Kollaboratives Wissensmanagement

Wissensmanagement in zentralisierten Wissensbasen erfordert einen hohen Aufwand für Erstellung und Wartung, und es entspricht nicht immer den Anforderungen der Benutzer. Wir geben in diesem Kapitel einen Überblick über zwei aktuelle Ansätze, die durch kollaboratives Wissensmanagement diese Probleme lösen können. Im Peer-to-Peer-Wissensmanagement unterhalten Benutzer dezentrale Wissensbasen, die dann vernetzt werden können, um andere Benutzer eigene Inhalte nutzen zu lassen. Folksonomies versprechen, die Wissensakquisition so einfach wie möglich zu gestalten und so viele Benutzer in den Aufbau und die Pflege einer gemeinsamen Wissensbasis einzubeziehen.

Lost in Research - Wissensmanagement in komplexen (psychoanalytischen) Forschungsprojekten

Die Wissenschaft weist im Zuge der Entwicklung von der Industrie- zu einer Wissensgesellschaft einschneidende Veränderungen in der Wissensordnung auf, welche sich bis hin zu einem zunehmenden Verlust der wissenschaftlichen Selbststeuerungsmechanismen bemerkbar machen und einen veränderten Umgang mit dem generierten Wissensschatz erfordern. Nicht nur Änderungen in der Wissensordnung und -produktion stellen die Psychoanalyse vor neue Herausforderungen: In den letzten Jahrzehnten geriet sie als Wissenschaft und Behandlungsverfahren zunehmend in die Kritik und reagierte mit einer konstruktiven Diskussion um ein dem Forschungsgegenstand – die Untersuchung unbewusster Prozesse und Fantasien – adäquates psychoanalytisches Forschungsverständnis. Die Auseinandersetzung mit Forderungen gesellschaftlicher Geldgeber, politischer Vertreter und Interessensgruppen wie auch der wissenschaftlichen Community stellt die Psychoanalyse vor besondere Herausforderungen. Um wissenschaftsexternen wie -internen Gütekriterien zu genügen, ist häufig ein hoher personeller, materieller, finanzieller, methodischer wie organisatorischer Aufwand unabdingbar, wie das Beispiel des psychoanalytischen Forschungsinstitutes Sigmund-Freud-Institut zeigt. Der steigende Aufwand schlägt sich in einer zunehmenden Komplexität des Forschungsprozesses nieder, die unter anderem in den vielschichtigen Fragestellungen und Zielsetzungen, dem vermehrt interdisziplinären, vernetzten Charakter, dem Umgang mit dem umfangreichen, hochspezialisierten Wissen, der Methodenvielfalt, etc. begründet liegt. Um jener Komplexität des Forschungsprozesses gerecht zu werden, ist es zunehmend erforderlich, Wege des Wissensmanagement zu beschreiten. Tools wie z. B. Mapping-Verfahren stellen unterstützende Werkzeuge des Wissensmanagements dar, um den Herausforderungen des Forschungsprozesses zu begegnen. In der vorliegenden Arbeit werden zunächst die veränderten Forschungsbedingungen und ihre Auswirkungen auf die Komplexität des Forschungsprozesses - insbesondere auch des psychoanalytischen Forschungsprozesses - reflektiert. Die mit der wachsenden Komplexität einhergehenden Schwierigkeiten und Herausforderungen werden am Beispiel eines interdisziplinär ausgerichteten EU-Forschungsprojektes näher illustriert. Um dieser wachsenden Komplexität psychoanalytischer Forschung erfolgreich zu begegnen, wurden in verschiedenen Forschungsprojekten am Sigmund-Freud-Institut Wissensmanagement-Maßnahmen ergriffen. In der vorliegenden Arbeit wird daher in einem zweiten Teil zunächst auf theoretische Aspekte des Wissensmanagements eingegangen, die die Grundlage der eingesetzten Wissensmanagement-Instrumente bildeten. Dabei spielen insbesondere psychologische Aspekte des Wissensmanagements eine zentrale Rolle. Zudem werden die konkreten Wissensmanagement-Tools vorgestellt, die in den verschiedenen Forschungsprojekten zum Einsatz kamen, um der wachsenden Komplexität psychoanalytischer Forschung zu begegnen. Abschließend werden die Hauptthesen der vorliegenden Arbeit noch einmal reflektiert und die geschilderten Techniken des Wissensmanagements im Hinblick auf ihre Vor- und Nachteile kritisch diskutiert.

Wege zur Entdeckung von Communities in Folksonomies

Ein wichtiger Baustein des neu entdeckten World Wide Web - des "Web 2.0" - stellen Folksonomies dar. In diesen Systemen können Benutzer gemeinsam Ressourcen verwalten und mit Schlagwörtern versehen. Die dadurch entstehenden begrifflichen Strukturen stellen ein interessantes Forschungsfeld dar. Dieser Artikel untersucht Ansätze und Wege zur Entdeckung und Strukturierung von Nutzergruppen ("Communities") in Folksonomies.

Trend detection in folksonomies

As the number of resources on the web exceeds by far the number of documents one can track, it becomes increasingly difficult to remain up to date on ones own areas of interest. The problem becomes more severe with the increasing fraction of multimedia data, from which it is difficult to extract some conceptual description of their contents. One way to overcome this problem are social bookmark tools, which are rapidly emerging on the web. In such systems, users are setting up lightweight conceptual structures called folksonomies, and overcome thus the knowledge acquisition bottleneck. As more and more people participate in the effort, the use of a common vocabulary becomes more and more stable. We present an approach for discovering topic-specific trends within folksonomies. It is based on a differential adaptation of the PageRank algorithm to the triadic hypergraph structure of a folksonomy. The approach allows for any kind of data, as it does not rely on the internal structure of the documents. In particular, this allows to consider different data types in the same analysis step. We run experiments on a large-scale real-world snapshot of a social bookmarking system.

Content aggregation on knowledge bases using graph clustering

Recently, research projects such as PADLR and SWAP have developed tools like Edutella or Bibster, which are targeted at establishing peer-to-peer knowledge management (P2PKM) systems. In such a system, it is necessary to obtain provide brief semantic descriptions of peers, so that routing algorithms or matchmaking processes can make decisions about which communities peers should belong to, or to which peers a given query should be forwarded. This paper proposes the use of graph clustering techniques on knowledge bases for that purpose. Using this clustering, we can show that our strategy requires up to 58% fewer queries than the baselines to yield full recall in a bibliographic P2PKM scenario.

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.

Integrated soil fertility management in eastern and western Africa: The role of knowledge and innovation systems, its adoption and impact

The rise in population growth, as well as nutrient mining, has contributed to low agricultural productivity in Sub-Saharan Africa (SSA). A plethora of technologies to boost agricultural production have been developed but the dissemination of these agricultural innovations and subsequent uptake by smallholder farmers has remained a challenge. Scientists and philanthropists have adopted the Integrated Soil Fertility Management (ISFM) paradigm as a means to promote sustainable intensification of African farming systems. This comparative study aimed: 1) To assess the efficacy of Agricultural Knowledge and Innovation Systems (AKIS) in East (Kenya) and West (Ghana) Africa in the communication and dissemination of ISFM (Study I); 2) To investigate how specifically soil quality, and more broadly socio-economic status and institutional factors, influence farmer adoption of ISFM (Study II); and 3) To assess the effect of ISFM on maize yield and total household income of smallholder farmers (Study III). To address these aims, a mixed methodology approach was employed for study I. AKIS actors were subjected to social network analysis methods and in-depth interviews. Structured questionnaires were administered to 285 farming households in Tamale and 300 households in Kakamega selected using a stratified random sampling approach. There was a positive relationship between complete ISFM awareness among farmers and weak knowledge ties to both formal and informal actors at both research locations. The Kakamega AKIS revealed a relationship between complete ISFM awareness among farmers and them having strong knowledge ties to formal actors implying that further integration of formal actors with farmers’ local knowledge is crucial for the agricultural development progress. The structured questionnaire was also utilized to answer the query pertaining to study II. Soil samples (0-20 cm depth) were drawn from 322 (Tamale, Ghana) and 459 (Kakamega, Kenya) maize plots and analysed non-destructively for various soil fertility indicators. Ordinal regression modeling was applied to assess the cumulative adoption of ISFM. According to model estimates, soil carbon seemed to preclude farmers from intensifying input use in Tamale, whereas in Kakamega it spurred complete adoption. This varied response by farmers to soil quality conditions is multifaceted. From the Tamale perspective, it is consistent with farmers’ tendency to judiciously allocate scarce resources. Viewed from the Kakamega perspective, it points to a need for farmers here to intensify agricultural production in order to foster food security. In Kakamega, farmers with more acidic soils were more likely to adopt ISFM. Other household and farm-level factors necessary for ISFM adoption included off-farm income, livestock ownership, farmer associations, and market inter-linkages. Finally, in study III a counterfactual model was used to calculate the difference in outcomes (yield and household income) of the treatment (ISFM adoption) in order to estimate causal effects of ISFM adoption. Adoption of ISFM contributed to a yield increase of 16% in both Tamale and Kakamega. The innovation affected total household income only in Tamale, where ISFM adopters had an income gain of 20%. This may be attributable to the different policy contexts under which the two sets of farmers operate. The main recommendations underscored the need to: (1) improve the functioning of AKIS, (2) enhance farmer access to hybrid maize seed and credit, (3) and conduct additional multi-locational studies as farmers operate under varying contexts.