Network properties of folksonomies

Social resource sharing systems like YouTube and del.icio.us have acquired a large number of users within the last few years. They provide rich resources for data analysis, information retrieval, and knowledge discovery applications. A first step towards this end is to gain better insights into content and structure of these systems. In this paper, we will analyse the main network characteristics of two of the systems. We consider their underlying data structures – socalled folksonomies – as tri-partite hypergraphs, and adapt classical network measures like characteristic path length and clustering coefficient to them. Subsequently, we introduce a network of tag co-occurrence and investigate some of its statistical properties, focusing on correlations in node connectivity and pointing out features that reflect emergent semantics within the folksonomy. We show that simple statistical indicators unambiguously spot non-social behavior such as spam.

Network properties of folksonomies

Social resource sharing systems like YouTube and del.icio.us have acquired a large number of users within the last few years. They provide rich resources for data analysis, information retrieval, and knowledge discovery applications. A first step towards this end is to gain better insights into content and structure of these systems. In this paper, we will analyse the main network characteristics of two of these systems. We consider their underlying data structures – so-called folksonomies – as tri-partite hypergraphs, and adapt classical network measures like characteristic path length and clustering coefficient to them. Subsequently, we introduce a network of tag cooccurrence and investigate some of its statistical properties, focusing on correlations in node connectivity and pointing out features that reflect emergent semantics within the folksonomy. We show that simple statistical indicators unambiguously spot non-social behavior such as spam.

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.

UEBERflow

In der Dissertation wird der Frage nachgegangen, welche globalen bildungspolitischen Maßnahmen erforderlich sind, um auch bislang exkludierten Menschen den Kompetenzerwerb zu ermöglichen, der benötigt wird, eine positive User Experience in benutzergenerierten, digitalen Lernumgebungen auszubilden, damit sie an der modernen Weltgesellschaft selbstbestimmt teilhaben können. Zu diesem Zweck wurden Castells ‘Netzwerkgesellschaft’ und Csikszentmihalys ‘Theorie der optimalen Erfahrung’ als analytische Grundlagen zur Einordnung der sozialen Netzwerk-Aktivitäten herangezogen. Dies ermöglichte es, unter Rückgriff auf aktuelle Lerntheorien, Kompetenzdebatten, ökonomische Analysen des Bildungssystems und User Experience-Forschungen, einige individuelle und gesamtgesellschaftliche Voraussetzungen abzuleiten, um in der Netzwerkgesellschaft konstruktiv überleben zu können. Mit Blick auf unterschiedliche sozio-kulturelle Bedingungen für persönlichen Flow im ‘space of flows’ liessen sich schließlich differenzierte Flow-Kriterien entwickeln, die als Grundlage für die Operationalisierung im Rahmen einer Real-Time Delphi (RTD)-Studie mit einem internationalen Expertinnen-Panel dienen konnten. Ziel war es, bildungspolitische Ansatzpunkte zu finden, den bislang Exkludierten bis zum Jahre 2020 erste Rahmenbedingungen zu bieten, damit sie potentiell teilhaben können an der Gestaltung der zukünftigen Netzwerkgesellschaft. Das Ergebnis der Expertinnen-Befragung wurde unter Rückgriff auf aktuelle Global und Educational Governance-Studien und das Einflusspotenzial der Zivilgesellschaft auf den Digital Divide reflektiert. Vor diesem Hintergrund konnten abschließend vier bildungspolitische Verlaufsszenarien entworfen werden, die es ermöglichen könnten, bis 2020 die Kluft zu den global Exkludierten wenigstens etwas zu schließen.