The Impact on Citation Analysis Based on Ontology and Linked Data

Research focus: The aim of this chapter is to introduce a new citation analysis and service framework based on the semantic web technologies (e.g., ontology and linked data). Research methods: This research project is based on a review of relevant literature and a series of experimental results based on ontology and linked data. Motivation: Traditional citation analysis methods and tools are overly dependent on citation databases, and traditional citation information service may ignore the semantics of knowledge resources and lack ability to store and query data in a machine-readable mode. Findings: The findings underline that the new citation analysis and service system framework based on ontology and linked data are feasible, which can integrate information requirements and knowledge services, and provide users with more personalized and comprehensive services

Webometrics benefitting from web mining? An investigation of methods and applications of two research fields

Webometrics and web mining are two fields where research is focused on quantitative analyses of the web. This literature review outlines definitions of the fields, and then focuses on their methods and applications. It also discusses the potential of closer contact and collaboration between them. A key difference between the fields is that webometrics has focused on exploratory studies, whereas web mining has been dominated by studies focusing on development of methods and algorithms. Differences in type of data can also be seen, with webometrics more focused on analyses of the structure of the web and web mining more focused on web content and usage, even though both fields have been embracing the possibilities of user generated content. It is concluded that research problems where big data is needed can benefit from collaboration between webometricians, with their tradition of exploratory studies, and web miners, with their tradition of developing methods and algorithms

A Meta-Study and Content Analysis of Science Fiction in Computer Science Research

Ph.D

Technologies to enhance self-directed learning from hypertext

With the growing popularity of the World Wide Web, materials presented to learners in the form of hypertext have become a major instructional resource. Despite the potential of hypertext to facilitate access to learning materials, self-directed learning from hypertext is often associated with many concerns. Self-directed learners, due to their different viewpoints, may follow different navigation paths, and thus they will have different interactions with knowledge. Therefore, learners can end up being disoriented or cognitively-overloaded due to the potential gap between what they need and what actually exists on the Web. In addition, while a lot of research has gone into supporting the task of finding web resources, less attention has been paid to the task of supporting the interpretation of Web pages. The inability to interpret the content of pages leads learners to interrupt their current browsing activities to seek help from other human resources or explanatory learning materials. Such activity can weaken learner engagement and lower their motivation to learn. This thesis aims to promote self-directed learning from hypertext resources by proposing solutions to the above problems. It first presents Knowledge Puzzle, a tool that proposes a constructivist approach to learn from the Web. Its main contribution to Web-based learning is that self-directed learners will be able to adapt the path of instruction and the structure of hypertext to their way of thinking, regardless of how the Web content is delivered. This can effectively reduce the gap between what they need and what exists on the Web. SWLinker is another system proposed in this thesis with the aim of supporting the interpretation of Web pages using ontology based semantic annotation. It is an extension to the Internet Explorer Web browser that automatically creates a semantic layer of explanatory information and instructional guidance over Web pages. It also aims to break the conventional view of Web browsing as an individual activity by leveraging the notion of ontology-based collaborative browsing. Both of the tools presented in this thesis were evaluated by students within the context of particular learning tasks. The results show that they effectively fulfilled the intended goals by facilitating learning from hypertext without introducing high overheads in terms of usability or browsing efforts

Contextual Social Networking

The thesis centers around the multi-faceted research question of how contexts may be detected and derived that can be used for new context aware Social Networking services and for improving the usefulness of existing Social Networking services, giving rise to the notion of Contextual Social Networking. In a first foundational part, we characterize the closely related fields of Contextual-, Mobile-, and Decentralized Social Networking using different methods and focusing on different detailed aspects. A second part focuses on the question of how short-term and long-term social contexts as especially interesting forms of context for Social Networking may be derived. We focus on NLP based methods for the characterization of social relations as a typical form of long-term social contexts and on Mobile Social Signal Processing methods for deriving short-term social contexts on the basis of geometry of interaction and audio. We furthermore investigate, how personal social agents may combine such social context elements on various levels of abstraction. The third part discusses new and improved context aware Social Networking service concepts. We investigate special forms of awareness services, new forms of social information retrieval, social recommender systems, context aware privacy concepts and services and platforms supporting Open Innovation and creative processes. This version of the thesis does not contain the included publications because of copyrights of the journals etc. Contact in terms of the version with all included publications: Georg Groh, [email protected] zentrale Gegenstand der vorliegenden Arbeit ist die vielschichtige Frage, wie Kontexte detektiert und abgeleitet werden können, die dazu dienen können, neuartige kontextbewusste Social Networking Dienste zu schaffen und bestehende Dienste in ihrem Nutzwert zu verbessern. Die (noch nicht abgeschlossene) erfolgreiche Umsetzung dieses Programmes führt auf ein Konzept, das man als Contextual Social Networking bezeichnen kann. In einem grundlegenden ersten Teil werden die eng zusammenhängenden Gebiete Contextual Social Networking, Mobile Social Networking und Decentralized Social Networking mit verschiedenen Methoden und unter Fokussierung auf verschiedene Detail-Aspekte näher beleuchtet und in Zusammenhang gesetzt. Ein zweiter Teil behandelt die Frage, wie soziale Kurzzeit- und Langzeit-Kontexte als für das Social Networking besonders interessante Formen von Kontext gemessen und abgeleitet werden können. Ein Fokus liegt hierbei auf NLP Methoden zur Charakterisierung sozialer Beziehungen als einer typischen Form von sozialem Langzeit-Kontext. Ein weiterer Schwerpunkt liegt auf Methoden aus dem Mobile Social Signal Processing zur Ableitung sinnvoller sozialer Kurzzeit-Kontexte auf der Basis von Interaktionsgeometrien und Audio-Daten. Es wird ferner untersucht, wie persönliche soziale Agenten Kontext-Elemente verschiedener Abstraktionsgrade miteinander kombinieren können. Der dritte Teil behandelt neuartige und verbesserte Konzepte für kontextbewusste Social Networking Dienste. Es werden spezielle Formen von Awareness Diensten, neue Formen von sozialem Information Retrieval, Konzepte für kontextbewusstes Privacy Management und Dienste und Plattformen zur Unterstützung von Open Innovation und Kreativität untersucht und vorgestellt. Diese Version der Habilitationsschrift enthält die inkludierten Publikationen zurVermeidung von Copyright-Verletzungen auf Seiten der Journals u.a. nicht. Kontakt in Bezug auf die Version mit allen inkludierten Publikationen: Georg Groh, [email protected]