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.