EarlyData knowledge base for material decisions in building design

To make use of the great opportunities for emission reduction in early building design, future emissions need to be calculated when only geometric, but no detailed material information about a building is available. Currently, early design phase life cycle assessments (LCAs) are heavily reliant on assumptions of specific material choices, leading to single point emission values which suggest a precision not representative for an early design stage. By adding knowledge about possible locations and functions of materials within a building to life cycle inventory (LCI) data, the EarlyData knowledge base makes LCA data sets accessible and more transparent. Additionally, “generic building parts” are defined, which describe building parts independently of precise material choices as a combination of layers with specific functions. During evaluation, enriched LCI data and generic building parts enable assessment of a vast number of possible material combinations at once. Thus, instead of single value results for a particular material combination, ranges of results are displayed revealing the building parts with the greatest emission reduction potential. The application of the EarlyData tool is illustrated on a use case comparing a wood building and a concrete building. The database is developed with extensibility in mind, to include other criteria, such as (life cycle) costs.     

Supporting multi-criteria decisions based on a hierarchical structure by taking advantage of acquired knowledge

In this paper, an approach to decision making is described. It combines a knowledge acquisition technique, a multi-attribute decision making technique, a validation technique and a machine learning algorithm. The suggested system is an extension of a previous decision support system based on the fuzzy repertory table technique. The aim is to increase its efficiency when dealing with a great amount of alternatives and criteria. The solution is based on a mechanism to divide the original problem into several simpler sub-problems. Moreover, a case study is presented to illustrate how the proposed system is used to design a Product Search Assistant. It will be integrated into a multi-agent architecture developed to give support to an e-marketplace.     

Supporting manufacturing-marketing interface management with a partially automated knowledge base: a precursor to computer-integrated business enterprise

Providing continuity and transparency of decisions across interfaces of domains such as manufacturing and marketing is becoming imperative for competitiveness. Since the issues involved in interface management are not very well understood its practice has not been very successful. In this paper, we explore this problem in a scientific way and establish a set of mappings across and within domains that form the building-blocks of our research. We develop mathematical models to help understand the characteristics of such mappings in a knowledge base environment. One of the models helps support the decision-making process of a manager within a domain while the other helps in detecting patterns of decisions made between domains.The knowledge base mechanism and the models together form a computer-oriented system which encourages synergy between manufacturing and marketing. Our approach therefore can be considered to be a precursor to a fully, i.e. across all different domains in a firm, computer-integrated business enterprise.     

Making decisions in a two-stage identification system with knowledge updating

In this article, an algorithm for updating knowledge in an adaptive system to support decision making is proposed. Taking into account the specific character of the process, a two-stage identification approach is used. The first stage is built for diagnostic purposes, i.e., the estimated parameters in the first-stage relationship are used to make a decision at the second stage. The proposed algorithms to support decision making at the second stage rest on knowledge extracted from human experts and the effects of the diagnosis (based on identification results) at the first stage. To examine the effectiveness of the proposed algorithm, a numerical simulation of a biomedical problem is carried out.     

基于知识库的客户网购意向预测系统

电子商务网站所产生的海量客户网购行为数据中蕴含着丰富的反映客户网购行为规律的知识。这些知识是进行客户网购意向预测以及在此基础上进一步进行商品推荐的重要依据。基于Hadoop框架构建了“客户购买意向预测系统”,实现了利用隐含在交易数据中的知识对正在浏览中的电商客户的购买意向预测。在基于知识图谱技术构建网购客户购买行为知识库的基础上,利用FP-Growth算法和数据统计方法实现了以增量方式从购买者的交易数据中获取反映用户购买规律的知识,并将取得的知识融合到知识库中。系统能够利用存储于知识库中的知识在客户进行网上购物的过程中对其购买意向进行分析和预测,从而达到提高电子商务平台的运营效率,改善客户购物体验的目的。     

Validating the knowledge base of a production system by the operator coverage criterion

Principles and algorithms are proposed for validating the knowledge base of a production system with the use of the operator coverage criterion. We analyze the complexity of algorithms that verify static correctness and generate test data.Translated from Kibernetika, No. 3, pp. 86–89, May–June 1990.     

多媒体知识库管理系统的Jasmine实现

随着网络技术和知识工程的飞速发展,网络多媒体数据日益增加,提出使用Jasmine面向对象数据库实现多媒体知识库管理系统,探讨了将面向对象,数据库和多媒体技术有机结合,构造出能对知识进行系统化的组织与管理,存储,增加,删除和查询等功能齐全的多媒体知识库管理系统。     

Supporting image retrieval framework with rule base system

The present paper introduces an image retrieval framework based on a rule base system. The proposed framework makes use of color and texture features, respectively called color co-occurrence matrix (CCM) and difference between pixels of scan pattern (DBPSP). These features are used to perform the image mining for acquiring clustering knowledge from a large empirical images database. Irrelevance between images of the same cluster is precisely considered in the proposed framework through a relevance feedback phase followed by a novel clustering refinement model. The images and their corresponding classes pass to a rule base system for extracting a set of accurate rules. These rules are pruning and may reduce the dimensionality of the extracted features. The advantage of the proposed framework is reflected in the retrieval process, which is limited to the images in the class of rule matched with the query image features. Experiments show that the proposed model achieves a very good performance in terms of the average precision, recall and retrieval time compared with other models.     

Heuristics for a multiperiod inventory routing problem with production decisions

Manufacturers who resupply a large number of retailers on a periodic basis continually struggle with the question of how to formulate a replenishment strategy. This paper presents a comparative analysis of a series of heuristics for an inventory routing problem (IRP) that arises in a manufacturing supply chain. The IRP is formulated as a mixed integer program with the objective of maximizing the net benefits associated with making deliveries in a specific time period to a widely dispersed set of customers. It is assumed that inventory can accumulate at the customer sites, but that all demand must be met without backlogging. Because optimal solutions were not within reach of exact methods, a two-step procedure was developed that first estimates daily delivery quantities and then solves a vehicle routing problem for each day of the planning horizon. As part of the methodology, a linear program is used to determine which days it is necessary to make at least some deliveries to avoid stockouts.The IRP is investigated in the context of an integrated production–inventory–distribution–routing problem (PIDRP). The full model takes into account production decisions and inventory flow balance in each period. For the computations, a previously developed branch-and-price algorithm is used that requires the solution of multiple IRPs (one in each period) to generate columns for the master problem. Testing showed that PIDRP instances with up to eight time periods and 50 customers can be solved within 1 h. This level of performance could not be matched by either CPLEX or an exact version of the branch-and-price algorithm.     

Building a knowledge base: an example

The main goal of this paper is to illustrate applications of some recent developments in the theory of logic programming to knowledge representation and reasoning in common sense domains. We are especially interested in better understanding the process of development of such representations together with their specifications. We build on the previous work of Gelfond and Przymusinska in which the authors suggest that, at least in some cases, a formal specification of the domain can be obtained from specifications of its parts by applying certain operators on specifications called specification constructors and that a better understanding of these operators can substantially facilitate the programming process by providing the programmer with a useful heuristic guidance. We discuss some of these specification constructors and their realization theorems which allow us to transform specifications built by applying these constructors to declarative logic programs. Proofs of two such theorems, previously announced in a paper by Gelfond and Gabaldon, appear here for the first time. The method of specifying knowledge representation problems via specification constructors and of using these specifications for the development of their logic programming representations is illustrated by design of a simple, but fairly powerful program representing simple hierarchical domains. This revised version was published online in June 2006 with corrections to the Cover Date.     

Case-based tutoring from a medical knowledge base

The past decade has seen the emergence of programs that make use of large knowledge bases to assist physicians in diagnosis within the general field of internal medicine. One such program, Internist-I, contains knowledge about over 600 diseases, covering a significant proportion of internal medicine. This paper describes the process of converting a subset of this knowledge base--in the area of cardiovascular diseases--into a probabilistic format, and the use of this resulting knowledge base to teach medical diagnostic knowledge. The system (called KBSimulator--for Knowledge-Based patient Simulator) generates simulated patient cases and uses these cases as a focal point from which to teach medical knowledge. This project demonstrates the feasibility of building an intelligent, flexible instructional system that uses a knowledge base constructed primarily for medical diagnosis.     

Expert guided integration of induced knowledge into a fuzzy knowledge base

This paper proposes a method for building accurate and interpretable systems by integrating expert and induced knowledge into a single knowledge base. To favor the cooperation between expert knowledge and data, the induction process is run under severe constraints to ensure the fully control of the expert. The procedure is made up of two hierarchical steps. Firstly, a common fuzzy input space is designed according to both the data and expert knowledge. The compatibility of the two types of partitions, expert and induced, is checked according to three criteria : range, granularity and semantic interpretation. Secondly, expert rules and induced rules are generated according to the previous common fuzzy input space. Then, induced and expert rules have to be merged into a new rule base. Thanks to the common universe resulting from the first step, rule comparison can be made at the linguistic level only. The possible conflict situations are managed and the most important rule base features, consistency, redundancy and completeness, are studied. The first step is thoroughly described in this paper, while the second is only introduced.     

烧结法氧化铝配料知识库编辑维护系统的实现

根据烧结法氧化铝生料浆配料机理和长期积累的专家经验知识,设计了一种适用于烧结法氧化铝配料的知识库编辑和维护系统,该系统从生产过程出发,将机理知识和群体专家知识统一为一种形式,通过产生式知识表示,对本系统的知识的特点进行了阐述,然后由知识的特点分别建立有关配料参数、状态、关系等字典,最后对系统的编辑和维护功能的实现进行了说明.实际使用效果表明,该知识库系统操作简单,编辑和维护方便.     

Integrating forward-chaining rules with operations and permanent knowledge bases

Traditionally, rule-based forward-chaining systems are considered to be standalone, working on a volatile memory. This paper focuses on the integration of forward-chaining rules with command-driven programming paradigms in the context of permanent, integrated knowledge bases. A system architecture is proposed that integrates the data management functions of large computerized knowledge bases into a module called a knowledge base management system (KBMS). Experiences we had in integrating rules with operations into a prototype KBMS called DALI are surveyed. For this integration, a new form of production rule, called the activation pattern controlled rule, is introduced, which augments traditional forward-chaining rules by a second, additional left-hand side, which allows making rules sensitive to calls of particular operations. Activation pattern controlled rules play an important role in DALI's system architecture, because they facilitate the storage of knowledge that has been specified relying on mixed programming, a combination of data-driven, command-driven, and preventive programming. The general problems of implementing permanent knowledge bases that contain rules and operations are discussed, and an algorithm for implementating activation pattern controlled rules, called IPTREAT, a generalization of the TREAT algorithm, is provided. Furthermore, the paper intends to clarify the differences between traditional, volatile rule-based systems and rule-based systems that are geared toward knowledge integration by supporting a permanent knowledge base.This paper is an extended and significantly revised version of a paper entitled Integrating Rules into a Knowledge Base Management System, which was presented at the First International Conference on Systems Integration, April 1990 [1].     

Supporting ordinal four-state classification decisions using neural networks

Many accounting and finance problems require ordinal multi-state classification decisions, (e.g., control risk, bond rating, financial distress, etc.), yet few decision support systems are available to aid decision makers in such tasks. In this study, we develop a Neural Network based decision support system (NN-DSS) to classify firms in four ordinal states of financial condition namely healthy, dividend reduction, debt default and bankrupt. The classification results of the NN-DSS model are compared with those of a Naïve model, a Multiple Discriminant Analysis (MDA) model, and an Ordinal Logistic Regression (OLGR) model. Four different evaluation criteria are used to compare the models, namely, simple classification accuracy, distance-weighted classification accuracy, expected cost of misclassification (ECM) and ranked probability score. Our study shows that NN-DSS models perform significantly better than the Naïve, MDA, and OLGR models on the ECM criteria, and provide better results than MDA and OLGR on other criteria, although not always significantly better. The effect of the proportion of firms of each state in the training set is also studied. A balanced training set leads to more uniform (less skewed) classification across all four states, whereas an unbalanced training set biases the classification results in favor of the state with the largest number of observations.     

Conceptual modeling of data base operations

A formalism adequate for the specification of behavioral properties of data bases is proposed. The formalism is a many-sorted first order predicate calculus, including a formalized notion of data base state. Both update and query requests are modeled through expressions by the use of predicates supplied in the language of the formal system, and are treated uniformly as a theorem proving process. The process consists of using the axioms defining the data base for either synthesizing a valid sequence of update operations (if such exists) or for answering the query.     

PREPARE: a tool for knowledge base verification

The knowledge base is the most important component in a knowledge-based system. Because a knowledge base is often built in an incremental, piecemeal fashion, potential errors may be inadvertently brought into it. One of the critical issues in developing reliable knowledge-based systems is how to verify the correctness of a knowledge base. The paper describes an automated tool called PREPARE for detecting potential errors in a knowledge base. PREPARE is based on modeling a knowledge base by using a predicate/transition net representation. Inconsistent, redundant, subsumed, circular, and incomplete rules in a knowledge base are then defined as patterns of the predicate/transition net model, and are detected through a syntactic pattern recognition method. The research results to date have indicated that: the methodology ran be adopted in knowledge-based systems where logic is used as knowledge representation formalism; the tool can be invoked at any stage of the system's development, even without a fully functioning inference engine; the predicate/transition net model of knowledge bases is easy to implement and provides a clear and understandable display of the knowledge to be used by the system     

Addition and removal of information for a knowledge base with incomplete information

A knowledge base containing incomplete information in the form of disjunctions and negative information shows difficulties regarding the update operators. In this paper simple and straightforward definitions are given for an ‘adding’ operator (‘+’) and a ‘removing’ operator (‘−’) using Hebrand models.