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This paper describes the background surrounding the need for risk analysis of commercial business within the domain of bank lending and of the development of an expert system for that task. Previous attempts at constructing expert systems in this area have either proved unsuccessful, software difficulties often being cited as the cause; or have stopped short of encapsulating all the relevant expertise. This paper considers the relevance of knowledge engineering to successful expert system construction. It reports on the development and structure of COMPASS, the Bank of Scotland's commercial lending adviser expert system which, by appropriate application of knowledge engineering, has succeeded in capturing and modelling the inherent risk of the Bank of Scotland's commercial lending process. The stages of its development are outlined; the knowledge elicitation process is described; knowledge articulation is examined from the perspective of the expert; the architecture of the system is explained; and the consultation procedure is described. In addition, reference is made to major attempts elsewhere to produce lending adviser expert systems; and the advantages; by-products, and long-term benefits of COMPASS are summarized.

This paper outlines the structure and the results of a research project titled Development of a KBS for Personal Financial Planning Guided by Pragmatic KADS. The aim of the project was the development of a prototype knowledge-based system for personal financial planning. The prototype system developed assists a potential private investor in selecting a professionally balanced mixture of investment products that meets all the investor's requirements with regard to the investment. As part of the specification, it was decided that the prototype is to develop a portfolio for a single client investing a one-off lump sum, assuming that the client's pension, insurance, and mortgage have been arranged and that no other prior investments need to be taken into account. The development of the prototype was guided by the Pragmatic KADS methodology, a streamlined version of KADS suitable to the development of small and medium-sized systems. The paper therefore describes how Pragmatic KADS helped to guide and structure the development of the prototype. The development process showed that the models that are developed as part of KADS knowledge analysis, which view knowledge from different viewpoints, contribute both to breaking down the complexity of the problem and to ensuring a comprehensive knowledge analysis. It also showed that the top-down approach of KADS has the inherent danger of neglecting some of the low-level knowledge that may have an impact on the high-level problem-solving strategies. This paper presents a way in which this problem can be resolved by using a modified version of the spiral approach to expert system development, guided by KADS.

In this paper, we report the results of the first phase of a three-part study of the emerging role of Artificial Intelligence (AI) as an academic discipline. The stratification among journals that are formal communication vehicles for AI research is described and analyzed. Based on a survey of business faculty from AACSB-accredited schools around the country, 30 leading AI journals are ranked and evaluated, in terms of their academic quality and reputation. The paper also reports on the progress toward the development of a cumulative tradition for AI and its foundation. The second phase of this research focuses on the knowledge utilization of AI researchers and practitioners. In the third phase, the findings from the above two studies will be analyzed and used as a basis to develop a taxonomy of research for Artificial Intelligence.

A virtual enterprise (VE) is an organization intended to cope with the rapidly changing manufacturing environment. Organization building is important in virtual domains because it has largely been affecting the success of VEs. However, the process of forming a VE is based on self-determination by the participants. This paper adopts a bargaining model under a scenario of incomplete information to formalize the formation process, considers the characteristics of the VE formation process, presents the pricing strategies for the corresponding bargaining, and verifies the correctness and validity of the pricing strategies using computer simulation. This paper breaks through the relative research that compares the formation process with partner selection from the core enterprise’s perspective and also provides the basis for the intelligent information platform of VE, whose key part is pricing software.

In knowledge acquisition, knowledge engineers typically lack the background needed to pose optimal questions, to capture the right knowledge, and to structure the knowledge acquired about a particular application area. A large amount of time and effort is consumed in forming a detailed coherent model of the application domain. Even after the model is formed, it is typically informal both in their mind and in their documentation. This makes further acquisition of problem solving knowledge and later system design difficult. This paper discusses the situation and presents a model-based approach to problem acquisition in scheduling domains. In order for this approach to work, an adequate model of scheduling problems is a prerequisite. In the paper, a domain-independent model and a problem-formulation framework are described. Guided by them, knowledge engineers will know what to ask in their interview with domain experts and how to organise formally the domain knowledge they acquire.

This paper examines the introduction of knowledge acquisition techniques to facilitate managerial and administrative problem solving. The characteristics of such problem situations do not lend themselves to normative approaches such as simply introducing expert systems. However appropriately performed knowledge acquisition can still be of benefit by providing a better understanding of problem situations and it can provide a suitable basis for the process of negotiation and compromise needed to resolve organisational problems. This emerging role for knowledge acquisition differs from that of systems analysis, for example, in that it focuses on improving the understanding of the location, ownership and impact of available organisational knowledge rather than the data flows and tasks performed within the organisation. The paper provides guidelines for performing knowledge acquisition for organisational problem solving.

This paper investigates some of the key research issues which arise when expert system methodology is used as an approach to developing models of domains as complex as auditing. It also discusses the types of research which can be conducted using expert systems methodology in auditing and argues that, although useful, the distinction between research and development may have been overworked. Central areas of concern and inquiry which are addressed in this paper are knowledge acquisition, knowledge representation, and of particular concern to the author and his co-researchers at the present time, validation and verification.

This paper describes the LEXIS project, a research endeavor in building a legal expert system on Italian family law. An expert system shell, Xi Plus, was used to help construct this expert system. This paper will describe some of the development steps of this project and also cite some research issues in building legal expert systems.

This paper presents experiences from development, use, and maintenance of a large rule-based expert system known as Matias. The application contains knowledge about farming loans in Finland. Using this large application as a reference point, we will discuss project management, knowledge acquisition, knowledge base design, and maintenance of large knowledge-based systems (KBS). Based on the experiences gained during the development of Matias we would like to promote better planning in KBS projects, use of structured knowledge base design principles, and use of graphics in representing knowledge to the experts. One graphical representation technique, dependency graphs, is presented in this paper. Techniques presented in this paper are applicable to most application areas but are of most importance to developers of large applications. Building a large application sets high requirements for system design, knowledge transparency, and knowledge management facilities.

Many expert systems operate in dynamic environments where various pertinent environmental variables and conditions vary with the passage of time. These environmental variables and conditions may affect both the set of conditions applied to input variables of expert systems and the set of recommendations provided by expert systems. For this reason, expert systems developed according to dynamic structure will generate timely recommendations. To incorporate dynamic characteristics into the structure of expert systems, it is necessary to develop expert systems as adaptive systems. This paper intends to integrate concepts of learning and adaptiveness into expert system technology. Expert systems used to assist loan officers in improving the decision-making process of commercial loans are typical examples of expert systems that operate in dynamic environments. This paper illustrates that the quality of information provided to loan officers by expert systems may be improved when expert systems are designed as adaptive expert systems.

One particular area within expert systems where applications are increasing is configuration management. The need for accurately configuring hardware is an important requirement for many manufacturing organizations. This paper surveys some of the work that is being done in developing expert configuration systems. The paper also presents some lessons learned

In a recent paper in this journal, Wu and Lee (2007) proposed an effective method that combining fuzzy logic and DEMATEL approach to segment required competencies for better promoting the competency development of global managers. Then they applied their method to an empirical study to show the application of it. When we investigated their results, we found that the results related to applying their method to an empirical study, are not consistent with definitions given in Wu and Lee (2007). In this note, we express a flaw in aforementioned paper and correct it.

Sustainable tourism is an emerging trend around the world. Eco-friendly (green) hotels are environmentally friendly properties that are becoming more popular among green travellers. Electronic Word-of-Mouth (e-WOM) is a method of communicating with customers to share their experiences and is a powerful marketing tool for hotel marketing. This paper investigates the role of online reviews of eco-friendly hotels for preference learning using multi-criteria decision-making and machine learning techniques. We develop a new method using multi-criteria decision making, supervised and unsupervised learning techniques. The Expectation-Maximization (EM) algorithm is used as an unsupervised learning technique to cluster travellers’ online reviews. We use the Higher-Order Singular-Value Decomposition technique along with a similarity measure to find the most similar customers based on their preference. To predict travellers’ preference for eco-friendly hotels, we employ a neuro-fuzzy system, the Adaptive Neuro-Fuzzy Inference System, as a supervised learning technique. To select the most important criteria, we use the entropy-weight approach in each segment. Several experiments were performed on the collected data from the Czech Republic's eco-friendly hotels on the TripAdvisor platform. The results demonstrated that the hybrid approach is effective for customers’ segmentation, and preference learning and prediction in eco-friendly hotels.

With the changes of lifestyle and environment of people, the incidence rate of lung cancer has increased year by year, and lung cancer has become one of the most malignant tumors that threaten the health of people. Within this context, choosing appropriate anti-lung cancer drugs is of great significance for the treatment of lung cancer patients. To improve the accuracy of anti-lung cancer drug selection, it is necessary to invite many experts to participate in the evaluation process, and such a selection process can be regarded as a large-scale group decision-making problem. In existing group decision-making models, there are two hypotheses: one assumed that all experts are independent, while the other assumed that experts have certain relationships. However, in practical decision-making problems involving both internal and external experts, it is common that only some experts have mutual relationships. To address this issue, this paper proposes a large-scale group decision-making model considering the trust relationship between a set of experts. We divide experts into internal experts and external experts. The internal experts are supposed to be not independent of each other due to trust relationships, and we analyze the relationships between internal experts through the DEMATEL method. The external experts are supposed to be independent of each other. Considering the non-cooperative behaviors of experts, we provide a confidence-based adaptive consensus reaching mechanism for internal experts and a delegation-based adaptive consensus reaching mechanism for external experts. The two expert panels reach consensus through their separate consensus reaching mechanisms, and the moderator determines the optimal alternative by combining the final opinions of the two expert panels. Finally, an illustrative example about the selection of anti-non-small cell lung cancer drugs is presented to show the validity and practicality of the proposed model.

Case-based Reasoning (CBR) has been extensively used as its capability to reuse previous solvable problems in recommending new solution through its adaptation strategy. In our work, CBR is chosen as the strategy to identify the leader of specific domain within the online community based on the profile that was developed through one's social participation and contribution as well as feedbacks from the community, called CBRIKL (CBR for Identifying Knowledge Leader). CBRIKL continuously builds leader profiles based on the identified knowledge domains and problems are assigned to them based on their expertise. The novelty introduced in the paper is on building a user profiling based on mixed sources in identifying leader and measuring and comparing strategy on expertise skill set in locating new knowledge leader. The methods and approaches that are developed are made to be usable to other problem domains

The purpose of this paper is to propose a three-dimensional fuzzy logic approach to evaluate the level of alignment between the knowledge an enterprise possesses and the knowledge management systems (KMSs) it adopts. The study also aims to propose the KMSs best suited to reducing misalignment and improving operational performance in terms of efficiency and effectiveness, analysing the level of alignment between an enterprise's knowledge and its KMSs from both the ontological and epistemological points of view. The authors have used the proposed methodology to develop a software-based Knowledge Management Decision Support System (KM-DSS), which was tested on a small and medium enterprise (SME) operating in the high-tech industry. The results highlight that the proposed DSS allows managers to evaluate knowledge management processes and identify which KMSs to adopt to improve alignment with the nature of the knowledge their enterprise possesses as well as to increase their level of efficiency and effectiveness.

Today’s supply chains are very different from those of just a few years ago, and they continue to evolve within an extremely competitive economy. Dynamic supply chain processes require a technology that can cope with their increasing complexity. In recent years, several functional supply chain applications based on artificial intelligence (AI) have emerged, yet very few studies have addressed the applications of AI in supply chain processes. Machine learning, natural language processing, and robotics are all potential enablers of supply chain transformation. Aware of the potential advantages of AI implementation in supply chains and of the paucity of work done regarding it, we explore what researchers have done so far with respect to AI and what needs further exploration. We reviewed 136 research papers published between 1996 and 2020 from the Scopus database and provided a classification of the research material according to four critical structural dimensions (level of analytics, AI algorithms or techniques, sector or industry of application, and supply chain processes). This study is the first attempt to study the AI applications in SC from a process perspective and provides a decisional framework for adequate use of AI techniques in the different SC processes.

We propose personalized social individual explanations for group recommenders.We propose both a textual and a graphical social explanation approach.We study the benefits of including explanations in group recommender systems.We study the benefits of including social components to these explanations.Results show a significant increase in users intent to follow our recommendations. Recommender systems help users to identify which items from a variety of choices best match their needs and preferences. In this context, explanations act as complementary information that can help users to better comprehend the systems output and to encourage goals such as trust, confidence in decision-making or utility. In this paper we propose a Personalized Social Individual Explanation approach (PSIE). Unlike other expert systems the PSIE proposal novelly includes explanations about the systems group recommendation and explanations about the groups social reality with the goal of inducing a positive reaction that leads to a better perception of the received group recommendations. Among other challenges, we uncover a special need to focus on tactful explanations when addressing users personal relationships within a group and to focus on personalized reassuring explanations that encourage users to accept the presented recommendations. Besides, the resulting intelligent system significatively increases users intent (likelihood) to follow the recommendations, users satisfaction and the systems efficiency and trustworthiness.

A Dynamic Group Consensus reaching process is proposed. This approach uses twofold feedback mechanism: judgment feedback and priority weighting. Group AHP is applied to Supplier Involvement in Collaborative Product Development. Application of the method within the Chery company case study. The group Analytic Hierarchy Process (AHP) is an effective tool to collect experts' wisdom to evaluate complex decision making problems. Because judgments are always diverse in the real world, it is crucial to adequately support the consensus reaching process. In this paper, we develop a convergent group AHP consensus reaching model with a twofold feedback mechanism, which consists of both a judgment and a weighting feedback mechanism. In each round of this dynamic and interactive model, the most incompatible expert is asked to revise her judgment according to the judgment feedback mechanism. If the expert rejects the suggestion, her weight of importance will be adjusted downward based on the compatibility within the group by the weighting feedback mechanism. The proof of convergence of this consensus reaching model with the twofold mechanism is also provided and discussed. Hence this proposed consensus reaching process supports the leader or client in reaching a successful decision with a dispersed group of experts. The proposed consensus reaching model is applied to the brake pad supplier selection problem of Chery Automobile Co., Ltd. The empirical example demonstrates that the proposed methodology provides an operational decision framework for companies to select suitable suppliers in the supplier involvement under the environment of collaborative product development (SICPD) through its successful application in that context

A tripartite model in context of Malaysian SMEs using PLS-SEM-ANN analysis.Knowledge management and competitive advantage mediated by technological innovation.KM and TI spending are directly proportional to each other.KM spending, especially on knowledge dissemination, may lead to stronger CA. Unlike most Knowledge Management (KM) studies which focus on large enterprises, this study focuses on SMEs in Malaysia which represent 99.2% of the total business establishments, the largest percentage of establishments in the country. The tridimensional relationship between KM practices, technological innovation (TI) and competitive advantage (CA) was examined in this case study. Survey approach was conducted to gather data from managers of the manufacturing SMEs and 195 samples were usable for statistical analysis using Partial-Least-Square Structural Equation Modeling (PLS-SEM)-Artificial Neural Network (ANN). The use of the combined PLS-SEM and ANN analysis can provide a significant methodological contribution and substantial impacts to the world of expert and intelligent systems and could be the next methodological research paradigm. Findings validated that KM has a direct positive and significant relation with both TI and CA; while TI positively and significantly affects CA. Most outstandingly, the mediating role of TI that connects KM and CA has been proven to be positive and significant. This paper utilizes samples that were collected from Malaysian SMEs only; therefore the findings cannot be generalized to represent the larger firms. Nevertheless, conclusions garnered from the present research can help both practitioners of the manufacturing SMEs and scholars in implementing the correct KM strategies, so that both TI and CA can be enhanced and improved.

Among emerging technologies large attention has been devoted to the so called Fourth Industrial Revolution, or Industry 4.0, which is also a case study of a major industrial policy initiative, led by Germany. In the field of methodologies to profile and monitor emerging technologies the role of Wikipedia has been recently explored. In this paper we extend the use of Wikipedia by comparing the German edition with the world largest edition (in English) in order to examine whether there are significant structural differences. We first validate the use of Wikipedia for emerging technologies and for cross-language comparisons as a tool for (almost) real time strategic analysis. We then extract all Wikipedia pages related to Industry 4.0, build up a knowledge network and study its topological properties in the two editions. We find striking differences, which can be explained with respect to the persistence of the industrial pattern of specialization of Germany with respect to all other countries. Emerging technologies introduce novelty but also preserve path-dependency in the pattern of specialization. The implications for companies and policy makers are discussed.

The research paper purports to assess the antecedents that affect users’ behavioral intention to use wearable payment. Specifically, this empirical research examines the roles of perceived aesthetics, technology readiness, mobile usefulness, and mobile ease of use on behavioral intention. Differing from past mobile payment studies, a newly proposed methodology that involves a dual-stage analysis and an emerging Artificial Intelligence analysis named deep learning was performed on 307 usable responses. Findings revealed that all relationships were supported except for the linkage between mobile ease of use and behavioral intention. The results of this study provide valuable insights to payment companies and smart wearable device manufacturers to come up with plans and marketing strategies to convince the potential adopters to adopt wearable payment, guiding marketers to design a more successful wearable payment solution. Theoretically, the newly integrated theoretical model that incorporates Mobile Technology Acceptance Model, Fashion Theory, and Technology Readiness Theory could help ascertain the relative significance of certain determinants, providing a clearer insight on the acceptance of wearable payment among consumers.

This paper takes a text analytics approach to measuring dimensions of employee-visitor encounters that impact on visitor outcomes. A conceptual model measuring dimensions of employee-tourist encounters is implemented using a big data analytics approach more suited to large-scale online review data than the traditional, limited survey approach. Using a dictionary-based measurement approach and a large sample of reviews for hotels (n = 265,016), we test the model and the importance of the factors for leveraging perceptions of satisfaction, service and value. The results demonstrate the importance of the different dimensions of experiential value in employee-tourist encounters in creating positive tourist perceptions. This knowledge is crucial for tourism companies aiming to create experiential value for visitors, rather than simply delivering service quality.

Propose a novel decision model for group argumentation under complexities.Resolve the two-fold complexity derived from the problem and the group in our model.Evaluate our model to practice supplier selection.Explore the theoretical and practical development of a classic metasynthesis for complex system designs. This paper reports on a decision-making model that can be used for group argumentation when decisions contain twofold complexities: the problem itself and the people (i.e., the decision makers). Related studies have been well documented in literature; however, research on the group mechanism remains limited with regard to two aspects: (1) the complexities of problems and people and (2) the interaction manners of opinions derived from people. In this study, we develop a mechanism called the complex group argumentation (CGA) framework for group decision making. This solution applies the classic methodology of system designs, that is, qualitative-to-quantitative metasynthesis, and contains two core processes, namely, complexity resolution and group argumentation. From a practical perspective, we evaluate the performance of the CGA framework in the context of supplier selection (SS). Results show that our approach can satisfy the requirements of practical SS, while simultaneously coping with the disadvantages of real-world complex GDM. The results of this research can inspire studies on group argumentation in academics and provide proposals for mechanisms on the development of group support systems in the industrial community.

We propose a tool that visually guides the recommendation process to increase collaboration.It analyzes interactions and an influence diagram which warns about the collaboration circumstances.A visual explanation decision tree shows the collaboration circumstances which are understandable.Our tool provokes self-reflection and provokes sense making about collaboration. Collaborative learning incorporates a social component in distance education to minimize the disadvantages of studying in solitude. Frequent analysis of student interactions is required for assessing collaboration. Collaboration analytics arose as a discipline to study student interactions and to promote active participation in e-learning environments. Unfortunately, researchers have been more focused on finding methods to solve collaboration problems than on explaining the results to tutors and students. Yet if students do not understand the results of collaboration analysis methods, they will rarely follow their advice. In this paper we propose a tool that analyzes student interactions and visually explains the collaboration circumstances to provoke the self-reflection and promote the sensemaking about collaboration. The tool presents a visual explanatory decision tree that graphically highlights student collaboration circumstances and helps to understand the reasoning followed by the tool when prescribing a recommendation. An assessment of the tool has demonstrated: (1) the students collaboration circumstances showed by the tool are easy to understand and (2) the students could realize the possible actions to improve the collaboration process.

Nowadays soccer is the most practiced sport in the world and moves a multimillionaire market. Therefore, a club that is able to recruit and develop talented players to theirs fullest potential has a lot of advantages and economic benefits. However, in most clubs the players are selected through scouts and coaches recommendation, with predictive success based mostly on intuition than other objective criteria. In addition, it is known that talent development and identification is a multifactorial process involving many characteristics. To this end, this paper proposes the creation of performance indicators based on multivariate statistical analysis. Usual principal components and factor analysis are performed to construct physical, technical and general score and copula modeling is proposed to create the consistency index, which generalizes the Z score method. With these indicators, a web-oriented expert system for analyzing sport data in real time via R software is proposed as a powerful tool for talent identification in soccer. This system, the so called iSports, allows the monitoring and continuous comparison of athletes in a simple and efficient way, taking into account essentials aspects, as well as identifying candidate talented that have above the average performance, that is, who stand out from the studied population of soccer players. In order to promote and popularize the access of information and the statistical science applied in the sports context, the iSports system can be used in any training center of the country, impacting the increase of knowledge of the athletes in training phase at any school, city or region.

Exploration to transpose service science principles to guide service systems design.Modelling of value co-creation service interactions embedding customer experience.Description of the Socio-Technical Systems Engineering (STSE) process.Exploratory case study of a real time information and travel planning service.Integration guidelines of the STSE process steps with Presage2 multiagent platform. This paper describes an exploration towards the transposition of service science principles into design guidelines. This aims at capturing value co-creation service interactions embedding customer experience in service design and delivery activities. The new Socio-Technical Systems Engineering (STSE) process is proposed to guide improved design, and it is exemplified with a new real time service that provides integrated information for trip planning in a city. The STSE process supports a high level visual modelling approach assisted by model execution and simulation tools. From a service engineering perspective, the outcomes of this process are artefacts that automatically support consistency among design steps and effective integration of customer experience and stakeholder requirements through iterative cycles related to service design. The application of the STSE process in the design of an exploratory case study of a real time information and travel planning service is validated through simulation using an executable representation of requirements. It can be executed over more complex transport service offerings, with different resource allocation algorithms, or different public transport planning services over a sample of real users requesting information. Results of such a simulation are beneficial for the users, for the service providers, and for the authorities managing public transport in city or metropolitan areas.

Interlocking directors play a crucial role in corporate decision making and profitability of an organization. They also act as information channels between corporate entities and various hypotheses have been proposed in the literature regarding effectiveness of board-interlocks. Generally these hypotheses are tested against country specific data over a given time period. Therefore, a network diffusion model of companies, with interlocking directors as the links, would serve as an expert system to test hypotheses and policies in a more general framework. Such a model will also be useful in decision making and identifying organizations and directors for board-interlock for optimal performance. In this paper, we propose a hybrid multi-edge directed weighted network model and an agent based simulation scheme for analysing the diffusion process through board interlocks. The general framework introduced in this work is capable of incorporating individual characteristics of directors such as personal identification with a corporate firm. We test the hypothesis that the organisational affinity of interlocking directors does not affect the rate of knowledge transfer using the Indian Company Affiliation Network (ICAN) and the results show that organisational affinity affects the diffusion rate in a statistically significant manner. This is further confirmed with three other random networks of different topologies. The model is also used to simulate knowledge diffusion of the Indian Company Affiliation Network (ICAN) and investigate the uniformity of diffusion rates across various clusters.

We study the link between social network analysis and project selection.We develop a multi-criteria model to integrate social network information into project selection.We calculate social network criteria from a research for development network.The presented model is extendable to a variety of project selection decision processes. The social relationships between development agencies, non-governmental organizations, private companies, and other groups working on development projects play an important role in the overall success of projects. However, traditional project selection and prioritization processes ignore the organizational relationships. This paper proposes to integrate social network analysis into multi-criteria decision-making processes to enhance the effectiveness of project selection. A set of topological metrics of social network are used to quantitatively measure the organizational relationships and integrated into the analytic network process (ANP) to form a multi-criteria ANP project selection model. Utilizing empirical social network data of a water and food security research for development network in the Mekong River Basin, we investigate the effectiveness of the proposed model. The results show that it will offer companies, government agencies, and other donor organizations the opportunity to prioritize strategic network goals simultaneously with research and development priorities, and help companies and research organizations to increase their impact and reach within networks.

Visual ontology design is affected by cognitive peculiarities of expert or analyst.Field-independence, reflection and category width are main cognitive style features.Ontology assessment is feasible via cognitive ergonomic metrics.Collaborative ontology design may have several different scenarios. The paper presents the main results of the KOMET (Knowledge and cOntent structuring via METhods of collaborative ontology design) project, which aims to develop a novel paradigm for knowledge structuring based on the interplay between cognitive psychology and ontology engineering. By the knowledge structure (a conceptual model) we define the main domain concepts and relations between them in the form of a graph, map or diagram. This approach considers individual cognitive styles and uses recent advances in knowledge engineering and conceptual structuring; it aims to create new, consistent and structurally holistic knowledge bases for various areas of science and technology. Two stages of research have been completed: research into correlations between the expert's individual cognitive style and the peculiarities of the expert's subject domain ontology development; and research into correlations between the expert's individual cognitive style and the group ontology design (including design accomplished by groups of experts with either similar or different cognitive styles). The results of these research stages can be applied to organizing collaborative ontology design (especially for research and learning purposes), data structuring and other group analytical work. Implications for practice are briefly delineated.

Complete definition and description of integration function and postulates.Proposition of Aug function, which determines new knowledge gained in integration.Consistent description of multi-level integration and necessary postulates.Example of multi-level integration for two specific cases in tree integration.Multiple examples of integration function and criteria for trees. Techniques for processing knowledge in collectives are more and more needed because of rapidly increasing number of autonomous sources of knowledge in the world. Collective intelligence, among others, deals with creating the knowledge of a collective which is consistent and complete. This means that it should contain all elements not belonging to the knowledge of collective members, but can be inferred on the basis of knowledge of them. For this process the methodologies for knowledge integration seem to be very useful. In this paper the authors present a framework for integrating knowledge of a collective which shows that knowledge of a collective should not be a normal sum of knowledge of its members. The model for knowledge integration using complex hierarchical structures has been also presented and analyzed.

Providing alert communication in emergency situations is vital to reduce the number of victims. However, this is a challenging goal for researchers and professionals due to the diverse pool of prospective users, e.g. people with disabilities as well as other vulnerable groups. Moreover, in the event of an emergency situation, many people could become vulnerable because of exceptional circumstances such as stress, an unknown environment or even visual impairment (e.g. fire causing smoke). Within this scope, a crucial activity is to notify affected people about safe places and available evacuation routes. In order to address this need, we propose to extend an ontology, called SEMA4A (Simple EMergency Alert 4 [for] All), developed in a previous work for managing knowledge about accessibility guidelines, emergency situations and communication technologies. In this paper, we introduce a semi-automatic technique for knowledge acquisition and modeling on accessible evacuation routes. We introduce a use case to show applications of the ontology and conclude with an evaluation involving several experts in evacuation procedures. © 2014 Elsevier Ltd. All rights reserved.

In a context characterized by a growing demand for networked services, users of advanced applications sometimes face network performance troubles that may actually prevent them from completing their tasks. Therefore, providing assistance for user communities that have difficulties using the network has been identified as one of the major issues of performance-related support activities. Despite the advances network management has made over the last years, there is a lack of guidance services to provide users with information that goes beyond merely presenting network properties. In this light, the research community has been highlighting the importance of User-Perceived Quality (UPQ) scores during the evaluation of network services for network applications, such as Quality of Experience (QoE) and Mean Opinion Score (MOS). However, despite their potential to assist end-users to deal with network performance troubles, only few types of network applications have well established UPQ scores. Besides that, they are defined through experiments essentially conducted in laboratory, rather than actual usage. This paper thus presents a knowledge and Collaboration-based Network Users’ Support (CNUS) Case-Based Reasoning (CBR) Process that predicts UPQ scores to assist users by focusing on the collaboration among them through the sharing of their experiences in using network applications. It builds (i) a knowledge base that includes not only information about network performance problems, but also applications’ characteristics, (ii) a case base that contains users’ opinions, and (iii) a user database that stores users’ profiles. By processing them, CNUS benefits users through the indication of the degree of satisfaction they may achieve based on the general opinion from members of their communities in similar contexts. In order to evaluate the suitability of CNUS, a CBR system was built and validated through an experimental study conducted in laboratory with a multi-agent system that simulated scenarios where users request for assistance. The simulation was supported by an ontology of network services and applications and reputation scheme implemented through the PageRank algorithm. The results of the study pointed to the effectiveness of CNUS, and its resilience to users’ collusive and incoherent behaviors. Besides that, they showed the influence of the knowledge about network characteristics, users’ profiles and application features on computer-based support activities.

Nowadays, there is an increasing demand for the identification of an organization's intellectual capital (IC) for decision support and providing important managerial insights in knowledge-intensive industries. In traditional approaches, identification of an organization's IC is usually done manually through interviews, surveys, workshops, etc. These methods are labor and time intensive and the quality of the results is highly dependent on, among other things, the experience of the investigators. This paper presents a Knowledge-based Intellectual Capital Extraction (KBICE) algorithm which incorporates the technologies of computational linguistics and artificial intelligence (AI) for automatic processing of unstructured data and extraction of important IC-related information. The performance of KBICE was assessed through a series of experiments conducted by using publicly available financial reports from the banking industry as the testing batch and encouraging results have been obtained. The results showed that, through the use of hybrid intelligent matching strategies, it is possible to extract commonly referred IC-related information from unstructured data automatically. IC information analyst can rely on this method as an additional mean to identify and extract the commonly sought IC information from financial reports in a fast, systematic and reliable manner.

Despite the importance of identifying industry-wide impact of technology fusion, there are few studies to analyze interdisciplinary trends of technology convergence from an industry-wide perspective. This paper, therefore, presents a procedural method to analyze trends of industry-wide technology fusion by measuring knowledge flows of patents. The method constructs a technological knowledge flow matrix that represents knowledge flows among technology classes, and then extends it to an industry-wide knowledge flow matrix by exploiting the concordance between technology classes and industrial sectors. By computing assessment indicators of technology fusion regarding industrial sectors of a specific technology area, the method outputs a visual map that shows trends of industrial technology fusion. The presented method is illustrated using patents related to the new and renewable energy-based railway technology. We expect that the method will be incorporated into the R&D planning processes to assist R&D planners to initiate new R&D projects with a proper direction. Under these directions, the R&D projects can create new inventions by converging prominent technologies beyond industrial boundaries. Further, the method has the potential to become a basis of systematic support systems for technology experts to conduct knowledge-intensive technology planning activities.

This paper presents four synergistic systems that exemplify the approaches and benefits of case-based reasoning in medical domains. It then explores how these systems couple Artificial Intelligence (AI) research with medical research and practice, integrate multiple AI and computing methodologies, leverage small numbers of available cases, reason with time series data, and integrate numeric data with contextual and subjective information. The following systems are presented: (1) CARE-PARTNER, which supports the long-term follow-up care of stem-cell transplantation patients; (2) the 4 Diabetes Support System, which aids in managing patients with type 1 diabetes on insulin pump therapy; (3) Retrieval of HEmodialysis in NEphrological Disorders, which supports hemodialysis treatment of patients with end stage renal disease; and (4) the Malardalen Stress System, which aids in the diagnosis and treatment of stress-related disorders.

The aim of this study is to identify and prioritize the solutions of Knowledge Management (KM) adoption in Supply Chain (SC) to overcome its barriers. It helps organizations to concentrate on high rank solutions and develop strategies to implement them on priority. This paper proposes a framework based on fuzzy analytical hierarchy process (AHP) and fuzzy technique for order performance by similarity to ideal solution (TOPSIS) to identify and rank the solutions of KM adoption in SC and overcome its barriers. The AHP is used to determine weights of the barriers as criteria, and fuzzy TOPSIS method is used to obtain final ranking of the solutions of KM adoption in SC. The empirical case study analysis of an Indian hydraulic valve manufacturing organization is conducted to illustrate the use of the proposed framework for ranking the solutions of KM adoption in SC to overcome its barriers. This proposed framework provides a more accurate, effective and systematic decision support tool for stepwise implementation of the solutions of KM adoption in SC to increase its success rate.

The processes of logistics service providers are considered as highly human-centric, flexible and complex. Deviations from the standard operating procedures as described in the designed process models, are not uncommon and may result in significant uncertainties. Acquiring insight in the dynamics of the actual logistics processes can effectively assist in mitigating the uncovered risks and creating strategic advantages, which are the result of uncertainties with respectively a negative and a positive impact on the organizational objectives. In this paper a comprehensive methodology for applying process mining in logistics is presented, covering the event log extraction and preprocessing as well as the execution of exploratory, performance and conformance analyses. The applicability of the presented methodology and roadmap is demonstrated with a case study at an important Chinese port that specializes in bulk cargo.

Due to globalization, logistics has become an important part in the supply chain. Many logistics service providers have realised the importance of adoption of technologies that can help manufacturers, warehouses, and retailers to communicate with each other more efficiently. Among many logistics technologies, Radio frequency identification (RFID) has been identified as an important technology to improve logistics operations and supply chain management, and thus is increasingly gaining both practitioners’ and researchers’ attention. The purpose of this study is to identify the impact of usability features of RFID in the adoption of the technology by the logistics sector in the UK. We have used questionnaire survey method to collect data from the UK Logistics firms. The analysis of the data shows that the usability of RFID technology positively influences adoption of technology. We have further tested the moderating effects of firm size, experience with barcode use, and government support in adopting RFID. Our results show that government support strongly moderates the link between usability of RFID and its adoption but size and experience with barcode do not moderate this link. We elaborate the contributions of the study and managerial implications of our results in this paper.

In a complex business world, characterised by globalisation and rapid rhythms of change, understanding supply chain (SC) operation dynamics is crucial. This paper describes a logic-based approach to analysing SC operation dynamics, named SCOlog. SC operation is modelled in a declarative fashion and it is simulated following rule-based execution semantics. This approach facilitates the automated explanation of simulated SC operational behaviours and performance. The automated explanation support provided by SCOlog is found to improve the understanding of the domain for non-SCM experts. Furthermore, SCOlog allows for maintainability and reusability.

In recent years, the attention that the European Community has focused on the education sector has produced a new university commitment addressed to quality aspects for all education related services. In fact, a quality oriented service requires excellence in the design and planning of service activities, as well as during its delivering and also for the adopted service performance evaluation method. However, considering that service performance evaluations are deeply based on stakeholders' judgments, they can be characterized by possible uncertainties related to incompleteness for partial ignorance, imprecision for subjectivity and even vagueness. Therefore, under these conditions, unreliable results can be obtained by widely considered service analysis methods. In the present paper, a method based on a recent extension of the ServQual model and that uses in combined manner the Fuzzy Set Theory and the Analytic Hierarchy Process method is proposed to effectively handle uncertainty in service performance analyses. In particular, the Fuzzy Set Theory is considered to deal with such uncertainty, whereas the AHP method is adopted as tool to estimate the importance weights of the strategic service attributes. Subsequently, the strategic analysis of the service value tree related to the Management Engineering program at the University of Palermo (Italy) is performed by using the proposed method. The performed service analysis allows the most influencing service performance factors to be captured and commented upon. Finally, the obtained results show that the professors' perception of service quality meaningfully influences the overall service performance level.

It is now very popular for companies to collaborate as a global supply chain (GSC) for their business benefits. Many companies are inclined to outsource manufacturing, logistics and business activities globally. The senior managers of companies are faced with more complicated and dynamic situations to make decisions than ever before. They not only have to consider the internal factors including production, inventory, and financial status, but also have to take into account the external factors such as policies, market forces, competitive behaviors, etc. To survive in today’s fierce market environment, it has become increasingly important for companies to find ways to combine the multi-source decision knowledge, and utilize it to make sound decisions across the organizational boundaries. In this paper, a rule-based ontology reasoning method is proposed to support decision makings and improve industrial practices for companies in the dynamic and heterogeneous GSC context. A shared GSC ontology is developed to describe the heterogeneous internal and external decision knowledge of the GSC companies and the dynamic market environments. It is contributed in enabling a semantic interoperable decision-making environment, along with the decision knowledge being evolved timely. In addition, semantic rules serving as decision requirements are developed to reason the shared GSC ontology to support the complicated and sound decision-makings, and also to provide suggestions on improving their industrial practices. A case study in China’s iron and steel industry is introduced to justify the feasibility and effectiveness of the proposed ontology-based approaches.

In the process of analyzing knowledge innovation, it is necessary to identify the existing boundaries of knowledge so as to determine whether knowledge is new - outside these boundaries. For a patent to be granted, all aspects of the patent request must be studied to determine the patent innovation. Knowledge innovation for patent requests depends on analyzing current state of the art in multiple languages. Currently the process is usually limited to the languages and search terms the patent seeker knows. The paper describes a model for representing the patent request by a set of concepts related to a multilingual knowledge ontology. The search for patent knowledge is based on Fuzzy Logic Decision Support and allows a multilingual search. The model was analyzed using a twofold approach: a total of 104,296 patents from the United States Patent and Trademark Office were used to analyze the patent extraction process, and patents from the Korean, US, and Chinese patent offices were used in the analysis of the multilingual decision process. The results display high recall and precision and suggest that increasing the number of languages used only has minor effects on the model results.

Practical agent languages and their corresponding architectures have often relied on a static plan library with more or less direct trigger-response activation mechanisms as a source for agent behaviours for the sake of runtime efficiency. Although efficient, such a language design choice severely limits an agent's ability to reason about its goals and adapt to unforeseen circumstances after being deployed. This effectively delegates the task of planning to the designers themselves, who must design plan libraries able to cope with every foreseeable situation an agent might find itself in by designing plans to deal with any contingency. In this paper we develop a formal conversion process from traditional BDI agent languages into declarative planning. Using this conversion process, we show how to integrate domain independent planning algorithms into the BDI interpreter, allowing a designer to program an agent not only through the trigger-response mechanism used in traditional languages, but also in terms of declarative goals. Our contribution here is twofold: firstly we increase an agent's ability to cope with unforeseen situations and secondly we unburden an agent designer from having to define multiple plan combinations that could be easily generated by a planner.

Manufacturing technology selection is traditionally a human-driven approach where the trade-off of alternative manufacturing investments is steered by a group of experts. The problem is a semi-structured and subjective-based decision practice influenced by the experience and intuitive feeling of the decision-makers involved. This paper presents a distinct experience-based decision support system that uses factual information of historical decisions to calculate confidence factors for the successful adoption of potential technologies for a given set of requirements. A fuzzy-decision-tree algorithm is applied to provide a more objective approach given the evidence of previous manufacturing technology implementation cases. The model uses the information relationship of key technology decision variables, project requirements of an implemented technology case and the success outcome of a project to support decision problems. An empirical study was conducted at an aircraft manufacturer to support their technology decision for a typical medium complexity assembly investment project. The experimental analysis demonstrated encouraging results and practical viability of the approach.

Determining the importance of different management areas in a company provides guidance about the needs of increasing the analysis and actions focuses in particular topic. To do it, it is necessary to decompose the management in a coherent set of specific management areas and provide a way that allows the company to determine the importance of these areas for them. This paper presents a novel system that guides companies to obtain a classification of important management areas for them. It is focused on the use of a case based reasoning system because the variability and the evolution of companies as time passes requires using techniques with learning capabilities. The proposed system provides an automatic self-assessment system that provides companies an ordered list of their most important management areas. This system was implemented a year ago for the evaluation of Spanish companies. Currently, it is in production providing relevant information about the management areas of these companies.

The improvement of the quality of the services is one of the primary sources of competitive advantage in health care organizations. As customers typically search for higher quality of care when choosing treatments, health plans and providers, the health care organizations strive to improve the quality of care and patient safety and satisfaction, as primary goals, with the resources that are available. To do so, the European Foundation for Quality Management (EFQM) Excellence Model for self-assessment has been used by the health care organizations to improve their services and their competitiveness in the global market. However, when the health care organizations address self-assessment processes for the first time, the initial effort needed presents many difficulties. The aim of this paper is to offer a consensus support model based on linguistic information to conduct the self-assessment of the EFQM Excellence Model when questionnaires are used. It is based on the use of linguistic information to provide the individuals' opinions, which facilitates the individual responses, and on the use of fuzzy majority, represented by means of a linguistic quantifier, to compute the measures guiding the consensus reaching process.

An increasing number of publications concerning data mining in the subject of human resource management (HRM) indicate the presence of a prospering new research field. The current paper reviews this research on HR data mining to systematically uncover recent advancements and suggest areas for future work. Based on the approach of domain driven data mining, an initial framework with significant domain-specific requirements is elaborated. Relevant research contributions are identified and reviewed against the background of this framework. The review reveals that HRM constitutes a noteworthy new domain of data mining research that is dominated by method- and technology-oriented work. However, specific domain requirements, such as evaluating the domain success or complying with legal standards, are frequently not recognized or considered in current research. Therefore, the systematic consideration of domain-specific requirements is demonstrated here to have significant implications for future research on data mining in HRM.

International audience; The increasing importance of ontologies has resulted in the development of a large number of ontologies in both coordinated and non-coordinated efforts. The number and complexity of such ontologies make hard to ontology and tool developers to select which ontologies to use and reuse. So far, there are no mechanism for making such decisions in an informed manner. Consequently, methods for evaluating ontology quality are required. OQuaRE is a method for ontology quality evaluation which adapts the SQuaRE standard for software product quality to ontologies. OQuaRE has been applied to identify the strengths and weaknesses of different ontologies but, so far, this framework has not been evaluated itself. Therefore, in this paper we present the evaluation of OQuaRE, performed by an international panel of experts in ontology engineering. The results include the positive and negative aspects of the current version of OQuaRE, the completeness and utility of the quality metrics included in OQuaRE and the comparison between the results of the manual evaluations done by the experts and the ones obtained by a software implementation of OQuaRE.

Highlights? Develop a new rule-based decision model under interval-valued intuitionistic fuzzy environments. ? Mathematical modeling of warehouse evaluation towards logistic and supply chain management. ? Construct an uncertain group decision process for warehouse evaluation. We consider the issue of warehouse evaluation towards successful logistic and supply chain management. Suppose a company has managed a chain of owned warehouses, and now this company is in need of acquiring some new and profitable warehouse adding to its operation chain. A key business decisions here is how to choose the most profitable warehouses from a number of potential warehouses. In reality, the challenge is that the future profitability is unpredictable. Therefore, it is infeasible to rank potential warehouses directly for choice. To address such a problem, this paper proposes a new rule-based decision model. This model includes the following characteristics: (i) decision information is provided via interval-valued intuitionistic fuzzy values; (ii) multiple experts as a group of decision makers are involved; (iii) both subjective evaluations from experts and objective data of historical profitability are employed; (iv) both certain and uncertain information are exploited. The core decision mechanism is, making use of uncertain information of owned warehouses, to induce a collection of "if?then?"rules, and subsequently to exploit these rules for prediction of preference orders of all potential warehouses. Therein, we develop and integrate multiple techniques for the purposes of (a) aggregation of uncertain information; (b) construction of pairwise comparison; (c) induction of certain and uncertain rules; and (d) decision rules exploitation. We finally elaborate our discussion with a numerical example illustrating the application of the proposed decision mechanism to supply-chain domain problems.