Search

Highlights • The construction industry is knowledge intensive and relies on effective KM. • There are various weaknesses within existing IT-based KM tools or systems. • BIM has distinctive features to support KM in construction projects. • BIM-supported KM is advantageous over IT-based KM. • BIM-supported KM is just emerging and therefore needs future development. Abstract Construction is a knowledge-intensive industry, in which organizations are known for the delivery of products and services, relying on different types of knowledge. To manage knowledge effectively, various information technology (IT) models and systems have been developed for knowledge management (KM) in the construction industry over the years. As the next generation of IT application in construction, building information modeling (BIM) is increasingly used today to aid KM. Compared to generic IT tools, BIM has some distinctive features, such as parametric modeling, virtual visualization and centralized platform. However, how to apply these features of BIM to better serve KM has not yet been well summarized and analyzed. In this research, 115 papers on IT-based KM and 73 papers on BIM-supported KM are reviewed, based on which an integrated framework is developed to describe the current status and future directions for KM in IT-generic and BIM-specific contexts. It is followed by a conceptual model of BIM-supported KM which shows the possible KM factors and their relationships in the BIM environment. This research highlights the transformation from IT-based KM into BIM-supported KM. It contributes to the identification of remaining challenges to BIM-supported KM and the elaboration of BIM-supported KM in construction research and practice. [ABSTRACT FROM AUTHOR]

• Proposing an iterative and incremental knowledge management model for problem solving. • Developing a new search intent predicting method. • Designing a knowledge learning strategy addressing knowledge visualization techniques. • Achieving an interactive healthcare knowledge management system. Healthcare is a knowledge intensive process, which requires large amount of information from various healthcare knowledge providers. However, this growing information makes challengeable for users in finding useful healthcare information to support their healthcare decision making. Knowledge management and knowledge management systems are drawing increasing attention in healthcare community. This paper proposes an online healthcare knowledge management framework and system prototype, featured with integrating knowledge learning and exploratory search for solving problems efficiently. Inspired by the knowledge to action framework in healthcare and the iterative process in software engineering, an iterative and incremental knowledge management model is designed to organize the activities during the knowledge learning and searching procedure. Supported by the knowledge-enhanced infrastructure of online healthcare information designed, domain knowledge learning and task knowledge learning environment can work collaboratively and interactively to improve the result of learning and exploratory search. Knowledge base also provides helpful information in an interactive way for solving problems. Given exploiting probabilistic information retrieval model and concept semantics described in Description Logics, a search intent discovery method and search intent guided learning and searching mechanism are designed within this environment for achieving the learning and search goal. The results of the experiments have proved the efficacy of the methods and process model used in the leaning-focused healthcare knowledge management system. [ABSTRACT FROM AUTHOR]

This paper presents a framework to build home automation systems reactive to voice for improved comfort and autonomy at home. The focus of this paper is on the context-aware decision process which must reason from uncertain facts inferred from real sensor data. This framework for building context aware systems uses a hierarchical knowledge model so that different inference modules can communicate and reason with same concepts and relations. The context-aware decision module is based on a Markov Logic Network, a recent approach which make it possible to benefit from formal logical representation and to model uncertainty of this knowledge. In this work, uncertainty of the decision model has been learned from data. Although some expert systems are able to deal with uncertainty, the Markov Logic Network approach brings a unified theory for dealing with logical entailment, uncertainty and missing data. Moreover, the ability to use a priori knowledge and to learn weights and structure from data make this model appealing to address the challenge of adaptation of expert systems to new applications. Finally, the framework has been implemented in an on-line system which has been evaluated in a real smart home with real naive users. Results of the experiment show the interest of context-aware decision making and the advantages of a statistical relational model for the framework. [ABSTRACT FROM AUTHOR]

An important step in building expert and intelligent systems is to obtain the knowledge that they will use. This knowledge can be obtained from experts or, nowadays more often, from machine learning processes applied to large volumes of data. However, for some of these learning processes, if the volume of data is large, the knowledge extraction phase is very slow (or even impossible). Moreover, often the origin of the data sets used for learning are measure processes in which the collected data can contain errors, so the presence of noise in the data is inevitable. It is in such environments where an initial step of noise filtering and reduction of data set size plays a fundamental role. For both tasks, instance selection emerges as a possible solution that has proved to be useful in various fields. In this paper we focus mainly on instance selection for noise removal. In addition, in contrast to most of the existing methods, which applied instance selection to classification tasks (discrete prediction), the proposed approach is used to obtain instance selection methods for regression tasks (prediction of continuous values). The different nature of the value to predict poses an extra difficulty that explains the low number of articles on the subject of instance selection for regression. More specifically the idea used in this article to adapt to regression problems “classic” instance-selection algorithms for classification is as simple as the discretization of the numerical output variable. In the experimentation, the proposed method is compared with much more sophisticated methods, specifically designed for regression, and shows to be very competitive. The main contributions of the paper include: ( i ) a simple way to adapt to regression instance selection algorithms for classification, ( ii ) the use of this approach to adapt a popular noise filter called ENN (edited nearest neighbor), and ( iii ) the comparison of this noise filter against two other specifically designed for regression, showing to be very competitive despite its simplicity. [ABSTRACT FROM AUTHOR]

Abstract: Constant advances in construction planning and site management are crucial to improving construction productivity. This paper presents an intelligent system MoCCAS (a monolithic construction computer aided system), which is a comprehensive decision support tool for flexible construction site management in repetitive projects. MoCCAS aids the construction site manager in developing optimal and attainable execution scenarios by providing contextualized variants of construction strategies. The discussion is centered on the modeling procedures of repetitive processes with the special focus on the synchronization of resources and workflow continuity, which is crucial in scheduling and managing repetitive projects. The paper introduces an innovative conceptualization of the construction process and presents a dynamic three-dimensional construction process model in a temporal perspective. How the system synchronizes the work of resources combined into a balanced production sets and also supports the decision maker in creating what-if scenarios in designing execution strategies is discussed. [Copyright &y& Elsevier]

Abstract: Information systems (IS) facilitate organisations to increase responsiveness and reduce the costs of their supply chain. This paper seeks to make a contribution through exploring and visualising knowledge mapping from the perspective of IS investment evaluation. The evaluation of IS is regarded as a challenging and complex process, which becomes even more difficult with the increased complexity of IS. The intricacy of IS evaluation, however, is due to numerous interrelated factors (e.g. costs, benefits and risks) that have human or organisational dimensions. With this in mind, there appears to be an increasing need to assess investment decision-making processes, to better understand the often far-reaching implications associated with technology adoption and interrelated knowledge components (KC). Through the identification and extrapolation of key learning issues from the literature and empirical findings, organisations can better improve their business processes and thereby their effectiveness and efficiency, while preventing others from making costly oversights that may not necessarily be only financial. In seeking to enlighten the often obscure evaluation of IS investments, this paper attempts to inductively emphasise the dissemination of knowledge and learning through the application of a fuzzy Expert System (ES) based knowledge mapping technique (i.e. Fuzzy Cognitive Map [FCM]). The rationale for exploring knowledge and IS investment evaluation is that a knowledge map will materialise for others to exploit during their specific technology evaluation. This is realised through conceptualising the explicit and tacit investment drivers. Among the several findings drawn from this research, the key resulting knowledge mapping through FCM demonstrated the complex, multifaceted and emergent behaviour of causal relationships within the knowledge area. The principal relationships and knowledge within IS investment evaluation are illustrated as being determined by a blend of managerial and user perspectives. [Copyright &y& Elsevier]

Abstract: Knowledge management (KM) and data mining (DM) have become more important today, however, there are few comprehensive researches and categorization schemes to discuss the characteristics for both of them. Using a bibliometric approach, this paper analyzes KM and DM research trends, forecasts and citations from 1989 to 2009 by locating headings “knowledge management” and “data mining” in topics in the SSCI database. The bibliometric analytical technique was used to examine these two topics in SSCI journals from 1989 to 2009, we found 1393 articles with KM and 1181 articles with DM. This paper implemented and classified KM and DM articles using the following eight categories—publication year, citation, country/territory, document type, institute name, language, source title and subject area—for different distribution status in order to explore the differences and how KM and DM technologies have developed in this period and to analyze KM and DM technology tendencies under the above result. Also, the paper performs the K–S test to check whether the distribution of author article production follows Lotka’s law. The research findings can be extended to investigate author productivity by analyzing variables such as chronological and academic age, number and frequency of previous publications, access to research grants, job status, etc. In such a way characteristics of high, medium and low publishing activity of authors can be identified. Besides, these findings will also help to judge scientific research trends and understand the scale of development of research in KM and DM through comparing the increases of the article author. Based on the above information, governments and enterprises may infer collective tendencies and demands for scientific researcher in KM and DM to formulate appropriate training strategies and policies in the future. This analysis provides a roadmap for future research, abstracts technology trend information and facilitates knowledge accumulations, therefore the future research can concentrated in core categories. This implies that the phenomenon “success breeds success” is more common in higher quality publications. [Copyright &y& Elsevier]

Abstract: Subgroup discovery is a descriptive data mining technique which aims at obtaining interesting rules through supervised learning. In general, there are no works analysing the consequences of the presence of missing values in data in this task, although improper handling of this type of data in the analysis may introduce bias and can result in misleading conclusions being drawn from a research study. This paper presents a study on the effect of using the most relevant approaches for pre-processing of missing values in a determined group of algorithms, the evolutionary fuzzy systems for subgroup discovery. The experimental study presented in this paper show that, among the methods studied, the KNNI pre-processing approach for missing values obtains the best results in evolutionary fuzzy systems for subgroup discovery. [Copyright &y& Elsevier]

Abstract: This paper aims to contribute to the debate on the role of knowledge management in supply chain management by reviewing the published literature. A total of 58 selected referred journal articles were systematically analyzed. This review identifies various theoretical and methodological characteristics of the way in which knowledge management applications are proposed in the supply chain context. The review shows that little evidence exists of the positive relation between the use of IT solutions and firms’ performance. Some issues remain unexplored such as the problem of knowledge obsolescence in supply chain management. A deeper understanding of the knowledge accumulation process could give new insights. The paper concludes with some future directions for theory construction and empirical research. [Copyright &y& Elsevier]

Abstract: A collaborative team usually consists of team members with various domains. These members’ demands for knowledge are also different from each other. For recommending potentially useful knowledge to suitable members, their user profiles should be well managed and maintained. User profile can be input by the members, but a more intelligent way should be the automatic extraction of the user profiles. Workflow and information flow are two types of collaborative processes, which exist behind every collaborative team. This paper is mainly concerned with how to extract these team members’ user profile from the two types of contexts: workflow and information flow. This paper defines a model for the user profile. Then some methods are proposed for extracting the profile information on the basis of workflow and information flow. This study on the user profile extraction can pave the way for developing knowledge recommender systems, which can recommend proper knowledge to proper team members with a collaborative team. [Copyright &y& Elsevier]

Highlights • A novel intelligent, collaborative health knowledge system supporting diagnosis. • Temporal rule and knowledge management. • An inference engine handling the embedded time and uncertainty. • A conflict detection policy for supporting rule generation. • A well-designed and user-friendly web interface with strong collaboration features. Abstract The huge diversity and quantity of data and information, and the requirements for knowledge extraction out of them put new challenges for knowledge management, synthesis, conflict detection and reasoning This paper elaborates on the design and development of COSMOS, an intelligent system which supports a) collaboration of experts for developing common knowledge bases and b) diagnosis derivation which takes into account time and uncertainty. The health domain is used for illustration and discussion of the features of our approach. Initially, we present the syntax and the semantics of the rules which incorporate time (temporal rules) and the data items upon which reasoning is performed. Then we introduce its inference engine, able to perform reasoning on top of rules and data, handling also the embedded time and uncertainty. We proceed further to define a conflict detection policy for supporting the difficult and error prone task of rule generation. The complexities of the aforementioned tasks are hidden from users via a well-designed and user friendly web interface that possesses strong collaboration features enabling multiple experts to work on defining a rule and on developing a common knowledge base. Evaluation of COSMOS has been performed using a) students studying expert systems and b) health experts in order to demonstrate the usability of the approach and the considerable advantages gained. To the best of our knowledge, COSMOS is one of the very few systems combining temporal rules, a powerful inference engine handling uncertainty and conflict detection and progresses beyond state of the art by adopting strong collaboration features and paradigms. [ABSTRACT FROM AUTHOR]

Abstract: Knowledge management involves the systematic management of vital knowledge resources and the associated processes of creating, gathering, organizing, diffusion, utilizing and exploiting information. A key challenge emerging for organizations is how to encourage knowledge sharing within an organization because knowledge is an organization’s intellectual capital and is of increasing importance in gaining a competitive business advantage. Isolated initiatives for promoting knowledge sharing and team collaboration without taking into consideration the limitations and constraints of knowledge sharing can halt any further development in the KM culture of an operation. This article investigates knowledge sharing bottlenecks and proposes the use of conversational knowledge sharing as an effective instrument for knowledge sharing. And to develop strategies, this paper determines the causes and effects of knowledge barriers and proposes solutions by using HOQ. The article introduces a financial company case study as a best practice example of conversational knowledge sharing. Then, the paper analyzes the case study to provide evidence for the feasibility and effectiveness of the proposed approach. [Copyright &y& Elsevier]

Abstract: The rapid development of information technology and the emergence of the Internet have created a borderless business environment and intensified market competition. Riding on the globalization trend, high-tech companies have been gradually leveraging information technology in order to shorten their manufacturing processes, enhance productivity with lower costs and prompt delivery to meet the customers’ needs. To achieve these targets and maintain competitive advantages, companies have been introducing enterprise resource planning (ERP) and knowledge management (KM). This paper finds, via literature review, that most scholars focus only on the deployment of ERP systems and improvement of flows. Few have introduced the KM concept into ERP systems. This paper collated the literature relevant to ERP and KM and integrates the findings to introduce the ERP KM concept. The most important thing is to establish a detailed introduction plan and a prior understanding of the critical success factors (CSFs) for ERP KM introduction. This paper summarizes the CSFs for ERP KM introduction via literature review and examines the influence of these CSFs on management performance. A questionnaire survey is conducted to collect the relevant data and SPSS 10.0 (statistics software) is run for statistical and multiple regression analyses. Among these CSFs, support from senior managers, corporate vision, reengineering of corporate flows and project management, selection of appropriate consulting firms and software suppliers, the identification of suitable employees to take part in ERP introduction and the proper training and education programs have positive influences on management performance. In the multiple regression analysis, all of the individual constructs are positively and significantly correlated. The explanatory power of individual variables is high. It is hoped that the research finding can serve as a reference for ERP KM introduction to corporations. [Copyright &y& Elsevier]

Abstract: This paper investigates the effect of organizational demographic variables on successful knowledge management (KM) implementation which insofar has not been thoroughly reported in the KM literature. For meaningful results to be generated, four organizational demographic variables, namely functional areas, years of KM involvement, KM development stage, and degree of knowledge intensity were moderated against a comprehensive set of KM activities, which comprise of KM preliminary success factors, KM strategies and KM processes, with organizational performance. The respondents comprised of middle managers working in the telecommunication industry in Malaysia. Based on the data collected from 289 respondents using a set of structured questionnaire, the results reveal that all the four demographic characteristics interacted with the degree of implementation of the KM activities, while three of the characteristics, with exception of functional areas, show significant relationships with organizational performance. The contributions of the paper, along with the implications of the results are discussed and interpreted to provide guidance to organizations for improved business performance through KM implementation success. [Copyright &y& Elsevier]

Abstract: This paper proposes a model of the inner-enterprise knowledge recommender system. Among an organization, different members have different demands for knowledge in different context. Comparing with traditional knowledge query way, the knowledge recommender systems supply us a more proactive way that could deliver the proper knowledge to the proper people at the proper time. The recommendation mechanism is based on semantic matching on context information from both users’ side and knowledge’s side. Recommendation rules are also maintained in the recommendation engine, which is the core module in the system. By adjusting the rules, the configuration of the knowledge recommender system could be adapted to different users. This paper presents the system design as well as some key technologies analysis, and also discusses the advantages and preconditions for implementing the proposed system. [Copyright &y& Elsevier]

Abstract: This paper proposes an approach of measuring a technology university’s knowledge management (KM) performance from competitive perspective. The approach integrates analytical network process (ANP), which is a theory of multiple criteria decision-making and is good at dealing with tangible and intangible information, with balanced scorecard (BSC) that contains four perspectives, including customer perspective, internal business perspective, innovation and learning perspective, and financial perspective, being adopted as the indicators of KM performance measurement (KMPM). This paper makes three important contributions: (1) it propose a methodology of comparing an organization’s knowledge management performance with its major rivals to offer effective information for improving KM, increasing decision-making quality, and obtaining clear effort direction of attaining competitive advantage; (2) it explores the case involving a lot of findings that present the positions of the case organization against it major rivals and imply that the technology university has to reinforce knowledge creation and accumulation to catch up with its competitive rivals; and (3) it is generic in nature and applicable to benefit an organization. The results prove the proposed method can act as a measurement tool for the entire KM of an organization. [Copyright &y& Elsevier]

Abstract: Difference detection is actual and extremely useful for evaluating a new medicine B against a specified disease by comparing to an old medicine A, which has been used to treat the disease for many years. The datasets generated by applying A and B to the disease are called contrast groups and, main differences between the groups are the mean and distribution differences, referred to structural differences in this paper. However, contrast groups are only two samples obtained by limited applications or tests on A and B, and may be with missing values. Therefore, the differences derived from the groups are inevitably uncertain. In this paper, we propose a statistically sound approach for measuring this uncertainty by identifying the confidence intervals of structural differences between contrast groups. This method is designed significantly against most of those applications whose exact data distributions are unknown a priori, and the data may also be with missing values. We apply our approach to UCI datasets to illustrate its power as a new data mining technique for, such as, distinguishing spam from non-spam emails; and the benign breast cancer from the malign one. [Copyright &y& Elsevier]

Abstract: Knowledge management capability (KMC) is the source for organizations to gain the sustainable competitive advantage. KMC evaluation is a required work with strategic significance. However it still has not been addressed in the existing literatures. So the objective of this study is to investigate a fuzzy multiple attributes decision-making method (FMADM) for evaluating KMC. In this paper, a framework for evaluating KMC is presented, which includes two parts, one is an evaluation hierarchy with attributes, the other a judgment matrix model with two dimensions to identify the evaluation results of KMC. Then, a fuzzy linguistic approach is proposed to evaluate the KMC of organizations. The evaluation results of KMC obtained through the proposed approach are objective and unbiased due to two reasons. Firstly, the results are generated by a group of experts in the presence of motile attributes. Secondly, the fuzzy linguistic approach employed in this paper has more advantage to reduce distortion and losing of information than other fuzzy linguistic approaches. Through evaluation result of KMC, managers could judge the necessity to improve the KMC and determine which dimension of KMC is the most needed direction to improve. Additionally, an example is used to illustrate the availability of the proposed method. [Copyright &y& Elsevier]

Abstract: Many enterprises have been devoting a significant portion of their budget to new product development (NPD) in order to distinguish their products from those of their competitors, and to make them better fit the needs and wants of customers. Hence, businesses should develop products that fulfill the customer demands, since this will increase the enterprise’s competitiveness and it is an essential criterion to earning higher loyalties and profits. This paper presents the product map obtained from data mining results, which investigates the relationships among customer demands, product characteristics, and transaction records, using the Apriori algorithm as a methodology of association rules for data mining. The product map shows that different knowledge patterns and rules can be extracted from customers to develop new cosmetic products and possible marketing solutions. Accordingly, this paper suggests that the cosmetics industry should extract customer knowledge from the demand side and use this as a knowledge resource on its supply chain for new product development. [Copyright &y& Elsevier]

Abstract: In data mining applications, it is important to develop evaluation methods for selecting quality and profitable rules. This paper utilizes a non-parametric approach, Data Envelopment Analysis (DEA), to estimate and rank the efficiency of association rules with multiple criteria. The interestingness of association rules is conventionally measured based on support and confidence. For specific applications, domain knowledge can be further designed as measures to evaluate the discovered rules. For example, in market basket analysis, the product value and cross-selling profit associated with the association rule can serve as essential measures to rule interestingness. In this paper, these domain measures are also included in the rule ranking procedure for selecting valuable rules for implementation. An example of market basket analysis is applied to illustrate the DEA based methodology for measuring the efficiency of association rules with multiple criteria. [Copyright &y& Elsevier]

This paper surveys knowledge management (KM) development using a literature review and classification of articles from 1995 to 2002 with keyword index in order to explore how KM technologies and applications have developed in this period. Based on the scope of 234 articles of knowledge management applications, this paper surveys and classifies KM technologies using the seven categories as: KM framework, knowledge-based systems, data mining, information and communication technology, artificial intelligence/expert systems, database technology, and modeling, together with their applications for different research and problem domains. Some discussion is presented, indicating future development for knowledge management technologies and applications as the followings: (1) KM technologies tend to develop towards expert orientation, and KM applications development is a problem-oriented domain. (2) Different social studies methodologies, such as statistical method, are suggested to implement in KM as another kind of technology. (3) Integration of qualitative and quantitative methods, and integration of KM technologies studies may broaden our horizon on this subject. (4) The ability to continually change and obtain new understanding is the power of KM technologies and will be the application of future works. [Copyright &y& Elsevier]

Expert and intelligent systems understand the underlying information behind the data by relying on a wide range of machine learning techniques. The interpretation of machine learning models is often the key to success in research areas such as business, finance, medical and health science, and bioinformatics; such research areas demand human understanding of the obtained model. The logistic model tree (LMT) algorithm is a popular classification method that combines a decision tree and logistic regression models. The combination of two complementary algorithms produces an accurate and interpretable classifier by combining the advantages of both logistic regression and tree induction. However, LMT has the disadvantage of high computational cost, which makes the algorithm undesirable in practice. In this paper, we propose an efficient method to learn the logistic regression models in the tree. We employ least angle regression to update the regression model in LogitBoost so that the algorithm efficiently learns sparse logistic regression models composed of relevant input variables. We compare the performance of our proposed method with the original LMT algorithm using 14 benchmark datasets and show that the training time dramatically decreases while the accuracy is preserved. Our proposed algorithm is not only accurate and intuitively interpretable but also computationally efficient. It helps users in making the best possible use of the data that are included in expert and intelligent systems. [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• Gamification can empower and cater to a specific type of knowledge workers. • Transaction model of knowledge work becomes more collaborative with gamification. • Enacted information model of knowledge work becomes more creative with gamification. • Gamification can provide more autonomy and flexibility to knowledge workers. • Gamification can help to reveal new knowledge worker roles. The increasingly popular trend of gamification has proved powerful in many areas, such as education and marketing, and has started making its way to the corporate environment. This exploratory study is focused on a particular part of corporate applications – using gamification to empower knowledge workers and to help them to interact with each other. Based on a review of the extant literature and an exploratory case study, we conceptualise different ways in which gamification supports knowledge workers and influences the dynamics of their interactions. The case study we present is that of online retailer Zappos who have been pioneers in this field. This paper is intended as the beginning of a journey towards utilising gamification in various aspects of knowledge work. Through studying the Zappos case, we draw out key learning points that can be used by other organisations in their journey to use gamification to empower knowledge workers. The paper also identifies areas for further research relevant to expert and intelligent systems, including the potential for synergies between gamification and intelligent systems, and the use of gamification in intelligent systems implementation. [ABSTRACT FROM AUTHOR]

• Equipment is developed by utilizing negation and entropy concepts. • The equipment allows the learning organization to refine existing knowledge. • The equipment helps the learning organization in the decision-making process. • The equipment is validated and tested on two well-known organizational cases. The present paper aims to put forward negation and entropy as novel perspectives for knowledge management in learning organizations. The present work considers the surrounding factors of information to enumerate the effectiveness of information in the knowledge management field. Moreover, by utilizing the effectiveness of surrounding factors, a framework inspired by Dempster-Shafer theory has been presented to generate negation. Finally, the present paper defines and calculates entropy for information. We have applied negation and entropy concepts to two well-known cases of organizational systems and management domains to study their impact on the knowledge management domain. The cases have supported our decisions taken after the calculation of proposed negation and entropy concepts. The proposed method has a quality interpretation of the information operators and has the merit of simplifying knowledge management and decision making problems in the context of a learning organization. The present study combines multiple aspects of knowledge management, organizational systems, decision making, and organizational learning. [ABSTRACT FROM AUTHOR]

In Data Mining, during the preprocessing step, there is a considerable diversity of candidate algorithms to select important features, according to some criteria. This broad availability of algorithms that perform the Feature Selection task gives rise to the difficulty of choosing, a priori , between the algorithms at hand, the most promising one for a particular problem. In this paper, we present the proposal and evaluation of a new architecture for the recommendation of Feature Selection algorithms based on the use of Metalearning. Our framework is very flexible since the user can adapt it to its proper needs. This flexibility is one of the main advantages of our proposal over other approaches in the literature, which involve steps that cannot be adapted to the user’s local requirements. Furthermore, it combines several concepts of intelligent systems, including Machine Learning and Data Mining, with topics derived from expert systems, as user and data-driven knowledge, with meta-knowledge. This set of solutions coupled with leading-edge technologies allows our architecture to be integrated into any information system, which impact on the automation of services and in reducing human effort during the process. Regarding the Metalearning process, our framework considers several types of properties inherent to the data sets, as well as, Feature Selection algorithms based on many information, distance, dependence and consistency measures. The quality of the methods for Feature Selection was estimated according to a multicriteria performance measure, which guided the ranking process of these algorithms for the construction of data metabases. Proposed by the authors of this work, this multicriteria performance measure combines any three measurements on a single one, creating an interesting and powerful tool to evaluate not only FS algorithms but also to assess any context where it is necessary a combination to maximize a measure or minimize it. The recommendation models, represented by decision trees and induced from the training metabases, allowed us to see in what circumstances a Feature Selection algorithm outperforms the other and what aspects of the data present greater influence in determining the performance of these algorithms. Nevertheless, if the user wishes, any other learning algorithm may be used to induce the recommendation model. This versatility is another strong point of this proposal. Results show that with the characterization of data, through statistical, information and complexity measures, it is possible to reach an accuracy higher than 90%. Besides yielding recommendation models that are interpretable and robust to overfitting, the developed architecture is less computationally expensive than approaches recently proposed in the literature. [ABSTRACT FROM AUTHOR]

The volume of text data has been growing exponentially in the last years, mainly due to the Internet. Automatic Text Summarization has emerged as an alternative to help users find relevant information in the content of one or more documents. This paper presents a comparative analysis of eighteen shallow sentence scoring techniques to compute the importance of a sentence in the context of extractive single- and multi-document summarization. Several experiments were made to assess the performance of such techniques individually and applying different combination strategies. The most traditional benchmark on the news domain demonstrates the feasibility of combining such techniques, in most cases outperforming the results obtained by isolated techniques. Combinations that perform competitively with the state-of-the-art systems were found. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Tender analysis processes are everyday processes in any company, but they become even more important when the financial resources are limited. Achieving optimal and transparent tender analysis processes requires hard work from everyone involved. This paper proposes a linguistic multi-criteria decision-making model which helps decision-makers in this task by automating it. To do that, the proposal requires diverse experts to assess different criteria and establish their corresponding weights. The processing of all these input data will determine a minimum value, called profile , to consider an alternative as valid. Additionally, assessments of a similar (probably external) process will be taken into account, becoming a reference valuation. Unlike other decision-making models, the use of this last value gives an idea of the goodness of the result depending on whether the solution obtained is close to the reference value. To the best of our knowledge, there is no expert or intelligent system specifically designed to fully meet the needs of tender analysis processes. For both input and output process stages carried out in our proposal, 2-tuple linguistic labels have been used. These linguistic labels were chosen to facilitate decision-making for the staff involved in the process, as well as for being the most suitable communication way used by human beings. To validate the model, we apply it to a case study in the ICT (Information and Communications Technology) sector. In addition, we include a literature review related to applications using the 2-tuple representation, as well as a comparison of our proposal with related methods, including the results obtained by these methods for the case study presented. [ABSTRACT FROM AUTHOR]

In an evolving competitive environment, characterized by an increasing competition and a rapid market change, companies strive to reach their competitive advantage by monitoring and processing the information related to such environment. Getting better informational and intelligence support is consequently critical and vital. Competitive Intelligence (CI) appears then as a vital component of strategic planning and management process within organization. It has attracted increasing attention over the past two decades and has become a challenge for business professionals and begun to be studied and implemented in large and small companies, in the private and public sectors, and within different industrial contexts. Interest towards CI is observed as well among academic researchers. Existing literature reveals that CI is a multi-disciplinary concept studied by researchers with different fields of expertise (management, marketing, information technology, decision support, and knowledge management) and addressed from different points of view (as concept, product, process, practice/discipline, method, and as a system). Despite this great diversity in the body of knowledge related to the CI, there is no empirical study that touched the practical aspect of the problem by developing a complete CI solution that can be delivered to the decision maker in terms of anticipating competitors' reactions. Existing research works focus more on the process, its steps, objectives and benefits for the company. Our paper aims to address this gap and present the first practical end-to-end CI solution based on rough set theory to anticipate competitor's decisions. To achieve this objective, several theoretical contributions were reached. Our proposed approach consists of four phases: (i) actions collection; (ii) a two-step actions association in which first we cluster actions using k-modes algorithm then we proceed to associate the competitive actions using our new proposed algorithm for action association,; (iii) rules generation using rough set theory with a modified version of LEM2 algorithm which can deal with inexact data and derive rules from it; and finally (iv) rules aggregation using a new proposed algorithm. As motivation for the research, we use a real case study in telecommunications to illustrate our whole proposed approach and show its efficiency. • The interest of anticipating competitors' decisions in a context of uncertainty. • Presenting the first practical end-to-end competitive intelligence solution. • Approach validation with a real case study in a highly competitive field. • Generating meaningful rules that contain relevant information. • Conducting several comparisons to prove the efficiency of our approach. [ABSTRACT FROM AUTHOR]

The Live Web is characterised by a new way of interacting with the Web through dynamic streams of relevant real-time contextual information to users. These sources of massive data usually overwhelm them, because they are not able to consume that amount of data. Task Automation Services (TASs) are platforms or apps that allow their users to author automation rules to combine events from streams while reducing the effort for handling incoming information. While these platforms are a reality, they suffer from two major drawbacks: (i) the only incoming data streams available are those the TASs developers decided to include in the system, and (ii) they lack of a mechanism to reason over large scale data outside their platform. To face these challenges, this paper contributes in (i) reviewing the existing state of the art including research and commercial work given their relevance. Based on the lessons learnt from this review, (ii) we propose the Evented WEb ontology (EWE), that models the Evented WEb domain, and in particular those concepts around TASs. EWE enables scalability, interoperability and definition of rules with reasoning over Linked Open Data (LOD) cloud. To illustrate these contributions, (iii) a semantic TAS has been implemented that benefits from the advantages EWE offers, and solves a realistic problem using semantic technologies. Finally, (iv) to validate the ontology covers the domain it models, a thorough ontology evaluation is presented. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Highlights: [•] This paper argues the need for economics knowledge sharing. [•] We demonstrate how economics knowledge sharing can be effectively achieved. [•] Three search functions are implemented based on OEKSS. [•] We propose instrumental variable recommendation algorithm (IVRA). [•] IVRA provides a systematic way to find instrumental variables in OEKSS. [Copyright &y& Elsevier]

Abstract: Despite the importance of decision-making (DM) techniques for construction of effective decision models for supplier selection, there is a lack of a systematic literature review for it. This paper provides a systematic literature review on articles published from 2008 to 2012 on the application of DM techniques for supplier selection. By using a methodological decision analysis in four aspects including decision problems, decision makers, decision environments, and decision approaches, we finally selected and reviewed 123 journal articles. To examine the research trend on uncertain supplier selection, these articles are roughly classified into seven categories according to different uncertainties. Under such classification framework, 26 DM techniques are identified from three perspectives: (1) Multicriteria decision making (MCDM) techniques, (2) Mathematical programming (MP) techniques, and (3) Artificial intelligence (AI) techniques. We reviewed each of the 26 techniques and analyzed the means of integrating these techniques for supplier selection. Our survey provides the recommendation for future research and facilitates knowledge accumulation and creation concerning the application of DM techniques in supplier selection. [Copyright &y& Elsevier]

Abstract: In today’s competitive business environment, Intellectual Capital (IC) management is ever more recognized as a fundamental factor in gaining competitive advantage. Actually, most firms have only a vague idea of how to manage investments in IC and what they should obtain from these investments. As a result, many companies overlook to balance IC investments, overinvesting in some IC components and neglecting other ones. Following this lead, the aim of the paper is to assess the relative importance of IC components, with respect to their contribution to the company value creation, in order to obtain guidelines for IC management and investments. We propose a model for IC evaluation by integrating Fuzzy Logic and Analytic Hierarchy Process (AHP). This Fuzzy AHP approach allows to capture and foster IC dynamics: experts and managers are greatly supported by the use of linguistic variables in the evaluation process of the company intangible assets. Finally, the application of the Fuzzy AHP methodology to a group of ICT service companies is presented. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: Trademarks are distinctive visual symbols with high reputational value, due to the perception of quality and innovation associated with them. They are important reputational assets used as a marketing tool to convey a certain assurance of quality, innovation, and the standards, which the manufacturer seeks to maintain. This motivates the need for trademark protection by providing a solution to prevent infringement. This problem can be addressed by developing retrieval systems capable of comparing the visual similarity of trademarks. This paper contributes to the research in this field by proposing an innovative trademark retrieval technique with improved retrieval performance due to the integration of global and local descriptors. The global descriptor employed is the Zernike moment’s coefficients. The local descriptor is the edge-gradient co-occurrence matrix, derived from the contour information that is considered very important in human perception of visual similarity. The proposed retrieval technique is tested using the standard MPEG-7 shape database of 1400 images and the MPEG-7 trademark database of 3260 images. The results show 5% precision/recall improvement in the case of the MPEG-7 shape database, as well as 2.35% Bull’s eye score improvement and 19.8% NMRR score improvement for the 10 randomly selected trademarks from the MPEG-7 trademarks database. [Copyright &y& Elsevier]

Abstract: The concept of Communities of Practice (CoPs) has been highlighted as an effective method for knowledge sharing in Knowledge Management (KM) and strategically utilized by many organizations. Therefore, the need to diagnose knowledge sharing activities in CoPs has increased. Previous research on CoP strategies has generally suggested broad guidelines without diagnosing the current knowledge sharing status of individual CoPs. Furthermore, diagnosis methodologies are not connected to strategic direction and require too much time and effort to conduct regularly. The purpose of this paper is to develop a sustainable diagnosis framework for identifying knowledge sharing activities in CoPs using Social Network Analysis (SNA) and to suggest strategies for individual CoPs based on the proposed diagnosis framework. Finally, we apply the proposed diagnosis framework to an industry case. [Copyright &y& Elsevier]

Abstract: Emergency management has drawn the attention of governments worldwide. It is an emerging realm for governments and organizations to respond to sudden catastrophic events rapidly and effectively during a crisis, e.g. social unrest, natural disaster, unforeseen event, and so on. There is a lot of information and knowledge involved in emergency management. The key question is how to search for the appropriate information and knowledge quickly and accurately from the massive information and knowledge based on the nature, characteristics, status and situations of the unexpected emergency in order to support the intelligent decision-making process in handling emergencies. In this paper, an unstructured information management system (UIMS) is presented for emergency management. A concept relationship model (CRM) and a dynamic knowledge flow model (DKFM) of the emergency incidence have been built to organize and represent emergency knowledge. The models can support decision-makers to have better understanding of the dependence and degree of correlation between different concepts about the emergency in order to make the appropriate decisions. The system is trial implemented in a city emergency management system and its performance is evaluated and discussed. [Copyright &y& Elsevier]

Abstract: Concept map is a graphical tool for representing knowledge. They have been used in many different areas, including education, knowledge management, business and intelligence. Constructing of concept maps manually can be a complex task; an unskilled person may encounter difficulties in determining and positioning concepts relevant to the problem area. An application that recommends concept candidates and their position in a concept map can significantly help the user in that situation. This paper gives an overview of different approaches to automatic and semi-automatic creation of concept maps from textual and non-textual sources. The concept map mining process is defined, and one method suitable for the creation of concept maps from unstructured textual sources in highly inflected languages such as the Croatian language is described in detail. Proposed method uses statistical and data mining techniques enriched with linguistic tools. With minor adjustments, that method can also be used for concept map mining from textual sources in other morphologically rich languages. [Copyright &y& Elsevier]

Abstract: Personal knowledge management (PKM) is different from the traditional centralized knowledge management (KM) modes. The PKM is suitable for distributed collaborative KM environments. This paper makes an explorative study on the PKM, and analyzes various forms of personal knowledge resources in the product development process. Then a model of recommender systems for PKM is proposed for knowledge sharing among members in the collaborative environment. The key function of the PKM recommender systems is to supply potentially useful personal knowledge resources from the sites where these knowledge resources are created to the sites where the members may need the knowledge. The PKM is in a mode of distributed control rather than a mode of centralized control, which is widely used by traditional KM methods and tools. This study paves a way for developing an advanced mode of KM platforms for PKM sharing in collaborative environments. [Copyright &y& Elsevier]

Abstract: Many enterprises collect Frequently Asked Questions (FAQs) from their customer service departments or technical support centers and compile the questions and responses in a knowledge database to reuse the information when similar questions occur. However, the categorization of these FAQs is based on human judgment, which can result in inconsistencies. In this paper, an auto-categorizing system is designed and proposed to facilitate a more precise categorization of software-based FAQs for the case subject, S Company. This study applies the system development methodology. First, the domain ontology of an FAQ auto-categorizing system is devised, and categorizing methods and rules are developed with the intention that the system will generate categories automatically. Next, the FAQ auto-categorizing system is tested using a database based on the real FAQs of the study subject Company S, and the system reliability and FAQ searching performance are verified via interviews with the customer service staff and user satisfaction surveys. This study demonstrates that the FAQ auto-categorizing system can automatically generate categories that match the original FAQ attributes, but manual adjustment is still occasionally necessary to optimize the system and boost the accuracy of the categorization. The interviews and user satisfaction surveys show that categorizing according to question attributes is a more efficient method than the method used by the software products available for FAQ search performance. Although users are satisfied with both the FAQ auto-categorizing system and the original FAQ system used by subject Company S, this study discovers that the FAQ auto-categorizing system requires significantly less search time than the original system. [Copyright &y& Elsevier]

Abstract: Ontology creation and management related processes are very important to define and develop semantic services. Ontology Engineering is the research field that provides the mechanisms to manage the life cycle of the ontologies. However, the process of building ontologies can be tedious and sometimes exhaustive. OWL-VisMod is a tool designed for developing ontological engineering based on visual analytics conceptual modeling for OWL ontologies life cycle management, supporting both creation and understanding tasks. This paper is devoted to evaluate OWL-VisMod through a set of defined tasks. The same tasks also will be done with the most known tool in Ontology Engineering, Protégé, in order to compare the obtained results and be able to know how is OWL-VisMod perceived for the expert users. The comparison shows that both tools have similar acceptation scores, but OWL-VisMod presents better feelings regarding user’s perception tasks due to the visual analytics influence. [Copyright &y& Elsevier]

Abstract: Data Mining projects are implemented by following the knowledge discovery process. This process is highly complex and iterative in nature and comprises of several phases, starting off with business understanding, and followed by data understanding, data preparation, modeling, evaluation and deployment or implementation. Each phase comprises of several tasks. Knowledge Discovery and Data Mining (KDDM) process models are meant to provide prescriptive guidance towards the execution of the end-to-end knowledge discovery process, i.e. such models prescribe how exactly each one of the tasks in a Data Mining project can be implemented. Given this role, the quality of the process model used, affects the effectiveness and efficiency with which the knowledge discovery process can be implemented and therefore the outcome of the overall Data Mining project. This paper presents the results of the rigorous evaluation of the Integrated Knowledge Discovery and Data Mining (IKDDM) process model and compares it to the CRISP-DM process model. Results of statistical tests confirm that the IKDDM leads to more effective and efficient implementation of the knowledge discovery process. [Copyright &y& Elsevier]

Abstract: The paper targets to devise a genuine Knowledge Management (KM) performance measurement model in a stochastic setting based on Data Envelopment Analysis (DEA), Monte Carlo simulation and Genetic Algorithm (GA). The proposed model evaluates KM using a set of proxy measures correlated with the major KM processes. Data Collection Budget Allocation (DCBA) that maximizes the model accuracy is determined using GA. Additional data are generated and analyzed using a Monte-Carlo-enhanced DEA model to obtain the overall KM efficiency and KM processes’ efficiency scores. An application of the model has been carried out to evaluate KM performance in higher educational institutions. It is found that with GA, the accuracy of the model has been greatly improved. Lastly, comparing with a conventional deterministic DEA model, the results from the proposed model would be more useful for managers to determine future strategies to improve their KM. [Copyright &y& Elsevier]