Search

As one of mature theories, formal concept analysis (FCA) possesses remarkable mathematical properties, but it may generate massive concepts and complicated lattice structure when dealing with large-scale data. With a view to the fact that granular computing (GrC) can significantly lower the difficulty by selecting larger and appropriate granulations when processing large-scale data or solving complicated problems, the paper introduces GrC into FCA, it not only helps to expand the extent and intent of classical concept, but also can effectively reduce the time complexity and space complexity of FCA in knowledge acquisition to some degree. In modeling, concept-base, as a kind of low-level knowledge, plays an important role in the whole process of information granularity. Based on concept-base, attribute granules, object granules and relation granules in formal contexts are studied. Meanwhile, supremum and infimum operations are introduced in the precess of information granularity, whose biggest distinction from traditional models is integrating the structural information of concept lattice. In addition, the paper also probes into reduction, core, and implication rules in granularity formal contexts. Theories and examples verify the reasonability and effectiveness of the conclusions drawn in the paper. In short, the paper not only can be viewed as an effective means for the expansion of FCA, but also is an attempt for the fusion study of the two theories. [ABSTRACT FROM AUTHOR]

Abstract To acquire knowledge of a research area, a researcher needs to understand many papers and their interrelations. The existing reference management systems help the researcher maintain and use a database of research papers, but do little to help understand the research area as a whole. The paper presents LiteRef , a novel reference management system that addresses this need by effectively integrating tools for (1) organizing knowledge about both individual papers and connections between the papers, (2) visualizing whole research directions/areas and (3) producing well-formed surveys to share this knowledge in a variety of formats. After describing LiteRef's design and features, the paper shows an example of how LiteRef helps further the author's own research. Extensive documentation with a video demo for each major feature supplements the paper. [ABSTRACT FROM AUTHOR]

While most dental clinicians use some sort of information system, they are involved with administrative functions, despite the advisory potential of some of these systems. This paper outlines some current decision support systems (DSS) and the common barriers facing dentists in adopting them within their workflow. These barriers include lack of perceived usefulness, complicated social and economic factors, and the difficulty for users to interpret the advice given by the system. A survey of current systems found that although there are systems that suggest treatment options, there is no real-time integration with other knowledge bases. Additionally, advice on drug prescription at point-of-care is absent from such systems, which is a significant omission, in consideration of the fact that disease management and drug prescription are common in the workflow of a dentist. This paper also addresses future trends in the research and development of dental clinical DSS, with specific emphasis on big data, standards and privacy issues to fulfil the vision of a robust, user-friendly and scalable personalised DSS for dentists. The findings of this study will offer strategies in design, research and development of a DSS with sufficient perceived usefulness to attract adoption and integration by dentists within their routine clinical workflow, thus resulting in better health outcomes for patients and increased productivity for the clinic. [ABSTRACT FROM AUTHOR]

This paper addresses a novel learning problem where data from multiple domains are generated from distributions that are allowed to be arbitrarily different. Domain adaptation and transfer learning have been addressed previously for two domains with different distributions, but based on the assumption that the distributions are sufficiently related to each other. However, in many real-world applications, multiple domains may have arbitrarily different distributions. In this paper, a general framework is proposed, called Multi-ATL, for bridging knowledge from multiple domains with arbitrarily different distributions. The proposed framework consists of three key components: latent feature space extraction, co-training with multiple views, and active sample selection. The basic concept is to explore each domain and characterize it with an extracted latent feature space, and then apply a co-training algorithm to the latent spaces to use multiple views simultaneously for training. In the co-training process, active sample selection is used to determine how much knowledge should be transferred from a source domain to the target domain. Experimental results on one synthetic and two real-world datasets show that the proposed framework significantly improves classification accuracy (13.5% on the synthetic and +17.79%, +23.87% on the real datasets) with only a few (2–5) active selections. [ABSTRACT FROM AUTHOR]

To obtain or even increase the expected benefits of using microservices architecture (MSA), it is necessary to solve a series of challenges encountered in the process of applying MSA. To properly assign group roles to build a development team that matches the MSA is one of the important challenges. This paper combines the group role assignment (GRA) algorithm and E-CARGO model to solve group role assignment in MSA (GRAMSA) and proposes three basic strategies for evaluating the created MSA team, including performance first, positivity first, and communication costs first. Furthermore, based on the combination of three basic strategies, two optimized role assignment strategies are proposed, namely, GRA based on agent positivity and GRA that maximizes team performance under location restrictions. The strategies mentioned in the paper provide companies using MSA with different team evaluation angles, and at the same time help them build the most suitable development team for different application scenarios. Finally, the proposed strategies are discussed through case studies, and the application scenarios and generalizability of the strategies are analyzed. [ABSTRACT FROM AUTHOR]

Enterprises are focusing more and more on knowledge issues for global product development. This paper describes knowledge evolution processes in product development activities and proposes a knowledge evaluation method in product lifecycle design. The paper also theoretically analyzes the evaluation model and illustrates how knowledge values can be assessed by case study. The case study shows how knowledge values calculated by the model can provide suggestions about which knowledge to choose and what to do next. The knowledge evaluation model serves as a useful tool for managing knowledge in product lifecycle design and support. [ABSTRACT FROM AUTHOR]

Engineering projects are organizations where several actors with different professional fields and know-how work together to carry out the same aim: to develop a new product. Inside these organizations, heterogeneous and distributed information has to be managed in order to create project memories that will be useful in future projects. In this paper we describe a Multi-Agent System (MAS), which is based on the social and cooperative approach to support the knowledge management process all along mechanical design projects. Indeed, this multi-agent system, called KATRAS, aims to capitalize and reuse knowledge according to the roles involved in the design projects. We will present in this paper how the agents capitalize six different types of knowledge (professional vocabulary, process, expertise, project evolution, and return of experience) and how they help the professional actors to reuse knowledge. [ABSTRACT FROM AUTHOR]

This paper explores what kinds of management actions are needed by businesses to enhance their innovation capabilities. The first step is to clarify the differences between information and knowledge. To do this, the author introduces a model that can explain an individual's mental processes in knowledge acquisition and creation. With this model, it becomes explainable in a comprehensive way how "explicit" knowledge received as information is turned into individual knowledge; how "tacit" knowledge can be successfully transferred between workers; and how new knowledge can be created by individuals. The model assumes that knowledge workers can be classified into two categories, i.e., Type-1 and Type-2. A Type-1 knowledge worker is one whose knowledge acquisition depends almost exclusively on learning. A Type-2 worker is one who has a substantial amount of self-created knowledge in addition to learned knowledge. It is quite common to find Type-1 workers, but there are not that many Type-2 workers. Successful business firms are usually led by Type-2 workers, who are more innovative. In order to enhance the innovation capabilities of business firms, rather than waiting for the fortuitous advent of Type-2 workers, management should make an effort to transform existing Type-1 workers into Type-2 workers. The author makes the assertion that such a transformation is possible by putting Type-1 knowledge workers into situations where their "insight for knowledge creation" is constantly stimulated. Constant stimulation is made possible by using an IT system based on the Timed-PDCA concept that was proposed by the author in his previous papers. When this system is deployed seriously by management, it becomes possible to facilitate workers' breakthrough efforts and to promote close collaboration among workers through information sharing and visualization. [ABSTRACT FROM AUTHOR]

This paper presents the development of a knowledge-based system (KBS) prototype able to design natural gas cogeneration plants, demonstrating new features for this field. The design of such power plants represents a synthesis problem, subject to thermodynamic constraints that include the location and sizing of components. The project was developed in partnership with the major Brazilian gas and oil company, and involved interaction with an external consultant as well as an interdisciplinary team. The paper focuses on validation and lessons learned, concentrating on important aspects such as the generation of alternative configuration schemes, breadth of each scheme description created by the system, and its module to support economic feasibility analysis. [ABSTRACT FROM AUTHOR]

This work presents a novel automated approach to construct topic knowledge maps with knowledge structures, followed by its application to an internationally renowned journal. Knowledge structures are diagrams showing the important components of knowledge in study. Knowledge maps identify the locations of objects and illustrate the relationship among objects. In our study, the important components derived from knowledge structures are used as objects to be spotted in a topic knowledge map. The purpose of our knowledge structures is to find out the major topics serving as subjects of article collections as well as related methods employed in the published papers. The purpose of topic knowledge maps is to transform high-dimensional objects (topic, paper, and cited frequency) into a 2-dimensional space to help understand complicated relatedness among high-dimensional objects, such as the related degree between an article and a topic. First, we adopt independent chi-square test to examine the independence of topics and apply genetic algorithm to choose topics selection with best fitness value to construct knowledge structures. Additionally, high-dimensional relationships among objects are transformed into a 2-dimensional space using the multi-dimension scaling method. The optimal transformation coordinate matrix is also determined by using a genetic algorithm to preserve the original relations among objects and construct appropriate topic knowledge maps. [ABSTRACT FROM AUTHOR]

Business process analysis is a key activity that aims at increasing the efficiency of business operations. In recent years, several data mining based methods have been designed for discovering interesting patterns in event logs. A popular type of methods consists of applying frequent itemset mining to extract patterns indicating how resources and activities are frequently used. Although these methods are useful, they have two important limitations. First, these methods are designed to be applied to original event logs. Because these methods do not consider other perspectives on the data that could be obtained by applying data transformations, many patterns are missed that may represent important information for businesses. Second, these methods can generate a large number of patterns since they only consider the minimum support as constraint to select patterns. But analyzing a large number of patterns is time-consuming for users, and many irrelevant patterns may be found. To address these issues, this paper presents an improved event log analysis approach named AllMining. It includes a novel pre-processing method to construct multiple types of transaction databases from a same original event log using transformations. This allows to extract many new useful types of patterns from event logs with frequent itemset mining techniques. To address the second issue, a pruning strategy is further developed based on a novel concept of pattern coverage, to present a small set of patterns that covers many events to decision makers. Results of experiments on real-life event logs show that the proposed approach is promising compared to existing frequent itemset mining approaches and state-of-the-art process model algorithms. [ABSTRACT FROM AUTHOR]

The efficient solution to justify train movement safety is to analyze train movement situations via train operation knowledge and knowledge-based inference tools. In this paper, train operation knowledge is represented as train movement models and conditions, collectively called rule-based train movement reference models. The Dempster–Shafer (D–S) evidence theory is employed to infer the model and condition under which a train is running. Consequently, aberrant models and conditions, potentially endangering train operation safety, are identified in advance so that emergency measures can be taken to prevent train operation accidents. The mass function is defined as the approximation level of the train operation time interval within one block section of a railway line to that obtained from various reference models. The D–S theory is also applied to train movement dynamic processes to gradually identify train operation situations, using the combined section and process mass functions. The proposed inference approach using evidence theory and reference models (ETRM) qualitatively and quantitatively judges the rationalities of train operation control logic and variation tendencies. A case study to prevent the occurrence of the 7/23 railway accident in China demonstrates the validity of the proposed inference approach using ETRM. The analysis and inference centering on train movement situations can meanwhile diagnose the operation status of train onboard and ground control systems. [ABSTRACT FROM AUTHOR]

The Semantic Web opens up new opportunities for the data mining research. Semantic Web data is usually represented in the RDF triple format (subject, predicate, object). Large RDF-style Knowledge Bases contain hundreds of millions of RDF triples that represent knowledge in a machine-understandable format. Association rule mining is one of the most effective techniques for detecting frequent patterns. In the context of Semantic Web data mining, most existing methods rely on users intervention that is time-consuming and error-prone due to a large amount of data. Meanwhile, rule quality factors (e.g. support and confidence) usually consider knowledge at the instance-level. Namely, these factors disregard the knowledge embedded at the schema-level. In this paper, we demonstrate that ignoring knowledge encoded at the schema-level negatively impacts the interpretation of discovered rules. We introduce an approach called SWARM (Semantic Web Association Rule Mining) that automatically mines Semantic Association Rules from RDF data. The main achievement of SWARM is to reveal common behavioural patterns associated with knowledge at the instance-level and schema-level. We discuss how to utilize knowledge encoded at the schema-level to add more semantics to the rules. We compare the semantic of rules discovered by SWRAM with one of the latest approaches in this field to show the importance of considering schema-level knowledge. Initial experiments performed on RDF-style Knowledge Bases demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Knowledge bases (KBs) such as Freebase and Yago are rather incomplete, and the situation is more serious in non-English KBs, such as Chinese KBs. In this paper, we present a language-independent framework to tackle the slot-filling task by searching the Web with high-precision queries, and deriving lightweight extraction patterns. The patterns are based on string matching, and since they make no use of complex NLP resources, which may be unavailable in some languages, they are very language-independent. We use a traditional bootstrapping approach for extraction, but also use a novel approach to suppress the noise associated with distant supervision: in particular, we use a pseudo-testing method to validate the patterns derived from different sentences. Experiments show that our framework achieves very encouraging results. [ABSTRACT FROM AUTHOR]

Abstract: The purpose of this paper is to apply a fuzzy quality function deployment (QFD) approach to prioritize knowledge management (KM) solutions for an international port in Taiwan. First, the paper examines house of quality (HOQ) matrices to facilitate handling of the ‘what’ (i.e., KM requirements) and ‘how’ (KM solutions) aspects of the QFD problem, and proposes procedures for the use of a fuzzy QFD method. A case study concerning port K in Taiwan is then used to demonstrate a systematic appraisal process for prioritizing KM solutions, and twenty attributes with sixteen feasible KM implementation solutions are measured employing an HOQ matrix. Finally, the top five feasible solutions for implementing KM at port K are identified. The empirical results show that ‘establishment of a data storage and data mining system’ in the technology dimension is the most urgent requirement for KM implementation at port K in Taiwan. [Copyright &y& Elsevier]

In this paper we present a system that automatically builds ordered timelines of events from different written texts in English. The system deals with problems such as automatic event extraction, cross-document temporal relation extraction and cross-document event coreference resolution. Its main characteristic is the application of three different types of knowledge: temporal knowledge, lexical-semantic knowledge and distributional-semantic knowledge, in order to anchor and order the events in the timeline. It has been evaluated within the framework of SemEval 2015. The proposed system improves the current state-of-the-art systems in all measures (up to eight points of F1-score over other systems) and shows a significant advance in the Cross-document event ordering task. [ABSTRACT FROM AUTHOR]

In this paper, we present a forgetting-based approach to handling inconsistency in DL-Lite. Our proposed approach cannot only characterize distance-based reasoning, which is proven to rationally draw meaningful conclusions even from inconsistent DL-Lite knowledge bases but also recovery the consistency of DL-Lite knowledge bases. We first present vectors forgetting for DL-Lite by extending predicates forgetting in DL-Lite and show the predicates to be forgotten which are obtained by computing minimal hitting sets. Moreover, we develop algorithms to compute those predicates by employing Reiter’s HS-tree method and then analyze the computational complexity of those proposed algorithms. Finally, we implement our proposed algorithms and evaluate them on both consistent and inconsistent ontologies. Besides, we discuss some applications of vectors forgetting in privacy protection. [ABSTRACT FROM AUTHOR]

This paper covers a particular area of interest in pattern recognition and knowledge-based systems (PRKbS), being intended for both young researchers and academic professionals who are looking for a polished and refined material. Its aim, playing the role of a tutorial that introduces three feature extraction (FE) approaches based on zero-crossing rates (ZCRs), is to offer cutting-edge algorithms in which clarity and creativity are predominant. The theory, smoothly shown and accompanied by numerical examples, innovatively characterises ZCRs as being neurocomputing agents. Source-codes in C/C++ programming language and interesting applications on speech segmentation, image border extraction and biomedical signal analysis complement the text. [ABSTRACT FROM AUTHOR]

This paper offers a multi-disciplinary review of knowledge acquisition methods in human activity systems. The review captures the degree of involvement of various types of agencies in the knowledge acquisition process, and proposes a classification with three categories of methods: the human agent, the human-inspired agent, and the autonomous machine agent methods. In the first two categories, the acquisition of knowledge is seen as a cognitive task analysis exercise, while in the third category knowledge acquisition is treated as an autonomous knowledge-discovery endeavour. The motivation for this classification stems from the continuous change over time of the structure, meaning and purpose of human activity systems, which are seen as the factor that fuelled researchers’ and practitioners’ efforts in knowledge acquisition for more than a century. We show through this review that the KA field is increasingly active due to the higher and higher pace of change in human activity, and conclude by discussing the emergence of a fourth category of knowledge acquisition methods, which are based on red-teaming and co-evolution. [ABSTRACT FROM AUTHOR]

Abstract: In the real world it is a routine that one must deal with uncertainty when security is concerned. Intrusion detection systems offer a new challenge in handling uncertainty due to imprecise knowledge in classifying the normal or abnormal behaviour patterns. In this paper we have introduced an emerging approach for intrusion detection system using Neutrosophic Logic Classifier which is an extension/combination of the fuzzy logic, intuitionistic logic, paraconsistent logic, and the three-valued logics that use an indeterminate value. It is capable of handling fuzzy, vague, incomplete and inconsistent information under one framework. Using this new approach there is an increase in detection rate and the significant decrease in false alarm rate. The proposed method tripartitions the dataset into normal, abnormal and indeterministic based on the degree of membership of truthness, degree of membership of indeterminacy and degree of membership of falsity. The proposed method was tested up on KDD Cup 99 dataset. The Neutrosophic Logic Classifier generates the Neutrosophic rules to determine the intrusion in progress. Improvised genetic algorithm is adopted in order to detect the potential rules for performing better classification. This paper exhibits the efficiency of handling uncertainty in Intrusion detection precisely using Neutrosophic Logic Classifier based Intrusion detection System. [Copyright &y& Elsevier]

Abstract: Visualizing the entire domain of knowledge and tracking the latest developments of an important discipline are challenging tasks for researchers. This study builds an intellectual structure by examining a total of 10,974 publications in the knowledge management (KM) field from 1995 to 2010. Document co-citation analysis, pathfinder network and strategic diagram techniques are applied to provide a dynamic view of the evolution of knowledge management research trends. This study provides a systematic and objective means in exploring the development of the KM discipline. This paper not only drew its finding from a large data set but also presented a longitudinal analysis of the development of the KM related studies. The results of this study reflect that the coverage of key KM papers has expanded into a broad spectrum of disciplines. A discussion of the future of KM research is also provided. [Copyright &y& Elsevier]

Abstract: In recent years, product knowledge has played increasingly significant roles in new product development process especially in the development of One-of-a-Kind products. Although knowledge-based systems (KBSs) have been proposed to support product development activities and new knowledge modelling methodologies have been developed, they are still far from complete. This area has become attractive to many researchers and as a result, many new knowledge-based systems, methods and tools have been developed. However, to the best of our knowledge, knowledge-based systems for product development have not been systematically reviewed, compared and summarized. This paper provides a comprehensive review on the recent development of KBS, methods and tools in supporting rapid product development. In the paper, the relevant technologies for modelling, managing and representing knowledge are investigated and reviewed systematically for better understanding their characteristics. The focus is placed on knowledge-based systems that support product development, and how product knowledge is identified, captured, represented and reused during the processes of One-of-a-Kind product development. The limitations and the future trend of KBS are presented in terms of how they can help One-of-a-Kind Production (OKP) companies. [Copyright &y& Elsevier]

Abstract: Since knowledge and its strategic applications have now become a major source of competitive advantage for organizations, the lifeblood of organizations is to possess the capabilities for acquiring, creating, sharing, diffusing, utilizing, and storing knowledge among organizational members. Among the capabilities, knowledge creation is the major resource of organizational innovation and it, therefore, plays a more crucial role in developing a sustained competitive advantage for organizations, especially in a dynamic environment. Although numerous papers have studied the issue of knowledge creation, those papers focused on conceptualizing the process of knowledge creation and did not investigate the concrete problem of knowledge creation. In the current paper, a mathematical representation of knowledge creation based on an evolutionary perspective is used to understand: (1) if knowledge can be created; (2) how knowledge is created; (3) under what conditions knowledge can be created; (4) the partial effects of variables which affect the process of knowledge creation. [Copyright &y& Elsevier]

Abstract: The notion of knowledge artifact has rapidly gained popularity in the fields of general knowledge management and more recently knowledge-based systems. The main goal on this paper is to propose and discuss a methodology for the design and implementation of knowledge-based systems founded on knowledge artifacts. We advocate that the systems built according to this methodology can be effective to convey the flow of knowledge between different communities of practice. Our methodology has been developed from the ground up, i.e. we have built some concrete systems based on the abstract notion of knowledge artifact and synthesized our methodology based on reflections upon our experiences building these systems. In this paper, we also describe the most relevant systems we have built and how they have guided us to the synthesis of our proposed methodology. [Copyright &y& Elsevier]

Abstract: The problem treated in this paper is the slow response of inference engines, especially in a multi-user environment. In this paper, we present a solution that caches all the possible answers to all the possible consultations, by means of translating the entire knowledge base into a set of HTML documents. We prove that the consumption of memory in this case is acceptable (depends linearly on the number of rules in the initial knowledge base), and that the translational algorithm has polynomial complexity. This solution outperforms any other possible solution, since in this case the infering time becomes equal to zero. [Copyright &y& Elsevier]

Abstract: The concept of knowledge and knowledge management is not new. Researchers identified the practice of knowledge management as early as 4000 years ago. However, knowledge management systems (KMS), which involve the application of IT systems and other organizational resources to manage knowledge strategically, are a relatively recent phenomenon. While the literature on knowledge management covers various issues, it lacks comprehensive studies of factors and variables of adoption and diffusion of KMS. This paper studies these factors and variables in the context of some Australian organizations. A qualitative field study is undertaken in this research, where six organizations of various sizes, all in various stages of KMS adoption and diffusion, are studied via interviews with key personnel. Content analysis is then performed to extract the factors and variables and a comprehensive model of KMS adoption and diffusion is developed. The results of the interviews identify four major variables affecting KMS diffusion as: organizational culture, top management support, benefits to individuals, and dream of KMS. The paper also highlights the research and managerial implications of the KMS diffusion model. [Copyright &y& Elsevier]

This paper argues that there are certain concepts within the general domain of Knowledge Management that have not been fully explored. The discipline will benefit from a more detailed look at some of these concepts. The concepts of risk, gap and strength are the particular concepts that are explored in some more detail within this paper. A reason for describing these elements as concepts rather than terms is discussed. More precise definitions for the concepts described can provide management support about the knowledge resource in decision-making. Several function definitions for risk, gap and strength are offered. Finally, the paper considers how these concepts can influence organisational knowledge management schemes. [Copyright &y& Elsevier]

Rough set provides a theoretical framework for classification learning in data mining and knowledge discovery. As an important application of rough set, attribute reduction, also called feature selection, aims to reduce the redundant attributes in a given decision system while preserving a particular classification property, e.g., information entropy and knowledge granularity. In view of the dynamic changes of the object set in a decision system, in this paper, we focus on knowledge granularity-based attribute reduction approach when some objects vary dynamically. We first introduce incremental mechanisms to compute new knowledge granularity. Then, the corresponding incremental algorithms for attribute reduction are developed when some objects are added into and deleted from the decision system. Experiments conducted on different data sets from UCI show that the proposed incremental algorithm can achieve better performance than the non-incremental counterpart and incremental algorithm based on entropy. [ABSTRACT FROM AUTHOR]

Attribute reduction plays an important role in the areas of rough sets and granular computing. Many kinds of attribute reducts have been defined in previous studies. However, most of them concentrate on data only, which result in the difficulties of choosing appropriate attribute reducts for specific applications. It would be ideal if we could combine properties of data and user preference in the definition of attribute reduct. In this paper, based on reviewing existing definitions of attribute reducts, we propose a generalized attribute reduct which not only considers the data but also user preference. The generalized attribute reduct is the minimal subset which satisfies a specific condition defined by users. The condition is represented by a group of measures and a group of thresholds, which are relevant to user requirements or real applications. For the same data, different users can define different reducts and obtain their interested results according to their applications. Most current attribute reducts can be derived from the generalized reduct. Several reduction approaches are also summarized to help users to design their appropriate reducts. [ABSTRACT FROM AUTHOR]

Incomplete contexts are a kind of formal contexts in which the relationship between some objects and some attributes is unavailable or lost. Knowledge discovery in incomplete contexts is of interest because such databases are frequently encountered in the real world. This paper mainly focuses on two issues: approximate concept construction with three-way decisions and attribute reduction in incomplete contexts. The theory of three-way decisions is formulated based on the notions of acceptance, rejection and non-commitment. It is an extension of the commonly used binary-decision model with an added third option. Based on three-way decisions, we propose two models to construct approximate concepts in incomplete contexts, and the equivalence of the two is revealed. To simplify the representation of the approximate concept lattices, we further present the attribute reduction approaches. [ABSTRACT FROM AUTHOR]

Cluster ensemble has been shown to be very effective in unsupervised classification learning by generating a large pool of different clustering solutions and then combining them into a final decision. However, the task of it becomes more difficult due to the inherent complexities among base cluster results, such as uncertainty, vagueness and overlapping. Granular computing is one of the fastest growing information-processing paradigms in the domain of computational intelligence and human-centric systems. As the core part of granular computing, the rough set theory dealing with inexact, uncertain, or vague information, has been widely applied in machine learning and knowledge discovery related areas in recent years. From these perspectives, in this paper, a hierarchical cluster ensemble model based on knowledge granulation is proposed with the attempt to provide a new way to deal with the cluster ensemble problem together with ensemble learning application of the knowledge granulation. A novel rough distance is introduced to measure the dissimilarity between base partitions and the notion of knowledge granulation is improved to measure the agglomeration degree of a given granule. Furthermore, a novel objective function for cluster ensembles is defined and the corresponding inferences are made. A hierarchical cluster ensemble algorithm based on knowledge granulation is designed. Experimental results on real-world data sets demonstrate the effectiveness for better cluster ensemble of the proposed method. [ABSTRACT FROM AUTHOR]

This paper considers proactive scheduling in response to stochastic machine breakdown under deteriorating production environments, where the actual processing time of a job gets longer along with machine's usage and age. It is assumed that a job's processing time is controllable by allocating extra resources and the machine breakdown can be described using a given probability distribution. If a machine breaks down, it needs to be repaired and is no longer available during the repair. To absorb the repair duration, the subsequent unfinished jobs are compressed as much as possible to match up the baseline schedule. This work aims to find the optimal baseline sequence and the resource allocation strategy to minimize the operational cost consisting of the total completion time cost and the resource consumption cost of the baseline schedule, and the rescheduling cost consisting of the match-up time cost and additional resource cost. To this end, an efficient multi-objective evolutionary algorithm based on elitist non-dominated sorting is proposed, in which a support vector regression (SVR) surrogate model is built to replace the time-consuming simulations in evaluating the rescheduling cost, which represents the solution robustness of the baseline schedule. In addition, a priori domain knowledge is embedded in population initialization and offspring generation to further enhance the performance of the algorithm. Comparative results and statistical analysis show that the proposed algorithm is effective in finding non-dominated tradeoff solutions between operational cost and robustness in the presence of machine breakdown and deterioration effect. [ABSTRACT FROM AUTHOR]

Transfer learning focuses on leveraging the knowledge in source domains to complete the learning tasks in target domains, where the data distributions of the source and target domains are related but different in accordance with original features. To tackle the challenge of different data distributions, previous methods mine the high-level concepts (e.g., feature clusters) from original features, which shows to be suitable for the classification. The general strategies of the previous approaches are to utilize the identical concepts, the synonymous concepts or both of them as shared concepts to establish the bridge between the source and target domains. Besides the shared concepts, some methods use the different concepts for training model. Specifically, these methods assume that the identical concepts (e.g., feature clusters) in different domains can be mapped to the same example classes. However, some ambiguous concepts may exist in different domains and result in misleading classification in the target domains. Therefore, we need a general transfer learning framework, which can exploit four kinds of concepts including the identical concepts, the synonymous concepts, the different concepts and the ambiguous concepts simultaneously, for cross-domain classification.In this paper, we present a novel method, Quadruple Transfer Learning (QTL), which models these four kinds of concepts together to fit different situations on the data distributions. In addition, an iterative algorithm with convergence guarantee based on non-negative matrix tri-factorization techniques is presented to solve the optimization problem. Finally, systematic experiments demonstrate that QTL is more effective than all the compared baselines. [ABSTRACT FROM AUTHOR]

Human activity recognition can be exploited to benefit ubiquitous applications using sensors. Current research on sensor-based activity recognition is mainly using data-driven or knowledge-driven approaches. In terms of complex activity recognition, most data-driven approaches suffer from portability, extensibility and interpretability problems, whilst knowledge-driven approaches are often weak in handling intricate temporal data. To address these issues, we exploit time series shapelets for complex human activity recognition. In this paper, we first describe the association between activity and time series transformed from sensor data. Then, we present a recursively defined multilayered activity model to represent four types of activities and employ a shapelet-based framework to recognize various activities represented in the model. A prototype system was implemented to evaluate our approach on two public datasets. We also conducted two real-world case studies for system evaluation: daily living activity recognition and basketball play activity recognition. The experimental results show that our approach is capable of handling complex activity effectively. The results are interpretable and accurate, and our approach is fast and energy-efficient in real-time. [ABSTRACT FROM AUTHOR]

This paper proposes a technique to improve the performance of spoken dialogue systems that not only consider knowledge about the semantic frames used by systems to understand the spoken language but also employ knowledge about the words in the system application domain that are used to complete frame slots. Using both knowledge sources, the technique considers specific word sequences to form what we call word-islands and which are then employed to create the language models and dictionary used by the system’s speech recogniser. Word-islands are easier to recognise than the words comprising the islands, which leads to improved spoken language understanding and system performance. Experiments have been conducted using two spoken dialogue systems which had previously been developed in our lab: one to provide fast food information and the other to provide bus travel information. Results show that the proposed technique improves the performance of both systems by improving speech recognition and spoken language understanding of sentence types that are difficult to process. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the resource scheduling assignment problem in cellular mobile networks by considering both the inter-cell interference and intra-cell interference simultaneously. The task of this problem is to find the minimum required bandwidth to satisfy channel demand from each cell without interference constraints violation. Different from existing works, a novel two-phase hyper-heuristic technique which integrates harmony search and a set of prior information based heuristics is proposed to solve it. We validate performance of our approach by integrating a local search procedure in the later iterations. The proposed algorithm is tested on a set of benchmark problems, and the results are compared with the best state-of-the-art approaches. The results show that the method proposed in this article works effectively on the benchmark problems and has better performance on most problems than the algorithms that are investigated. [ABSTRACT FROM AUTHOR]

The paper describes a new approach of developing and maintaining state-of-the-art decision support systems. Such systems are able to capture the collaborative work on decision problems over time. Due to the complexity of large problem spaces a multi-modal knowledge representation is proposed. For the realization of a multi-modal knowledge base we integrate semantic technologies as a fundamental layer by combining the W3C ontologies PROV-O and SKOS. The approach is demonstrated by an implementation report of an industrially deployed decision support system. [ABSTRACT FROM AUTHOR]

Organizing a set of domain-specific terms into a meaningful hierarchical structure is an essential task for faceted search and knowledge organization. In this paper, we present an automatic approach, called domain-specific facet (DF)-Miner, to discover DFs based on the hyperlink structure within the Wikipedia article pages. Each article page corresponds to a domain-specific term. The hyperlink structures among article pages represent the connections among these terms. The community structure of the connections among a domain-specific term set reveals the facets of the domain. The terms with more connections provide important clues for facet labeling. Accordingly, DF-Miner first constructs a domain-specific hyperlink graph from the Wikipedia article pages. Then it extracts a tree structure from the Wikipedia category pages. DF-Miner groups the terms of a domain into multiple facets based on the result of community detection. Finally, DF-Miner selects a meaningful label for each facet based on the connection number of terms and the extracted tree structure from the category pages. Two experiments were conducted with six real-world datasets to evaluate DF-Miner. The experimental results show that DF-Miner performs better than the textual content-based approaches. [ABSTRACT FROM AUTHOR]

Being aware of people’s unuttered knowledge needs is the prerequisite of providing “active” and “in time” knowledge assistance. However, such an awareness of knowledge needs has been achieved at a high cost since most existing methods rely on the manually defined rules or a large amount of user data to work. In this paper, we formulate the problematic situations in task processing as knowledge application context (KAC), and propose to elicit KACs semi-automatically from domain Q&A archives. Assuming that the KACs frequently occurring and semantically matching with the user’s task context are more likely to imply the knowledge needs of the user, we design a mechanism of knowledge need awareness (KNA) to predict users’ knowledge needs in complex tasks. Experimental results show that the proposed method has significantly outperformed the information retrieval approaches used as baselines. The study provides a new method for reusing the contextualized knowledge in Q&A and thereby opens up a new way to build efficient active knowledge systems. [ABSTRACT FROM AUTHOR]

To systematically create and share product development knowledge creates challenges for engineering companies. This paper presents an extensive study regarding the process of identifying such challenges in managing product development knowledge from the perspective of designers and engineers. This research is part of the LeanPPD, a project funded by the EU-PF7 ( www.leanppd.eu ), to address the need of European manufacturing companies for a new model, which extends beyond lean manufacturing and incorporates lean thinking into the product design and development process. A rigorous research methodology has been employed, which included the use of questionnaires and focused interviews with key informants from industry, involving forty-two product development engineers from nine different companies. The most significant concerns raised during the study concerned knowledge life cycle activities, product development environment and management. Thirty-eight challenges were identified, classified and discussed in order to provide the knowledge management community with practical evidence, and also to inform future research directions in managing product development knowledge. [ABSTRACT FROM AUTHOR]

This article is devoted to the issue of forecasting exchange rates. The objective of the conducted research is to develop a predictive model with the use of an innovative methodology - fuzzy logic theory - and to evaluate its effectiveness in times of prosperity (years 2005-2007) and during the financial crisis (years 2009-2011). The model is based on sets of rules written by the author in the form of IF-THEN, where expert knowledge is stored. This model is the result of ten years of the author's research on this issue. Empirically, this paper employs three currency pairs as experimental datasets: JPY/USD, GBP/USD and CHF/USD. From the model verification, it is demonstrated that refined processes are effective in improving the forecasting of exchange rate movements. The author's created model is characterised by high efficiency. These studies are among the world's first attempts to combine fundamental analysis with fuzzy logic to predict exchange rates. [ABSTRACT FROM AUTHOR]

In item-based top-N recommender systems, the recommendation results are generated based on item correlation computation among all users. Therefore, recommendation results can be used to infer the correlations among recommended items. This is not an issue as long as the total amount of queries produced by a typical user is small, and the queried items among users are largely uncorrelated. However, by systematically probing the recommender system, a large amount of correlated recommendation results can be obtained and combined, and valuable aggregated knowledge, such as system-wide cross-user item popularity ranking and item clustering, can be accurately inferred. Such aggregated knowledge is of significant commercial value to online service providers, and therefore need be restricted from open access. In this work, four aggregated knowledge attack methods are proposed to demonstrate that aggregated knowledge can be accurately inferred by attacking item-based top-N recommender systems. To make the recommender systems resilient to aggregated information revelation, a supervised randomization technique is proposed, which can protect item-based top-N recommender systems from aggregated knowledge attacks with bounded loss in recommendation accuracy. Detailed evaluation on real-world data demonstrates that the proposed attack methods can identify aggregated knowledge with high accuracy, and the proposed randomization technique can increase attack error or reduce attack precision significantly. In addition, guidelines for designing recommender systems that are resilient to such aggregated knowledge attacks are discussed in the paper. [ABSTRACT FROM AUTHOR]

Abstract: Cloud computing is a technological paradigm that permits computing services to be offered over the Internet. This new service model is closely related to previous well-known distributed computing initiatives such as Web services and grid computing. In the current socio-economic climate, the affordability of cloud computing has made it one of the most popular recent innovations. This has led to the availability of more and more cloud services, as a consequence of which it is becoming increasingly difficult for service consumers to find and access those cloud services that fulfil their requirements. In this paper, we present a semantically-enhanced platform that will assist in the process of discovering the cloud services that best match user needs. This fully-fledged system encompasses two basic functions: the creation of a repository with the semantic description of cloud services and the search for services that accomplish the required expectations. The cloud service’s semantic repository is generated by means of an automatic tool that first annotates the cloud service descriptions with semantic content and then creates a semantic vector for each service. The comprehensive evaluation of the tool in the ICT domain has led to very promising results that outperform state-of-the-art solutions in similarly broad domains. [Copyright &y& Elsevier]

Abstract: Data publishing is an easy and economic means for data sharing, but the privacy risk is a major concern in data publishing. Privacy preservation is a major task in data sharing for organizations like bureau of statistics, and hospitals. While a large number of data publishing models and methods have been proposed, their utility is of concern when a high privacy requirement is imposed. In this paper, we propose a new framework for privacy preserving data publishing. We cap the belief of an adversary inferring a sensitive value in a published data set to as high as that of an inference based on public knowledge. The semantic meaning is that when an adversary sees a record in a published data set, s/he will have a lower confidence that the record belongs to a victim than not. We design a method integrating sampling and generalization to implement the model. We compare the method with some state-of-the-art methods on privacy-preserving data publishing experimentally, our proposed method provides sound semantic protection of individuals in data and, provides higher data utility. [Copyright &y& Elsevier]

Abstract: Named entity recognition (NER) is a subtask in information extraction which aims to locate atomic element into predefined types. Various NER techniques and tools have been developed to fit the interest of the applications developed. However, most NER works carried out focus on non-fiction domain. Fiction based domain displays a complex context in locating its NE, specifically whereby its characters could be represented in diverse spectrums, ranging from living things (animals, plants, and person) to non-living things (vehicle, furniture). Motivated by a hypothesis such that there always exists verb specifically describes human being conduct, in this paper, we propose a NER system which aims to identify NEs that perform human activity based on verb analysis (VAHA) in an autonomous manner. More specifically, our approach attempts to identify dominant character (DC) by studying the nature of verb that associates with human activity via TreeTagger, Stanford packages and WordNet. Our experimental results validate our initial hypothesis that NEs can be accurately identified by referring to the associated verbs that associate with human activity. Our empirical study also proves that the approach is applicable to small text size articles. Another significant contribution of our approach is that it does not require training data set and anaphora resolution. [Copyright &y& Elsevier]

Abstract: Side information, like must-link (ML) and cannot-link (CL), has been widely used in single-view classification tasks. However, so far such information has never been applied in multi-view classification tasks. In many real world situations, data with multiple representations or views are frequently encountered, and most proposed algorithms for such learning situations require that all the multi-view data should be paired. Yet this requirement is difficult to satisfy in some settings and the multi-view data could be totally unpaired. In this paper, we propose an learning framework to design the multi-view classifiers by only employing the weak side information of cross-view must-links (CvML) and cross-view cannot-links (CvCL). The CvML and the CvCL generalize the traditional single-view must-link (SvML) and single-view cannot-link (SvCL), and to the best of our knowledge, are first definitely introduced and applied into the multi-view classification situations. Finally, we demonstrate the effectiveness of our method in our experiments. [Copyright &y& Elsevier]

Abstract: Marketing plays an essential role in the competitive business environment. Marketing decision makers constantly need to monitor changes in the environment and organization to make necessary changes. Therefore, a knowledge management (KM) system is required to acquire, store, retrieve and use up-to-date knowledge. Corporations also tend to look for systems to assist them in knowledge management. Agent technology looks set for assisting organizations in collecting, processing and using knowledge with high accuracy, speed and efficiency. This paper proposes a knowledge management framework for marketing decision making through using agent technology, fuzzy AHP (Analytical Hierarchy Process) and fuzzy logic. The fuzzy AHP is used for allocating the weight of determinant criteria and fuzzy logic is deployed for determining the final decision in pessimistic, moderate and optimistic satiations. The objectives of this study are as follows: (a) developing a knowledge management framework for capturing, storing, disseminating and utilizing marketing knowledge by using agent system, and (b) helping marketing decision makers to make effective decisions based on useful knowledge, and (c) encouraging marketing decisions towards the intelligence. Implementation of the proposed system in a car factory indicates that it is efficient and effective in supporting and improving marketing decision making. [Copyright &y& Elsevier]

Abstract: One of the most challenging steps in learning algebra is the translation of word problems into symbolic notation. This paper describes an Intelligent Tutoring System (ITS) that focuses on this stage of the problem solving process. On the one hand, a domain specific inference engine and a knowledge representation mechanism are proposed. These are based on a description language based on hypergraphs, and the idea of using conceptual schemes to represent the student’s knowledge. As a result, the system is able to simultaneously: (a) represent all potential algebraic solutions to a given word problem; (b) keep track of the student’s actions; (c) univocally determine the current state of the resolution process; (d) build a student model; and (e) provide adaptive automatic remediation. On the other hand, the Graphical User Interface (GUI) has been designed to force the student to follow the sequence of steps described in the Cartesian method. The ITS has been evaluated in an educational environment. The results show significant gains for the experimental group and hence support the use of the ITS in practice. [Copyright &y& Elsevier]

Discovering knowledge combination has been considered an effective strategy for knowledge retrieval and knowledge discovery. Generally, knowledge combination driven by close-cooperation can be achieved via modeling the process of knowledge transfer. However, the existing studies seldom built connections between knowledge transfer and the identification of knowledge combination, especially in the existing knowledge transfer models, less attention is paid to the effects of trust and knowledge similarity. Therefore, the research motivations of this paper are to model the process of knowledge transfer and to further discover knowledge combinations. To minimize the risks of knowledge transfer, both the knowledge similarity and the trust embodied within need to be taken into account, thereby proposing a bi-layered network regarding knowledge similarity and trust. First, a trust network is obtained novelly whereby the proposed method of trust link prediction. Accordingly, a directed knowledge flow network is constructed through a proposed knowledge transfer model endowed with trust scores. Second, knowledge combinations in a knowledge flow network are therefore acquired by adopting a community detection method. Third, various probabilities of knowledge combinations based on the maximum network modularity are calculated with respect to the influence of knowledge similarity on cooperation probability. The key contributions of this paper are summarized as an effective approach to identifying knowledge combinations conducted to improve the efficiencies of knowledge management. Related experiments and comparisons are presented to illustrate the practicalities of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: The Dempster–Shafer evidence theory (D–S theory) is one of the primary tools for knowledge representation and uncertain reasoning, and has been widely used in many information fusion systems. However, how to determine the basic probability assignment (BPA), which is the main and first step in D–S theory, is still an open issue. In this paper, based on the normal distribution, a method to obtain BPA is proposed. The training data are used to build a normal distribution-based model for each attribute of the data. Then, a nested structure BPA function can be constructed, using the relationship between the test data and the normal distribution model. A normality test and normality transformation are integrated into the proposed method to handle non-normal data. The missing attribute values in datasets are addressed as ignorance in the framework of the evidence theory. Several benchmark pattern classification problems are used to demonstrate the proposed method and to compare against existing methods. Experiments provide encouraging results in terms of classification accuracy, and the proposed method is seen to perform well without a large amount of training data. [Copyright &y& Elsevier]