Topic
Web intelligence
About: Web intelligence is a research topic. Over the lifetime, 9476 publications have been published within this topic receiving 227404 citations.
Papers published on a yearly basis
Papers
Book•
2 Feb 2011TL;DR: This Synthesis lecture provides readers with a detailed technical introduction to Linked Data, including coverage of relevant aspects of Web architecture, as the basis for application development, research or further study.
Abstract: The World Wide Web has enabled the creation of a global information space comprising linked documents. As the Web becomes ever more enmeshed with our daily lives, there is a growing desire for direct access to raw data not currently available on the Web or bound up in hypertext documents. Linked Data provides a publishing paradigm in which not only documents, but also data, can be a first class citizen of the Web, thereby enabling the extension of the Web with a global data space based on open standards - the Web of Data. In this Synthesis lecture we provide readers with a detailed technical introduction to Linked Data. We begin by outlining the basic principles of Linked Data, including coverage of relevant aspects of Web architecture. The remainder of the text is based around two main themes - the publication and consumption of Linked Data. Drawing on a practical Linked Data scenario, we provide guidance and best practices on: architectural approaches to publishing Linked Data; choosing URIs and vocabularies to identify and describe resources; deciding what data to return in a description of a resource on the Web; methods and frameworks for automated linking of data sets; and testing and debugging approaches for Linked Data deployments. We give an overview of existing Linked Data applications and then examine the architectures that are used to consume Linked Data from the Web, alongside existing tools and frameworks that enable these. Readers can expect to gain a rich technical understanding of Linked Data fundamentals, as the basis for application development, research or further study.
2,392 citations
Book•
1 Jan 2018
TL;DR: Computational Intelligence: An Introduction, Second Edition offers an in-depth exploration into the adaptive mechanisms that enable intelligent behaviour in complex and changing environments, encompassing swarm intelligence, fuzzy systems, artificial neutral networks, artificial immune systems and evolutionary computation.
Abstract: Computational Intelligence: An Introduction, Second Edition offers an in-depth exploration into the adaptive mechanisms that enable intelligent behaviour in complex and changing environments. The main focus of this text is centred on the computational modelling of biological and natural intelligent systems, encompassing swarm intelligence, fuzzy systems, artificial neutral networks, artificial immune systems and evolutionary computation. Engelbrecht provides readers with a wide knowledge of Computational Intelligence (CI) paradigms and algorithms; inviting readers to implement and problem solve real-world, complex problems within the CI development framework. This implementation framework will enable readers to tackle new problems without any difficulty through a single Java class as part of the CI library. Key features of this second edition include: A tutorial, hands-on based presentation of the material. State-of-the-art coverage of the most recent developments in computational intelligence with more elaborate discussions on intelligence and artificial intelligence (AI). New discussion of Darwinian evolution versus Lamarckian evolution, also including swarm robotics, hybrid systems and artificial immune systems. A section on how to perform empirical studies; topics including statistical analysis of stochastic algorithms, and an open source library of CI algorithms. Tables, illustrations, graphs, examples, assignments, Java code implementing the algorithms, and a complete CI implementation and experimental framework. Computational Intelligence: An Introduction, Second Edition is essential reading for third and fourth year undergraduate and postgraduate students studying CI. The first edition has been prescribed by a number of overseas universities and is thus a valuable teaching tool. In addition, it will also be a useful resource for researchers in Computational Intelligence and Artificial Intelligence, as well as engineers, statisticians, operational researchers, and bioinformaticians with an interest in applying AI or CI to solve problems in their domains. Check out http://www.ci.cs.up.ac.za for examples, assignments and Java code implementing the algorithms.
2,301 citations
TL;DR: This paper surveys the research in the area of Web mining, point out some confusions regarded the usage of the term Web mining and suggest three Web mining categories, which are then situate some of the research with respect to these three categories.
Abstract: With the huge amount of information available online, the World Wide Web is a fertile area for data mining research. The Web mining research is at the cross road of research from several research communities, such as database, information retrieval, and within AI, especially the sub-areas of machine learning and natural language processing. However, there is a lot of confusions when comparing research efforts from different point of views. In this paper, we survey the research in the area of Web mining, point out some confusions regarded the usage of the term Web mining and suggest three Web mining categories. Then we situate some of the research with respect to these three categories. We also explore the connection between the Web mining categories and the related agent paradigm. For the survey, we focus on representation issues, on the process, on the learning algorithm, and on the application of the recent works as the criteria. We conclude the paper with some research issues.
1,853 citations
TL;DR: Four factors that are critical to Web site success in EC were identified: information and service quality, system use, playfulness, and system design quality.
1,793 citations
Book•
1 Jan 2007TL;DR: This paper presents a meta-modelling architecture for the adaptive web that automates the very labor-intensive and therefore time-heavy and expensive process of manually cataloging and cataloging content on the web.
Abstract: I. Modeling Technologies.- User Models for Adaptive Hypermedia and Adaptive Educational Systems.- User Profiles for Personalized Information Access.- Data Mining for Web Personalization.- Generic User Modeling Systems.- Web Document Modeling.- II. Adaptation Technologies.- Personalized Search on the World Wide Web.- Adaptive Focused Crawling.- Adaptive Navigation Support.- Collaborative Filtering Recommender Systems.- Content-Based Recommendation Systems.- Case-Based Recommendation.- Hybrid Web Recommender Systems.- Adaptive Content Presentation for the Web.- Adaptive 3D Web Sites.- III. Applications.- Adaptive Information for Consumers of Healthcare.- Personalization in E-Commerce Applications.- Adaptive Mobile Guides.- Adaptive News Access.- IV. Challenges.- Adaptive Support for Distributed Collaboration.- Recommendation to Groups.- Privacy-Enhanced Web Personalization.- Open Corpus Adaptive Educational Hypermedia.- Semantic Web Technologies for the Adaptive Web.- Usability Engineering for the Adaptive Web.
1,741 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2025 | 19 |
| 2024 | 10 |
| 2023 | 30 |
| 2022 | 36 |
| 2021 | 9 |
| 2020 | 16 |