Capturing Emergent Semantics from Social Annotation Systems

The ongoing growth of the World Wide Web, catalyzed by the increasing possibility of ubiquitous access via a variety of devices, continues to strengthen its role as our prevalent information and commmunication medium. However, although tools like search engines facilitate retrieval, the task of finally making sense of Web content is still often left to human interpretation. The vision of supporting both humans and machines in such knowledge-based activities led to the development of different systems which allow to structure Web resources by metadata annotations. Interestingly, two major approaches which gained a considerable amount of attention are addressing the problem from nearly opposite directions: On the one hand, the idea of the Semantic Web suggests to formalize the knowledge within a particular domain by means of the "top-down" approach of defining ontologies. On the other hand, Social Annotation Systems as part of the so-called Web 2.0 movement implement a "bottom-up" style of categorization using arbitrary keywords. Experience as well as research in the characteristics of both systems has shown that their strengths and weaknesses seem to be inverse: While Social Annotation suffers from problems like, e. g., ambiguity or lack or precision, ontologies were especially designed to eliminate those. On the contrary, the latter suffer from a knowledge acquisition bottleneck, which is successfully overcome by the large user populations of Social Annotation Systems. Instead of being regarded as competing paradigms, the obvious potential synergies from a combination of both motivated approaches to "bridge the gap" between them. These were fostered by the evidence of emergent semantics, i. e., the self-organized evolution of implicit conceptual structures, within Social Annotation data. While several techniques to exploit the emergent patterns were proposed, a systematic analysis - especially regarding paradigms from the field of ontology learning - is still largely missing. This also includes a deeper understanding of the circumstances which affect the evolution processes. This work aims to address this gap by providing an in-depth study of methods and influencing factors to capture emergent semantics from Social Annotation Systems. We focus hereby on the acquisition of lexical semantics from the underlying networks of keywords, users and resources. Structured along different ontology learning tasks, we use a methodology of semantic grounding to characterize and evaluate the semantic relations captured by different methods. In all cases, our studies are based on datasets from several Social Annotation Systems. Specifically, we first analyze semantic relatedness among keywords, and identify measures which detect different notions of relatedness. These constitute the input of concept learning algorithms, which focus then on the discovery of synonymous and ambiguous keywords. Hereby, we assess the usefulness of various clustering techniques. As a prerequisite to induce hierarchical relationships, our next step is to study measures which quantify the level of generality of a particular keyword. We find that comparatively simple measures can approximate the generality information encoded in reference taxonomies. These insights are used to inform the final task, namely the creation of concept hierarchies. For this purpose, generality-based algorithms exhibit advantages compared to clustering approaches. In order to complement the identification of suitable methods to capture semantic structures, we analyze as a next step several factors which influence their emergence. Empirical evidence is provided that the amount of available data plays a crucial role for determining keyword meanings. From a different perspective, we examine pragmatic aspects by considering different annotation patterns among users. Based on a broad distinction between "categorizers" and "describers", we find that the latter produce more accurate results. This suggests a causal link between pragmatic and semantic aspects of keyword annotation. As a special kind of usage pattern, we then have a look at system abuse and spam. While observing a mixed picture, we suggest that an individual decision should be taken instead of disregarding spammers as a matter of principle. Finally, we discuss a set of applications which operationalize the results of our studies for enhancing both Social Annotation and semantic systems. These comprise on the one hand tools which foster the emergence of semantics, and on the one hand applications which exploit the socially induced relations to improve, e. g., searching, browsing, or user profiling facilities. In summary, the contributions of this work highlight viable methods and crucial aspects for designing enhanced knowledge-based services of a Social Semantic Web.

Towards semantic web mining

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. The idea is to improve, on the one hand, the results of Web Mining by exploiting the new semantic structures in the Web; and to make use of Web Mining, on overview of where the two areas meet today, and sketches ways of how a closer integration could be profitable.

Semantic web mining and the representation, analysis, and evolution of web space

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. This survey analyzes the convergence of trends from both areas: Growing numbers of researchers work on improving the results of Web Mining by exploiting semantic structures in the Web, and they use Web Mining techniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic Web itself. The second aim of this paper is to use these concepts to circumscribe what Web space is, what it represents and how it can be represented and analyzed. This is used to sketch the role that Semantic Web Mining and the software agents and human agents involved in it can play in the evolution of Web space.

Semantic web mining

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. This survey analyzes the convergence of trends from both areas: an increasing number of researchers is working on improving the results of Web Mining by exploiting semantic structures in the Web, and they make use of Web Mining techniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic Web itself. The Semantic Web is the second-generation WWW, enriched by machine-processable information which supports the user in his tasks. Given the enormous size even of today’s Web, it is impossible to manually enrich all of these resources. Therefore, automated schemes for learning the relevant information are increasingly being used. Web Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily syntactical nature of the data being mined, the discovery of meaning is impossible based on these data only. Therefore, formalizations of the semantics of Web sites and navigation behavior are becoming more and more common. Furthermore, mining the Semantic Web itself is another upcoming application. We argue that the two areas Web Mining and Semantic Web need each other to fulfill their goals, but that the full potential of this convergence is not yet realized. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer integration could be profitable.

Semantic network analysis of ontologies

A key argument for modeling knowledge in ontologies is the easy re-use and re-engineering of the knowledge. However, beside consistency checking, current ontology engineering tools provide only basic functionalities for analyzing ontologies. Since ontologies can be considered as (labeled, directed) graphs, graph analysis techniques are a suitable answer for this need. Graph analysis has been performed by sociologists for over 60 years, and resulted in the vivid research area of Social Network Analysis (SNA). While social network structures in general currently receive high attention in the Semantic Web community, there are only very few SNA applications up to now, and virtually none for analyzing the structure of ontologies. We illustrate in this paper the benefits of applying SNA to ontologies and the Semantic Web, and discuss which research topics arise on the edge between the two areas. In particular, we discuss how different notions of centrality describe the core content and structure of an ontology. From the rather simple notion of degree centrality over betweenness centrality to the more complex eigenvector centrality based on Hermitian matrices, we illustrate the insights these measures provide on two ontologies, which are different in purpose, scope, and size.

Lehrende als ‘agents of change’ - Konzeptionelle Überlegungen zu einer kinderrechtlichen Annotation und Ausdeutung der Standards für die Lehrerbildung

Die Dissertation geht von der Überlegung aus, dass ein Wandel im deutschen Bildungssystem aufgrund kinderrechtlicher und demokratischer Überlegungen eine essenzielle Zukunftsaufgabe ist. Dies geschieht im Besonderen vor dem Hintergrund kinderrechtlicher Forderungen, wie sie beispielsweise aus dem Status Deutschlands als Mitgliedsland der UN-Kinderrechtskonvention und des UN-Übereinkommens über die Rechte von Menschen mit Behinderung entstehen: Die Korrelation zwischen Herkunft und Bildungserfolg sowie die noch konzeptionell ungenügend entwickelten Ansätze zur Inklusion werden als bestimmend für die Diskussion erkannt. Die Arbeit vertritt die These, dass Lehrende eine zentrale Rolle in einem notwendigen Prozess der Umwandlung des deutschen Bildungssystems spielen können und nimmt in emanzipatorischer Absicht die Lehrenden als Zielgruppe in den Blick. Aufgrund der Struktur der Lehrendenausbildung wird zudem dafür argumentiert, entsprechende Veränderungen im Sinne ihrer Nachhaltigkeit und flächendeckenden Relevanz in der Lehrendenausbildung des ersten universitären Ausbildungsabschnittes zu verankern. Als Instrument, welches die Kriterien von Nachhaltigkeit und umfassender Relevanz erfüllt, werden die Standards für die Lehrerbildung im Bereich Bildungswissenschaften benannt und theoretisch unterfüttert. Grundsätzlich spannt sich der Blickwinkel der Arbeit zwischen der Betrachtung der Struktur des Bildungssystems, des Wissens und Könnens von Lehrenden und Lernenden im Sinne der Kompetenzorientierung sowie der Haltung auf, welche Lehrende einnehmen. Dabei wird aufgezeigt, dass sich diese Faktoren wechselseitig auf vielfache Art und Weise beeinflussen. Auf der Basis dieser Überlegungen wird zunächst das deutsche Bildungssystem in seinen Strukturen beschrieben und die Lehrenden und Lernenden als Teilnehmende am Bildungsgeschehen skizziert. Ein exkursiver Vergleich dreier aufgrund kinderrechtlicher Parameter ausgewählter europäischer Bildungssysteme eruiert zudem, inwiefern die aufgezeigten Bestimmungsgrößen Struktur, Wissen/Können und Haltung Einfluss auf kinderrechtliche Verfasstheit des Bildungssystems nehmen, wobei die Grundprinzipien der UN-Kinderrechtskonvention hier als Messinstrument dienen. Davon ausgehend, erscheint pädagogische Haltung als eine wesentliche Einflussgröße, die im weiteren Verlauf der Arbeit im Sinne der kritischen Pädagogik zu einer Konzeption Pädagogischer Verantwortung verdichtet wird. Vor diesem Hintergrund erfolgt eine Betrachtung aktueller Problematiken im deutschen Bildungssystem, die sich an den in den Standards für die Lehrerbildung identifizierten Ausbildungsschwerpunkten orientiert und erneut die Prinzipien und rechtlichen Vorgaben der UN-Kinderrechtskonvention als Maßstab wählt. Auf der Grundlage dieser umfassenden Diskussion werden in einem sich anschließenden Analyseschritt Annotationen vorgenommen, die die Formulierungen der 11 Standards für die Lehrerbildung inhaltlich erweitern und ergänzen. In Verbindung mit einer Pädagogischen Verantwortung, die sich der Ausbildung kritischer Mündigkeit verpflichtet, werden die annotierten Standards als eine Möglichkeit der kinderrechtlichen (Selbst-)Evaluation von Lehrenden sowie als Instrument einer kinderrechtliche Gesichtspunkte fokussierenden Lehrendenbildung verstanden und dargestellt.