A case-based reasoning approach to cognitive map-driven tacitknowledge management

Knowledge is at the heart of knowledge management. In literature, a lot of studies have been suggested covering the role of knowledge in improving the performance of management. However, there are few studies about investigating knowledge itself in the arena of knowledge management. Knowledge circulating in an organization may be explicit or tacit. Until now, literature in knowledge management shows that it has mainly focused on explicit knowledge. On the other hand, tacit knowledge plays an important role in the success of knowledge management. It is relatively hard to formalize and reuse tacit knowledge. Therefore, research proposing the explication and reuse of tacit knowledge would contribute significantly to knowledge management research. In this sense, we propose using cognitive map (CM) as a main vehicle of formalizing tacit knowledge, and case-based reasoning as a tool for storing CM-driven tacit knowledge in the form of frame-typed cases, and retrieving appropriate tacit knowledge from the case base according to a new problem. Our proposed methodology was applied to a credit analysis problem in which decision-makers need tacit knowledge to assess whether a firm under consideration is healthy or not. Experiment results showed that our methodology for tacit knowledge management can provide decision makers with robust knowledge-based support.     

Improving the tutoring of software design using case-based reasoning

Judging by results, the methods undertaken to teach software development to large classes of students are flawed; too many students are failing to grasp any real understanding of programming and software design. To address this problem the University of Wales, Aberystwyth has developed VorteX, an interactive collaborative design tool that captures the design processes of novice students, provides a diagnosis system capable of interpreting the students’ work, and advises on their design process.This paper provides an overview of VorteX, its capabilities and use, and explains how the case-based system identifies redundancies in the storage of student designs and reduces data volume. The paper describes how equivalence maps merge similar classes to reduce the design structure possibilities, how snippets eliminate the replication of components and how abstract snippets represent the design intent of students in a minimalist form. Finally it concludes with comments on the student experience of the VorteX case-based reasoning assistant.     

A connectionist approach for similarity assessment in case-based reasoning systems

Case-Based Reasoning (CBR) systems support ill-structured decision making. In ill-structured decision environments, decision makers (DMs) differ in their problem solving approaches. As a result, CBR systems would be more useful if they were able to adapt to the idiosyncrasies of individual decision makers. Existing implementations of CBR systems have been mainly symbolic, and symbolic CBR systems are unable to adapt to the preferences of decision makers (i.e., they are static). Retrieval of appropriate previous cases is critical to the success of a CBR system. Widely used symbolic retrieval functions, such as nearest-neighbor matching, assume independence of attributes and require specification of their importance for matching. To ameliorate these deficiencies connectionist systems have been proposed. However, these systems are limited in their ability to adapt and grow. To overcome this limitation, we propose a distributed connectionist-symbolic architecture that adapts to the preferences of a decision maker and that, additionally, ameliorates the limitations of symbolic matching. The proposed architecture uses a supervised learning technique to acquire the matching knowledge. The architecture allows the growth of a case base without the involvement of a knowledge engineer. Empirical investigation of the proposed architecture in an ill-structured diagnostic decision environment demonstrated a superior retrieval performance when compared to the nearest-neighbor matching function.     

Multi-modal diagnosis combining case-based and model-based reasoning: a formal and experimental analysis

Integrating different reasoning modes in the construction of an intelligent system is one of the most interesting and challenging aspects of modern AI. Exploiting the complementarity and the synergy of different approaches is one of the main motivations that led several researchers to investigate the possibilities of building multi-modal reasoning systems, where different reasoning modalities and different knowledge representation formalisms are integrated and combined. Case-Based Reasoning (CBR) is often considered a fundamental modality in several multi-modal reasoning systems; CBR integration has been shown very useful and practical in several domains and tasks. The right way of devising a CBR integration is however very complex and a principled way of combining different modalities is needed to gain the maximum effectiveness and efficiency for a particular task. In this paper we present results (both theoretical and experimental) concerning architectures integrating CBR and Model-Based Reasoning (MBR) in the context of diagnostic problem solving. We first show that both the MBR and CBR approaches to diagnosis may suffer from computational intractability, and therefore a careful combination of the two approaches may be useful to reduce the computational cost in the average case. The most important contribution of the paper is the analysis of the different facets that may influence the entire performance of a multi-modal reasoning system, namely computational complexity, system competence in problem solving and the quality of the sets of produced solutions. We show that an opportunistic and flexible architecture able to estimate the right cooperation among modalities can exhibit a satisfactory behavior with respect to every performance aspect. An analysis of different ways of integrating CBR is performed both at the experimental and at the analytical level. On the analytical side, a cost model and a competence model able to analyze a multi-modal architecture through the analysis of its individual components are introduced and discussed. On the experimental side, a very detailed set of experiments has been carried out, showing that a flexible and opportunistic integration can provide significant advantages in the use of a multi-modal architecture.     

Integrating a semantic-based retrieval agent into case-based reasoning systems: A case study of an online bookstore

Natural language search engines should be developed to provide a friendly environment for business-to-consumer e-commerce that reduce the fatigue customers experience and help them decide what to buy. To support product information retrieval and reuse, this paper presents a novel framework for a case-based reasoning system that includes a collaborative filtering mechanism and a semantic-based case retrieval agent. Furthermore, the case retrieval agent integrates short-text semantic similarity (STSS) and recognizing textual entailment (RTE). The proposed approach was evaluated using competitive methods in the performance of STSS and RTE, and according to the results, the proposed approach outperforms most previously described approaches. Finally, the effectiveness of the proposed approach was investigated using a case study of an online bookstore, and according to the results of case study, the proposed approach outperforms a compared system using string similarity and an existing e-commerce system, Amazon.     

A case-based reasoning system development for statistical process control: Case representation and retrieval

Statistical process control (SPC) is a sub-area of statistical quality control. Considering the successful results of the SPC applications in various manufacturing and service industries, this field has attracted a large number of experts. Despite the development of knowledge in this field, it is hard to find a comprehensive perspective or model covering such a broad area and most studies related to SPC have focused only on a limited part of this knowledge area. According to many implemented cases in statistical process control, case-based reasoning (CBR) systems have been used in this study for developing of a knowledge-based system (KBS) for SPC to organize this knowledge area. Case representation and retrieval play an important role to implement a CBR system. Thus, a format for representing cases of SPC and the similarity measures for case retrieval are proposed in this paper.     

Design information retrieval: a thesauri-based approach for reuse of informal design information

Information is integral to the engineering design process, and gaining access to design knowledge is critical to effective design decision-making. This paper considers the indexing and retrieval of informal, unstructured information captured from electronic design logbooks. One of the key observations of informal design information is its evolutionary nature over time. While this characteristic makes informal information a rich source for reuse, it also makes it difficult to employ traditional information retrieval (IR) approaches. The work described in this paper is based on a framework developed specifically for the information handling requirements of designers. This manual method for indexing information is adapted to meet the evolutionary nature of design through the development of thesauri for design context. Several approaches to building thesauri are examined, including manual and automated methods. It is found that manual methods provide a high level of IR performance, but also have high overhead requirements. Machine methods, however, may provide a viable, low overhead alternative.

Collaborative case-based estimating and design

Information Technology (IT) solutions to problems in construction design need to consider the perspectives of all the participants in the process; only then can IT provide a platform for integration. The research described examines issues involved in the integration of construction disciplines by using Case-Based Reasoning (CBR). It describes a hierarchical case memory structure and a context-based indexing method for retrieval and reuse of previous designs and their costs. Estimating and design cases selected for reuse are adapted with the use of sub-cases and domain specific adaptation rules. A prototype system, NIRMANI, was successfully implemented to support collaborative design.     

A hybrid case adaptation approach for case-based reasoning

Case-Based Reasoning is a methodology for problem solving based on past experiences. This methodology tries to solve a new problem by retrieving and adapting previously known solutions of similar problems. However, retrieved solutions, in general, require adaptations in order to be applied to new contexts. One of the major challenges in Case-Based Reasoning is the development of an efficient methodology for case adaptation. The most widely used form of adaptation employs hand coded adaptation rules, which demands a significant knowledge acquisition and engineering effort. An alternative to overcome the difficulties associated with the acquisition of knowledge for case adaptation has been the use of hybrid approaches and automatic learning algorithms for the acquisition of the knowledge used for the adaptation. We investigate the use of hybrid approaches for case adaptation employing Machine Learning algorithms. The approaches investigated how to automatically learn adaptation knowledge from a case base and apply it to adapt retrieved solutions. In order to verify the potential of the proposed approaches, they are experimentally compared with individual Machine Learning techniques. The results obtained indicate the potential of these approaches as an efficient approach for acquiring case adaptation knowledge. They show that the combination of Instance-Based Learning and Inductive Learning paradigms and the use of a data set of adaptation patterns yield adaptations of the retrieved solutions with high predictive accuracy.     

Zero anaphora resolution by case-based reasoning and pattern conceptualization

Effective anaphora resolution is helpful to many applications of natural language processing such as machine translation, summarization and question answering. In this paper, a novel resolution approach is proposed to tackle zero anaphora, which is the most frequent type of anaphora shown in Chinese texts. Unlike most of the previous approaches relying on hand-coded rules, our resolution is mainly constructed by employing case-based reasoning and pattern conceptualization. Moreover, the resolution is incorporated with the mechanisms to identify cataphora and non-antecedent instances so as to enhance the resolution performance. Compared to a general rule-based approach, the proposed approach indeed improves the resolution performance by achieves 78% recall and 79% precision on solving 1051 zero anaphora instances in 382 narrative texts.     

Using case-based reasoning to establish a continuing care information system of discharge planning

As family structure changes, population is aging and disease styles tend to be chronic, long-term care in Taiwan becomes problematic, needs to be addressed, and requires immediate solution. Presently, many medical care institutions in the country have assisted patients in discharge planning; however, the lack of a standard evaluation procedure in the process of discharge planning is disturbing for each hospital. Without it, there might be huge differences in the evaluation results. Moreover, the lack of support and the uncertainty of case eligibility standard in these institutions further affect the performance of continuing care services.

This research adopted Case-Based Reasoning to establish a continuing care information system of discharge planning. With previously evaluated information of past cases, the similarity index is compared among new cases. In coordinate with Analytic Hierarchy Process, index weight is calculated to reason an old case that is most closely related to the condition of the new case. This information system can assist discharge-planning staff in accurately formulating a plan of action based on previous case-assessment experience and in obtaining valuable information that helps make decision. Through the implementation of the system, accumulation on knowledge and experience of continuing care models will help staff evaluate process of discharge planning to achieve a reasonable, standardized, and simplified procedure as a whole.

This research will transform the evaluating experience of discharge-planning professionals into an assessment method with the application of computer reasoning to make the evaluation process of discharge planning convenient as well as to save more time for discharge-planning professionals to further understand the actual conditions of each case. On the other hand, this information system will provide discharge-planning staff with a set of recommendations as references for making individual discharge plan. It is expected through this research that each hospital be provided with a blue print of improvement in case evaluation process and management.     

A case-based method for service-oriented value chain and sustainable network design

PurposeThe purpose of this research is to present a case-based analytic method for a service-oriented value chain and a sustainable network design considering customer, environmental and social values. Enterprises can enhance competitive advantage by providing more values to all stakeholders in the network.Design/methodology/approachOur model employs a stylized database to identify successful cases of value chain application under similar company marketing conditions, illustrating potential value chains and sustainable networks as references. This work first identifies economic benefits, environmental friendliness and social contribution values based on prior studies. Next, a search engine which is developed based on the rough set theory will search and map similarities to find similar or parallel cases in the database. Finally, a visualized network mapping will be automatically generated to possible value chains.FindingsThis study applies a case-based methodology to assist enterprises in developing a service-oriented value chain design. For decision makers, this can reduce survey time and inspire innovative works based on previous successful experience. Besides, successful ideas from prior cases can be reused. In addition to customer values, this methodology incorporates environment and social values that may encourage a company to build their value chain in a more comprehensive and sustainable manner.Research implicationsThis is a pilot study which attempts to utilize computer-aided methodology to assist in service or value-related design. The pertinent existing solutions can be filtered from an array of cases to engage the advantages from both product-oriented and service-oriented companies. Finally, the visualized display of value network is formed to illustrate the results.Practical implicationsA customized service-oriented value chains which incorporates environment and social values can be designed according to different conditions. Also, this system engages the advantages from both product-oriented and service-oriented companies to build a more comprehensive value network. Apart from this, the system can be utilized as a benchmarking tool, and it could remind the decision makers to consider potential value from a more multifaceted perspective.Originality/valueThis is the first paper that applied a computer-aided method to design service-oriented value chains. This work also can serve as a decision support and benchmarking system because decision makers can develop different value networks according to various emphasized values. Finally, the visualized display of value network can improve the communication among stakeholders.     

CaBMA: a case-based reasoning system for capturing,refining, and reusing project plans

In this paper, we present CaBMA, a prototype of a knowledge-based system designed to assist with project planning tasks using case-based reasoning. CaBMA introduces a novel approach to project planning in that, for the first time, a knowledge layer is added on top of traditional project management software. Project management software provides editing and bookkeeping capabilities. CaBMA enhances these capabilities by automatically capturing project plans in the form of cases, refining these cases over time to avoid potential inconsistency between them, reusing these cases to generate plans for new projects, and indicating possible repairs for project plans when they derive away from existing knowledge. We will give an overview of the system, provide a detailed explanation on each component, and present an empirical study based on synthetic data.     

Design of a RFID case-based resource management system for warehouse operations

In the past, the selection of resources to execute various warehouse operation services was done solely by experts. In this paper, a RFID-based Resource Management System (RFID-RMS) is designed to help users to select the most suitable resource usage packages for handling warehouse operation orders by retrieving and analysing useful knowledge from a case-based data warehouse for solutions in both time saving and cost effective manner. In addition, a pure integral-linear programming model using a branch and bound algorithm to define the optimum travel distance of forklifts is also developed and embedded in the proposed system. The proposed system, which is suitable for usage in a warehouse operation environment, enhances the effectiveness in formulating resource usage package and managing resource operation by integrating the Radio Frequency Identification (RFID), case-based reasoning (CBR) technologies and the programming model for forklift route optimization. Through applying RFID-RMS in the GENCO Distribution System, a multinational logistics company, the utilization of warehouse resources is expected to be maximized while work efficiency will be greatly enhanced.     

HDI: Integrating Health Data and Tools

Health systems globally are looking to make better use of the data they capture in order to improve their services, both for service provision and clinical outcome. One way of doing this is to integrate existing data sources. However, major technical and legal questions exist concerning data integration, data quality, data security and privacy in health data usage. In this paper, we present the HDI^TM tool, that is currently under development at the e-Health Research Centre. Firstly we describe the HDI^TM architecture and its data integration capabilities. We then consider two of its core capabilities (1) privacy-preserving similarity linkage and (2) on-line analytical techniques and report generating. Finally, we discuss how HDI functionality can be used to provide the capabilities of a knowledge based medical system. The e-Health Research Centre is currently working with the Queensland Health department to deploy the software in practice.     

Binary classification in unstructured space with hypergraph case-based reasoning

Binary classification is one of the most common problem in machine learning. It consists in predicting whether a given element belongs to a particular class. In this paper, a new algorithm for binary classification is proposed using a hypergraph representation. The method is agnostic to data representation, can work with multiple data sources or in non-metric spaces, and accommodates with missing values. As a result, it drastically reduces the need for data preprocessing or feature engineering. Each element to be classified is partitioned according to its interactions with the training set. For each class, a seminorm over the training set partition is learnt to represent the distribution of evidence supporting this class.Empirical validation demonstrates its high potential on a wide range of well-known datasets and the results are compared to the state-of-the-art. The time complexity is given and empirically validated. Its robustness with regard to hyperparameter sensitivity is studied and compared to standard classification methods. Finally, the limitation of the model space is discussed, and some potential solutions proposed.     

ICARUS: design and deployment of a case-based reasoning system for locomotive diagnostics

Locomotives, like many complex modern machines, are equipped with the capability to generate on-board fault messages indicating the presence of anomalous conditions. Such messages tend to be generated in large quantities, and are difficult and time consuming to interpret manually. This paper presents the design and development of a case-based reasoning system for diagnosing locomotive faults using such fault messages as input. The process of using historical repair data and expert input for case generation and validation is described. An algorithm for case matching is presented, along with some results on pilot data.     

An ontology in OWL for legal case-based reasoning

The paper gives ontologies in the Web Ontology Language (OWL) for Legal Case-based Reasoning (LCBR) systems, giving explicit, formal, and general specifications of a conceptualisation LCBR. Ontologies for different systems allows comparison and contrast between them. OWL ontologies are standardised, machine-readable formats that support automated processing with Semantic Web applications. Intermediate concepts, concepts between base-level concepts and higher level concepts, are central in LCBR. The main issues and their relevance to ontological reasoning and to LCBR are discussed. Two LCBR systems (AS-CATO, which is based on CATO, and IBP) are analysed in terms of basic and intermediate concepts. Central components of the OWL ontologies for these systems are presented, pointing out differences and similarities. The main novelty of the paper is the ontological analysis and representation in OWL of LCBR systems. The paper also emphasises the important issues concerning the representation and reasoning of intermediate concepts.