Knowledge Representation Scheme Research Articles

Artificial-intelligence-based hospital-acquired infection control.

Nosocomial or hospital-acquired infections (NIs) are a frequent complication in hospitalized patients. The growing availability of computerized patient records in hospitals permits automated identification and extended monitoring for signs of NIs. A fuzzy- and knowledge-based system to identify and monitor NIs at intensive care units (ICUs) according to the European Surveillance System HELICS (NI definitions derived from the Centers of Disease Control and Prevention (CDC) criteria) was developed and put into operation at the Vienna General Hospital. This system, named Moni, for monitoring of nosocomial infections contains medical knowledge packages (MKPs) to identify and monitor various infections of the bloodstream, pneumonia, urinary tract infections, and central venous catheter-associated infections. The MKPs consist of medical logic modules (MLMs) in Arden syntax, a medical knowledge representation scheme, whose definition is part of the HL7 standards. These MLM packages together with the Arden software are well suited to be incorporated in medical information systems such as hospital information or intensive-care patient data management systems, or in web-based applications. In terms of method, Moni contains an extended data-to-symbol conversion with several layers of abstraction, until the top level defining NIs according to HELICS is reached. All included medical concepts such as "normal", "increased", "decreased", or similar ones are formally modeled by fuzzy sets, and fuzzy logic is used to process the interpretations of the clinically observed and measured patient data through an inference network. The currently implemented cockpit surveillance connects 96 ICU beds with Moni and offers the hospital's infection control department a hitherto unparalleled NI infection survey.

Case-Based Reasoning for Construction Hazard Identification: Case Representation and Retrieval

This paper proposes a case-based reasoning (CBR) approach to construction hazard identification that facilitates systematic feedback of past knowledge in the form of incident cases and hazard identification. This paper focuses on two of the key components of the CBR approach: (1) a detailed knowledge representation scheme, developed based on the modified loss causation model, to codify incident cases and past hazard identification and (2) an intelligent retrieval mechanism that can automatically retrieve relevant past cases. The detailed knowledge representation scheme presented herein is designed to model both incident cases and hazard identification so that both types of knowledge repository can be retrieved simultaneously and adapted for use. The scheme also includes a linguistic structure used to facilitate indexing of cases. The retrieval mechanism is based on the concept of similarity scoring. In this paper, a novel scoring technique based on semantic networks is presented. A case study is presented to demonstrate and validate the proposed approach.

Design and implementation of an agent-based collaborative product design system

The evolution of computer science and technology has brought new opportunities for multidisciplinary designers and engineers to collaborate with each other in a concurrent and coordinated manner. The development of computational agents with unified data structures and software protocols contributes to the establishment of a new way of working in collaborative design, which is increasingly becoming an international practice. In this paper, based on the analysis of the dynamic nature of collaborative design process, a new framework for collaborative design is described. This framework adopts an agent-based approach and relocates designers, managers, systems, and the supporting agents in a unified knowledge representation scheme for product design. In order to model the constantly evolving design process and the rationales resulted from design collaboration, a Collaborative Product Data Model (CPDM) and a constraint-based Collaborative Design Process Model (CDPM) are proposed to facilitate the management and coordination of the collaborative design process as well as design knowledge management. A prototype system of the proposed framework is implemented and its feasibility is evaluated using a real design scenario whose objective is designing a set of dining table and chairs.

INFERENCE PROCEDURES FOR FUZZY KNOWLEDGE REPRESENTATION SCHEME

This article presents a formal model of the knowledge representation scheme based on the fuzzy Petri net (FPN) theory. The model is represented as a 13-tuple consisting of the components of the FPN, two functions that give semantic meanings to the scheme and a set of contradictions. For the scheme, called the knowledge representation scheme based on the fuzzy Petri nets theory (KRFPN) the fuzzy inheritance and fuzzy recognition-inference procedures based on the dynamical properties of the FPN, are described in detail. The upper-time complexity of both the proposed inference algorithms is O(nm), where n is the number of places (concepts) and m is the number of transitions (relations) in the scheme. Illustrative examples of the fuzzy inheritance and the fuzzy recognition algorithms for the knowledge base, designed by the KRFPN, are given.

Automatic generation of explanation for expert systems implemented with different knowledge representations

Humans seem to have a natural instinct for wanting to understand and make sense of their environment and things in it. In expert systems (ES), explanation can be used to clarify the reasoning process to users such that they can gain a better understanding of how the system functions. With the help of good explanation facilities, a user can know why an ES is asking a particular question, how the expert system will act if given a certain input, and how the ES reaches a particular conclusion. This is especially important when an ES application is used as a high level advisor to professionals who must retain responsibility for the decisions which are made. However, most ES explanation components require acquiring additional knowledge for explanation, thus increasing the effort of implementing an ES with explanation capabilities. The primary goal of this work is to present a methodology for automatically generating explanations during and at the end of the reasoning process. The developed explanation components can deal with different knowledge representation schemes that are used by problem solving methods namely, the and confirm hypotheses that is based on the CommonKADS methodology[1], and the routine design generic task[2]. As a proof of concept the explanation components were developed and integrated into the agricultural expert system generic tool (AESGT)[3]. The developed explanation components can be easily reused with expert systems developed by the tool to automatically generate explanation for the reasoning process.

A spatial knowledge sharing platform. Using the visualization approach

Applications of information technologies have significantly improved efficiency of electronic document processing and management. The speed and amount of knowledge accumulation in cyberspace have exceeded what common users can perceive. In order to enhance the efficiency and accuracy for knowledge receivers to acquire domain knowledge, an appropriate knowledge representation scheme is required. Most of the traditional schemes for explicit knowledge representation are text-oriented and much time is required for knowledge receivers to recognize the contents, especially for the spatial knowledge (e.g. the geographical or assembly information). This research develops a knowledge visualization methodology that computer systems can automatically convert the textual knowledge into visualized display. In addition to the proposed methodology, a Web-based prototype system is developed to support spatial knowledge sharing. In the prototype system, the free-form documents with spatial knowledge can be converted into formatted ones and the corresponding visualized display can be established based on the formatted contents. Feasibility and effectiveness of the proposed model are evaluated via a demonstration case. The goal of this research is to provide a knowledge visualization approach that can be widely applied in enterprise e-training to enhance the effectiveness of enterprise knowledge reuse.

Evaluation of Knowledge Representation Schemes as a Prerequisite toward Development of a Knowledge-Based System

In developing a knowledge-based system, it is crucial to use an appropriate knowledge representation scheme (KRS) for efficient working of the developed system. However, it is difficult to select an appropriate KRS due to lack of a generalized method of comparison among the variety of schemes available. The objective of this paper is to discuss a generalized method for comparative evaluation of KRS, based on criteria of expressiveness and performance. The discussed method has been implemented for evaluation of four widely used KRSs: rule-based model, object-based model, relational model, and hybrid model. Evaluation is being carried out using a knowledge base for zonation of landslide hazard. Based on the criteria of expressiveness and performance, it has been found that hybrid-based KRS is best for representation of the domain knowledge available in the Indian Standard Code 14496-1998 (Part-II).

Knowledge Reuse Enhancement with Motional Visual Representation

The growing complexity of information and documents has made it difficult for knowledge receivers to understand digital contents, therefore, multiple knowledge representation schemes are required for enterprise knowledge services. Traditional schemes for explicit knowledge representation within enterprise and academic circles are primarily text-oriented and thus, a great deal of time and effort are required for knowledge receivers to understand the contents, especially for motion knowledge. In order to enhance knowledge reuse with motion knowledge extraction, representation, and visualization, this research focuses on the development of a motion knowledge representation and visualization (MKRV) model for Chinese documents with three modules, namely the automatic thesaurus definition (ATD) module, the target sentence extraction and formatting (TSEF) module, and the motion knowledge visualization (MKV) module. Moreover, based on the proposed model, a Motion Knowledge Representation and Management System (MKRMS) is established. A real world case of computer assembly is also applied in order to verify the feasibility of the proposed model. The verification results show that the system could achieve a high performance level with a small amount of training data.

Cumulative learning techniques in production rules with fuzzy hierarchy (PRFH) system

Cumulative learning, a promising route for automated knowledge acquisition and adaptation, involves using the results of prior learning to facilitate further learning. Achieving this objective would largely depend upon an enriched knowledge representation scheme. One of such efficient representations is Production Rules with Fuzzy Hierarchy (PRFHs) system. A PRFH, a standard production rule augmented with generality and specificity information, is of the form: where P is the set of preconditions = (P pub) k ∪ (P spl) k ∪ (P pvt) k and the specificity element Dki (di ) means that Dki is a specific class of Dk with degree of subsumption di. In this paper, a set of related PRFHs is called a cluster and is represented by a PRFH-tree. The proposed scheme incrementally incorporates new knowledge into set of clusters obtained from previous episodes and also maintains summary of clusters to be used in the future episodes. Using the Cumulative_growth algorithm, a new rule is added to the system, the Restructure_cluster algorithm restructures a cluster so as to minimize redundancy, and Merging_clusters algorithm enables merging of two related PRFHs clusters. The proposed system would be particularly useful in mining data streams and dynamic restructuring of knowledge bases.

Clinical Decision Support Systems: A Review on Knowledge Representation and Inference Under Uncertainties

This paper provides a literature review in clinical decision support systems (CDSSs) with a focus on the way knowledge bases are constructed, and how inference mechanisms and group decision making methods are used in CDSSs. Particular attention is paid to the uncertainty handling capability of the commonly used knowledge representation and inference schemes. The definition of what constitute good CDSSs and how they can be evaluated and validated are also considered. Some future research directions for handling uncertainties in CDSSs are proposed.

Efficient representation of the attacker’s knowledge in cryptographic protocols analysis

Abstract This paper addresses the problem of representing the intruder’s knowledge in the formal verification of cryptographic protocols, whose main challenges are to represent the intruder’s knowledge efficiently and without artificial limitations on the structure and size of messages. The new knowledge representation strategy proposed in this paper achieves both goals and leads to practical implementation because it is incrementally computable and is easily amenable to work with various term representation languages. In addition, it handles associative and commutative term composition operators, thus going beyond the free term algebra framework. An extensive computational complexity analysis of the proposed representation strategy is included in the paper.

Knowledge-Based Expert System Development and Validation with Petri Nets

Expert systems (ESs) are complex information systems that are expensive to build and difficult to validate. Numerous knowledge representation strategies such as rules, semantic networks, frames, objects and logical expressions are developed to provide high-level abstraction of a system. Rules are the most commonly used form of knowledge representation and they are derived from popular techniques such as decision trees and decision tables. Despite their huge popularity, decision trees and decision tables are static and cannot model the dynamic requirements of a system. In this study, we propose Petri Nets (PNs) for dynamic system representation and rule derivation. PNs with their graphical and precise nature and their firm mathematical foundation are especially useful for building ESs that exhibit a variety of situations, including: sequential execution, conflict, concurrency, synchronisation, merging, confusion, or prioritisation. We demonstrate the application of our methodology in the design and development of a medical diagnostic expert system.

Formal conceptualisation as a basis for a more procedural knowledge management

Knowledge management at an organisational level can only be brought into practice if a corporate memory is defined. Unfortunately, at this moment there is no complete and procedural specification on how to build it. This paper presents a complete and generic knowledge representation scheme that makes it possible to conceptualise/represent the knowledge of any domain in a systematic way, guiding the definition of a corporate memory and allowing us to reach a more procedural level in knowledge management discipline. The conclusions of our study, which follows the generic and formal definition of any conceptualisation, are illustrated by a real project.

A Knowledge Representation Scheme Using Ordinary Words and the Inference Mechanism on Order-sorted Logic

We propose a knowledge representation scheme (called WPL) and an inference method for the scheme. In WPL, both simple sentence and complex sentence are represented in one atomic formula. Subordinate clauses in a complex sentence are embedded into the formula forming the main clause. WPL is an extended order-sorted logic that can deal with structured sort symbols consisting of multiple ordinary words like noun phrases. Each word in a sort symbol can represent a general concept or a particular object. If it is the latter, each word stands for a variable or constant having itself as a sort symbol. It may also be a proper noun or variable itself. The inference processes for WPL is executed based on the resolution principle, semantically interpreting the sort symbols word by word. We extend the inference rules proposed by Beierle et al. in order to deal with complex sort symbols. This paper also describes an application scheme of the proposed inference rules and an algorithm for judging the subsort relation between complex sort symbols.

Evolution of concept networks and implications for knowledge representation

PurposeThe purpose of this paper is to present descriptive characteristics of the historical development of concept networks. The linguistic principles, mechanisms and motivations behind the evolution of concept networks are discussed. Implications emanating from the idea of the historical development of concept networks are discussed in relation to knowledge representation and organization schemes.Design/methodology/approachNatural language data including both speech and text are analyzed by examining discourse contexts in which a linguistic element such as a polysemy or homonym occurs. Linguistic literature on the historical development of concept networks is reviewed and analyzed.FindingsSemantic sense relations in concept networks can be captured in a systematic and regular manner. The mechanism and impetus behind the process of concept network development suggest that semantic senses in concept networks are closely intertwined with pragmatic contexts and discourse structure. The interrelation and permeability of the semantic senses of concept networks are captured on a continuum scale based on three linguistic parameters: concrete shared semantic sense; discourse and text structure; and contextualized pragmatic information.Research limitations/implicationsResearch findings signify the critical need for linking discourse structure and contextualized pragmatic information to knowledge representation and organization schemes.Originality/valueThe idea of linguistic characteristics, principles, motivation and mechanisms underlying the evolution of concept networks provides theoretical ground for developing a model for integrating knowledge representation and organization schemes with discourse structure and contextualized pragmatic information.

Design process of interactive information systems

In this work we study and experiment the integration of usage data in the design process of an interactive information system IIS, specially in the scope of an IIS dedicated to decision-aid. Data have been generated by using methods of activity analysis. We make the assumption that for integrating usage data successfully, they must be represented in a formalism close to the development language. First a generic scheme of information flow in a IIS is defined. Each components of this scheme is then characterized. Data representing user's needs are then collected, analyzed and represented in an adapted knowledge representation scheme. We build a formal model of the knowledge, which has been collected, and then define a users needs matching function for linking the usage activity to the resources the IIS. For each identified problem corresponding to a specific users needs, the function brings out the building of a solution. The approach has been tested by a functional specification design tool developed for the specification of a traveller information system for aiding users of a public transportation network in the choice of the best transportation mode according to the network functioning conditions. It is built in three steps: 1 the construction of a method for collecting users needs data in different use cases; 2 the object-oriented modelling of corresponding knowledge and its transcription into the UML formalism; 3 the validation of the approach by implementing a tool for functional specification aid.

Personalization in an interactive learning environment through a virtual character

Traditional hypermedia applications present the same content and provide identical navigational support to all users. Adaptive Hypermedia Systems (AHS) make it possible to construct personalized presentations to each user, according to preferences and needs identified. We present in this paper an alternative approach to educational AHS where a virtual character personalizes the interaction with the user through the use of a particular recommender system. The character has natural language communication abilities; it can learn students’ profiles and use this knowledge to recommend appropriate contents and activities. Through its interaction with the user, the character is able to collect and organize information about students in order to identify appropriate suggestions of contents. The recommender system employs a knowledge representation scheme that is easy to understand and to modify, enabling teachers/tutors to explore the types of recommendations being made and to appreciate why they are made. An experiment with computer science students was carried out in order to validate the approach proposed. The results of the experiment showed that the presentation of personalized links through a virtual character had a positive impact in the users’ perception of the system as a learning tool. The combination of the virtual character with a recommender system proved to be a good alternative for the delivery of personalized contents without making constant changes in the main user interface. This approach provides mechanisms to guide users through paths of study followed by students with similar profiles, without violating the human–computer interaction principle of perceived stability.

A frame knowledge system for managing financial decision knowledge

Managing decision knowledge or expertise from domain experts is one of the most exciting challenges in today's knowledge management field. The nature of decision knowledge in determining a firm's financial health is context-dependent, intangible, and tacit in nature. Knowledge-based systems (KBS) have been recognized as a successful paradigm for managing financial decision knowledge attributed to possessing capabilities of reasoning and enhancing the consistency of decision-making. However, most present KBS adopt rules as the knowledge representation scheme, which cannot express the expert's knowledge construct systematically when dealing with more numerous and disordered knowledge connotations. In addition, the standalone nature of the systems hinders them from deploying onto heterogeneous platforms and cannot accommodate to the emerging Web-enabled environment. To reduce these flaws, this study proposes a frame knowledge system in which the structural and procedural decision knowledge is encapsulated so that unnecessary interference can be avoided. A protocol analysis, before encapsulation, is conducted to elicit the tacit and unstructured knowledge from a senior CPA we cooperated with. The deployment and Web enabling issue is tackled by using Jess and Java interoperable computing. With these combined, it is possible to prompt the understandability, accessibility, and reusability of KBS. The effectiveness of the proposed system is validated in supporting the expert's decision-making by conducting an empirical experimentation on 537 companies listed in the Taiwan Stock Exchange Corporation.

Optimization Models for Training Belief-Rule-Based Systems

A belief rule-base inference methodology using the evidential reasoning approach (RIMER) has been developed recently, where a new belief rule representation scheme is proposed to extend traditional IF-THEN rules. The belief rule expression matrix in RIMER provides a compact framework for representing expert knowledge. However, it is difficult to accurately determine the parameters of a belief rule base (BRB) entirely subjectively, particularly, for a large-scale BRB with hundreds or even thousands of rules. In addition, a change in rule weight or attribute weight may lead to changes in the performance of a BRB. As such, there is a need to develop a supporting mechanism that can be used to train, in a locally optimal way, a BRB that is initially built using expert knowledge. In this paper, several new optimization models for locally training a BRB are developed. The new models are either single- or multiple-objective nonlinear optimization problems. The main feature of these new models is that only partial input and output information is required, which can be either incomplete or vague, either numerical or judgmental, or mixed. The models can be used to fine tune a BRB whose internal structure is initially decided by experts' domain-specific knowledge or common sense judgments. As such, a wide range of knowledge representation schemes can be handled, thereby facilitating the construction of various types of BRB systems. Conclusions drawn from such a trained BRB with partially built-in expert knowledge can simulate real situations in a meaningful, consistent, and locally optimal way. A numerical study for a hierarchical rule base is examined to demonstrate how the new models can be implemented as well as their potential applications.

Structure Identification of a Neuro-Fuzzy Model Can Reduce Inconsistency of Its Rulebase

It has been shown that the structure identification of a neuro-fuzzy model improves their accuracy performances in a various modeling problems. In this paper, we claim that the structure identification of a neuro-fuzzy model can also reduce the degree of inconsistency of its fuzzy rulebase. Thus, the resulting neuro-fuzzy model serves as more like a structured knowledge representation scheme. For this, we briefly review a structure identification method of a neuro-fuzzy model and propose a systematic method to measure inconsistency of a fuzzy rulebase. The proposed method is applied to problems of fuzzy system reproduction and nonlinear system modeling in order to validate our claim.