Knowledge provision with intelligent e‐services

Interorganizational knowledge networks and knowledge marketplaces have emerged to enable organizations to share or commercially exploit their knowledge outside narrow organizational borders. The materialization of these structures requires concrete and sound mechanisms for the efficient external provision of knowledge stored in knowledge repositories within the organization. In our approach, we employ semantic Web services as a vehicle for publishing knowledge repositories. We propose “knowledge provision services” as a means for efficient retrieval and composition of knowledge objects from knowledge repositories of various organizational contexts regardless of the environment within which they are delivered. In this direction, we have extended OWL‐S with a knowledge object ontology, which represents knowledge objects in a generic, application‐neutral way, and we have developed an infrastructure for the publication, discovery, composition, and delivery of Knowledge Provision Services founded on the Web services architecture. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 501–518, 2007.     

A model of an information retrieval system with unbalanced fuzzy linguistic information

Most information retrieval systems based on linguistic approaches use symmetrically and uniformly distributed linguistic term sets to express the weights of queries and the relevance degrees of documents. However, to improve the system–user interaction, it seems more adequate to express these linguistic weights and degrees by means of unbalanced linguistic scales, that is, linguistic term sets with different discrimination levels on both sides of the middle linguistic term. In this contribution we present an information retrieval system that accepts weighted queries whose weights are expressed using unbalanced linguistic term sets. Then, the system provides the retrieved documents classified in linguistic relevance classes assessed on unbalanced linguistic term sets. To do so, we propose a methodology to manage unbalanced linguistic information and we use the linguistic 2‐tuple model as the representation base of the unbalanced linguistic information. Additionally, the linguistic 2‐tuple model allows us to increase the number of relevance classes in the output and also to improve the performance of the information retrieval system. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1197–1214, 2007.     

Trust ontologies for e‐service environments

In this article, we introduce trust ontologies. An ontology represents a set of concepts that are commonly shared and agreed to by all parties in a particular domain. Here, we introduce generic and specific trust ontologies. These ontologies include the following: an agent trust ontology and trustworthiness; agents include sellers, service providers, Web sites, brokers, shops, suppliers, buyers, or reviewers. A services trust ontology and trustworthiness assists in measuring the quality of service that agents provide in the service‐oriented environment such as sales, orders, track and trace, warehousing, logistics, education, governance, advertising, entertainment, trading, online databases, virtual community services, security, information services, opinions, and e‐reviews. A goods or products trust ontology and trustworthiness is useful for measuring the quality of products such as commercial products, information products, entertainment products, or second‐hand products. We present a trust ontology that is suitable for all types of agents that exist in the service‐oriented environment. As agent trust is measured through the quality of goods and services, we introduce two additional distinct concepts of service trust ontology and product trust ontology. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 519–545, 2007.     

Folksonomy‐Based Recommender Systems: A State‐of‐the‐Art Review

Collaborative tagging systems, also known as folksonomies, have grown in popularity over the Web on account of their simplicity to organize several types of content (e.g., Web pages, pictures, and video) using open‐ended tags. The rapid adoption of these systems has led to an increasing amount of users providing information about themselves and, at the same time, a growing and rich corpus of social knowledge that can be exploited by recommendation technologies. In this context, tripartite relationships between users, resources, and tags contained in folksonomies set new challenges for knowledge discovery approaches to be applied for the purposes of assisting users through recommendation systems. This review aims at providing a comprehensive overview of the literature in the field of folksonomy‐based recommender systems. Current recommendation approaches stemming from fields such as user modeling, collaborative filtering, content, and link‐analysis are reviewed and discussed to provide a starting point for researchers in the field as well as explore future research lines.     

A multigranular linguistic content‐based recommendation model

Recommendation systems are a clear example of an e‐service that helps the users to find the most suitable products they are looking for, according to their preferences, among a vast quantity of information. These preferences are usually related to human perceptions because the customers express their needs, taste, and so forth to find a suitable product. The perceptions are better modeled by means of linguistic information due to the uncertainty involved in this type of information. In this article, we propose a content‐based recommendation model that will offer a more flexible context to improve the final recommendations where the preferences provided by the sources will be modeled by means of linguistic variables assessed in different linguistic term sets. The proposal consists of offering a multigranular linguistic context for expressing the preferences instead of forcing users to use a unique scale. Then the content‐based recommendation model will look for the most suitable product(s), comparing them with the customer(s) information according to its resemblance. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 419–434, 2007.     

Developing and assessing an intelligent forex rolling forecasting and trading decision support system for online e‐service

An effective foreign exchange (forex) trading decision is usually dependent on effective forex forecasting. In this study, an intelligent system framework integrating forex forecasting and trading decision is first proposed. Based on this framework, an advanced intelligent decision support system (DSS) incorporating a back‐propagation neural network (BPNN)‐based forex forecasting subsystem and Web‐based forex trading decision support subsystem is developed, which has been used to predict the directional change of daily forex rates and provide intelligent online decision support for financial institutions and individual investors. This article describes the forex forecasting and trading decision method, the system architecture, main functions, and operation of the developed DSS system. A comparative study is conducted between our developed system and others commonly used in order to assess the overall performance of the developed system. The assessment results show that our developed DSS outperforms some commonly used forex forecasting and trading decision systems and can provide intelligent e‐service for forex traders to make useful trading decisions in the forex market. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 475–499, 2007.     

Evaluating e‐learning web site quality in a fuzzy environment

The proliferation of the Internet and World Wide Web applications has created new opportunities as well as new challenges for institutions and individuals who are either receiving or delivering education. Electronic (e) learning is one of the most important developments in education. It recognizes the shift from teaching to learning and puts the learner or user before the institution. The objectives and expected outcomes of e‐learning are largely dependent on the quality of the teaching processes and the effectiveness of online access. Hence, assessing methods for the effectiveness of e‐learning Web sites are a critical issue in both practice and research. However, Web site quality is a complex concept and its measurement is expected to be multidimensional in nature. Multicriteria decision‐making (MCDM) techniques are widely used for evaluating and ranking such problems containing multiple, usually conflicting criteria. For this reason, this article presents a quality evaluation model based on the MCDM to measure the e‐learning Web sites' performance. In addition, the subjectivity and vagueness in the assessment process are dealt with using fuzzy logic. The study has investigated 10 worldwide and 11 locally successful Web sites with the proposed method. By suggesting an aggregated measure based on the Web site quality criteria, it is expected that the method could be useful to the e‐learning service providers and system developers, as well as to the researchers related with Web research. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 567–586, 2007.     

Handling linguistic web information based on a multi‐agent system

Much information over the Internet is expressed by natural languages. The management of linguistic information involves an operation of comparison and aggregation. Based on the Ordered Weighted Averaging (OWA) operator and modifying indexes of linguistic terms (their indexes are fuzzy numbers on [0,T] ? R⁺), new linguistic aggregating methods are presented and their properties are discussed. Also, based on a multi‐agent system and new linguistic aggregating methods, gathering linguistic information over the Internet is discussed. Moreover, by fixing the threshold α, “soft filtering information” is proposed and better Web pages (or documents) that the user needs are obtained. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 435–453, 2007.     

A fuzzy logic approach to experience‐based reasoning

Experience‐based reasoning (EBR) is a reasoning paradigm that has been used in almost every human activity such as business, military missions, and teaching activities since early human history. However, EBR has not been seriously studied from either a logical or mathematical viewpoint, although case‐based reasoning (CBR) researchers have paid attention to EBR to some extent. This article will attempt to fill this gap by providing a unified fuzzy logic‐based treatment of EBR. More specifically, this article first reviews the logical approach to EBR, in which eight different rules of inference for EBR are discussed. Then the article proposes fuzzy logic‐based models to these eight different rules of inference that constitute the fundamentals for all EBR paradigms from a fuzzy logic viewpoint, and therefore will form a theoretical foundation for EBR. The proposed approach will facilitate research and development of EBR, fuzzy systems, intelligent systems, knowledge management, and experience management. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 867–889, 2007.     

Assessment of four modifications of a novel indexing technique for case‐based reasoning

In this article, we investigate four variations (D‐HS^M, D‐HS^W, D‐HS^E, and D‐HS^EW) of a novel indexing technique called D‐HS designed for use in case‐based reasoning (CBR) systems. All D‐HS modifications are based on a matrix of cases indexed by their discretized attribute values. The main differences between them are in their attribute discretization stratagem and similarity determination metric. D‐HS^M uses a fixed number of intervals and simple intersection as a similarity metric; D‐HS^W uses the same discretization approach and a weighted intersection; D‐HS^E uses information gain to define the intervals and simple intersection as similarity metric; D‐HS^EW is a combination of D‐HS^E and D‐HS^W. Benefits of using D‐HS include ease of case and similarity knowledge maintenance, simplicity, accuracy, and speed in comparison to conventional approaches widely used in CBR. We present results from the analysis of 20 case bases for classification problems and 15 case bases for regression problems. We demonstrate the improvements in accuracy and/or efficiency of each D‐HS modification in comparison to traditional k‐NN, R‐tree, C4,5, and M5 techniques and show it to be a very attractive approach for indexing case bases. We also illuminate potential areas for further improvement of the D‐HS approach. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 353–383, 2007.     

Some technical and non‐technical issues in implementing a knowledge server

Increasingly, knowledge, as well as information and data, is being transferred over the World Wide Web. There is great potential in linking traditional knowledge‐based systems (KBS) technology with the Internet because each technology can overcome limitations in the other. As a result, it might enable expert knowledge that has hitherto been confined to those who possess the correct computing platforms to be made available to small enterprises and people in developing countries. Five types of KBS–Internet integration are outlined (Intelligent Agents, Active Web Pages, Local KBS Accessing Web‐distributed Information, Web‐distributed Knowledge Bases, and Knowledge Servers). This paper discusses knowledge servers in detail. It examines the issues and problems that must be addressed if existing KBS inference software is to be integrated with the World Wide Web, and discusses, in depth, solutions as implemented in the Istar knowledge server. The paper shows how technical design and implementation decisions can be influenced, not only by the technical characteristics of the Internet, but also by a range of other, ‘softer’ issues. In particular, it shows how real life styles of WWW browsing, and a desire to make knowledge available to developing countries, influences both overall architecture and detailed implementation decisions. Early experience of actual usage shows Istar to be a highly efficient knowledge server. Directions for future research are discussed. Copyright © 2000 John Wiley & Sons, Ltd.     

Clustering web search results using fuzzy ants

Algorithms for clustering Web search results have to be efficient and robust. Furthermore they must be able to cluster a data set without using any kind of a priori information, such as the required number of clusters. Clustering algorithms inspired by the behavior of real ants generally meet these requirements. In this article we propose a novel approach to ant‐based clustering, based on fuzzy logic. We show that it improves existing approaches and illustrates how our algorithm can be applied to the problem of Web search results clustering. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 455–474, 2007.     

A SUPER‐AGENT‐BASED FRAMEWORK FOR REPUTATION MANAGEMENT AND COMMUNITY FORMATION IN DECENTRALIZED SYSTEMS

In this article, we propose a novel super‐agent‐based framework for reputation management and community formation in decentralized systems. We describe this framework in the context of Web service selection where agents with more capabilities act as super‐agents. These super‐agents serve as reputation managers to maintain reputation information of services and share the information with other consumer agents that have fewer capabilities than the super‐agents. In addition, super‐agents can maintain communities and build community‐based reputation for a service based on the opinions from all community members that have similar interests and judgement criteria as the super‐agents or the other community members. A practical reward mechanism is also introduced to create incentives for super‐agents to contribute their resources (to maintain reputation and form communities) and provide truthful reputation information. Experimental results obtained through simulation confirm that our approach achieves better effectiveness and scalability compared to the systems that do not use super‐agents and that do not form communities.     

Boosting fuzzy rules in classification problems under single‐winner inference

In previous studies, we have shown that an Adaboost‐based fitness can be successfully combined with a Genetic Algorithm to iteratively learn fuzzy rules from examples in classification problems. Unfortunately, some restrictive constraints in the implementation of the logical connectives and the inference method were assumed. Alas, the knowledge bases Adaboost produces are only compatible with an inference based on the maximum sum of votes scheme, and they can only use the t‐norm product to model the “and” operator. This design is not optimal in terms of linguistic interpretability. Using the sum to aggregate votes allows many rules to be combined, when the class of an example is being decided. Because it can be difficult to isolate the contribution of individual rules to the knowledge base, fuzzy rules produced by Adaboost may be difficult to understand linguistically. In this point of view, single‐winner inference would be a better choice, but it implies dropping some nontrivial hypotheses. In this work we introduce our first results in the search for a boosting‐based genetic method able to learn weighted fuzzy rules that are compatible with this last inference method. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1021–1034, 2007.     

Creating user‐generated content for location‐based learning: an authoring framework

Two recent emerging trends are that of Web 2.0, where users actively create content and publish it on the Web, and also location awareness, where a digital device utilizes a person's physical location as the context to provide specific services and/or information. This paper examines how these two phenomena can be brought together so that user‐generated content on mobile devices is used to provide informal learning opportunities relevant to a person's location. However, the generative process of such media does not always have much guidance on how or what to create, so the quality of such information can be highly variable. To overcome this, a framework has been designed to guide the authoring of user‐generated content so that it can be used for informal learning about one's immediate surroundings (particularly in an outdoor setting), combining pedagogical aspects with those from human–computer interaction and environmental aesthetics. The framework consists of six dimensions that include aspects such as curriculum area (e.g. science, art), type of communication, use of language/media related to the landscape, knowledge level of content, contextual aspects, and types of interaction. In order to test the framework before it could be used to scaffold new content, it was first used to analyse and evaluate over 200 items of existing user‐generated content, to investigate the appropriateness of the proposed dimensions and the items contained therein or if any were missing. This paper presents the findings of this initial testing phase, together with a discussion of how the framework can be improved, in order to help scaffold the creation of new user‐generated content in the future.     

A design for a common‐sense knowledge‐enhanced decision‐support system: Integration of high‐frequency market data and real‐time news

According to efficient markets theory, information is an important factor that affects market performance and serves as a source of first‐hand evidence in decision making, in particular with the rapid rise of Internet technologies in recent years. However, a lack of knowledge and inference ability prevents current decision support systems from processing the wide range of available information. In this paper, we propose a common‐sense knowledge‐supported news model. Compared with previous work, our model is the first to incorporate broad common‐sense knowledge into a decision support system, thereby improving the news analysis process through the application of a graphic random‐walk framework. Prototype and experiments based on Hong Kong stock market data have demonstrated that common‐sense knowledge is an important factor in building financial decision models that incorporate news information.     

Using the moving average rule in a dynamic web recommendation system

In this, the Information Age, most people are accustomed to gleaning information from the World Wide Web. To survive and prosper, a Web site has to constantly enliven its content while providing various and extensive information services to attract users. The Web Recommendation System, a personalized information filter, prompts users to visit a Web site and browse at a deeper level. In general, most of the recommendation systems use large browsing logs to identify and predict users' surfing habits. The process of pattern discovery is time-consuming, and the result is static. Such systems do not satisfy the end users' goal-oriented and dynamic demands. Accordingly, a pressing need for an adaptive recommendation system comes into play. This article proposes a novel Web recommendation system framework, based on the Moving Average Rule, which can respond to new navigation trends and dynamically adapts recommendations for users with suitable suggestions through hyperlinks. The framework provides Web site administrators with various methods to generate recommendations. It also responds to new Web trends, including Web pages that have been updated but have not yet been integrated into regular browsing patterns. Ultimately, this research enables Web sites with dynamic intelligence to effectively tailor users' needs. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 621–639, 2007.     

Fuzzy multi‐attribute cost–benefit analysis of e‐services

E‐service evaluation is a complex problem in which many qualitative attributes must be considered. These kinds of attributes make the evaluation process hard and vague. Cost–benefit analyses applied to various areas are usually based on the data under certainty or risk. In case of uncertain, vague, and/or linguistic data, the fuzzy set theory can be used to handle the analysis. In this article, after the evaluation attributes of e‐services and the fuzzy multi‐attribute decision‐making methods are introduced, a fuzzy hierarchical TOPSIS model is developed and applied to an e‐service provider selection problem with some sensitivity analyses. The developed model is a useful tool for the companies that prefer outsourcing for e‐activities. It is shown that service systems can be effectively evaluated by the proposed method. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 547–565, 2007.     

Extending UML to model Web 2.0‐based context‐aware applications

Web 2.0 Mashups offer entirely new opportunities for context‐aware application (CAA) developers by integrating Web 2.0 technologies to facilitate interoperability among heterogeneous context‐aware systems. From a software engineering perspective, a visualized approach for Web 2.0‐based CAA modeling is crucial. Current CAA development, however, cannot provide a conceptual model for Web 2.0‐based CAA. Therefore, the development efficiency and potential for reuse are decreased. The UML is a general purpose modeling language with potential for use in many application domains. However, UML often lacks elements needed to model concepts in specific domains, such as Web 2.0‐based CAA modeling. To address the above issues, this study presents the Web 2.0‐based CAA UML profile, a UML profile for modeling Web 2.0‐based CAA. Copyright © 2011 John Wiley & Sons, Ltd.     

A City Metaphor to Support Navigation in Complex Information Spaces

A major problem in modern information systems is to locate information and to re-find information one has seen before. Systems like the World Wide Web are heavily interlinked, but do not show structures that help users to navigate the information it contains. The use of appropriate navigation metaphors can help to make the structure of modern information systems easier to understand and therefore easier to use.We propose a conceptual user interface metaphor based on the structure of a city. Cities are very complex spatial environments and people know how to get information, how to reach certain locations in a city, and how to make use of the available infrastructure, etc. Cities provide a rich set of navigational infrastructure that lends itself to creating sub-metaphors for navigational tools. A city metaphor makes this existing knowledge about a structured environment available to the user of a computerized information system.We first focus on several properties necessary for future user interfaces (or user interface metaphors) that will distinguish them from current systems, like the richness of information or the use of visualizations to show the structure of information spaces. We also describe the strengths and problems of spatial user-interface metaphors. Then we present the structure of the information city metaphor, its structuring and navigation metaphors and what we see as its main advantages and problems. We further outline a few scenarios of how an Information City might work. Finally, we compare implementing this metaphor using either a textual or a graphical virtual environment or a combination.