classical measures.     

MATISSE: a relational expert system for industrial site selection

This paper reports on the development of a relational knowledge-based decision support system for urban planning in general and industrial site selection in particular. The system treats the concept of site suitability as a matching process, using decision tables (DTs). The proposed computer-based system is tested using the locational choice problem of an industrial company. The system has been given the acronym MATISSE: “Matching Algorithm, A Technique for Industrial Site Selection and Evaluation”. The knowledge base of the system was created by conducting a series of in-depth interviews supplemented with a detailed survey of the relevant literature. Using this information, a series of decision tables could be constructed using prologa95. In total, 90 crisp (sub)decision tables were constructed. This set of DTs can be used as a decision support system to select and evaluate potential sites, given a set of locational requirements.     

The importance of item distinctiveness on performance using a menu selection system

Novice computer users searched an interactive menu system given either an explicit target phrase or a subject-matter topic. Two menus were used: an original menu as designed by a commercial timesharing service and a slightly modified version intended to increase the distinctiveness of same-level items. Subjects acquired knowledge about the system through one of four study methods: trial-and-error exploration, study of a diagram of the menu structure, trial-and-error exploration with documentation, or study of the diagram with documentation. Subjects using the modified menu (a) took less time per problem: lpar;b) found targets in a more direct path: and (c) gave up on fewer problems than subjects using the original menu. These results are consistent with a theory of choice that predicts that decision processes are facilitated by the distinctiveness of the alternatives. Overall the effect of study method was not significant. For highly meaningful menus, type of exposure, whether trial-and-error or study of the global tree, does not seem to matter.     

A link between k-nearest neighbor rules and knowledge based systems by sequence analysis

We propose, in this paper, non-parametric decision rules used in a statistical pattern recognition system with incomplete knowledge. These decision rules are based on the examination of membership sequences of k-nearest neighbors. Beyond these parameters, we introduce the non-decision. To apply the decision rules, an expert system is used, which is also described in this paper. Lastly, a numerical and simulated example is given.