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.

Quality Criteria for Enterprise Modelling in the context of networked Enterprises

The capability for collaboration is a key success factor for networked enterprises. The paper introduces a methodology supporting the application of Enterprise Modelling in order to improve the maturity for collaboration. The methodology considers the current status of maturity for interoperability for deducing the right modelling approach. The approach is combined with quality criteria of the models in order to guide the modelling process. Both the deducing approach and the quality criteria are related to the levels of interoperability proposed by the ATHENA Interoperability Framework.

Farmers’ desired traits and selection criteria for maize varieties and their implications for maize breeding: A case study from KwaZulu-Natal Province, South Africa

Adoption of hybrids and improved varieties has remained low in the smallholder farming sector of South Africa, despite maize being the staple food crop for the majority of households. The objective of this study was to establish preferred maize characteristics by farmers which can be used as selection criteria by maize breeders in crop improvement. Data were collected from three villages of a selected smallholder farming area in South Africa using a survey covering 300 households and participatory rural appraisal methodology. Results indicated a limited selection of maize varieties grown by farmers in the area compared to other communities in Africa. More than 97% of the farmers grew a local landrace called Natal-8-row or IsiZulu. Hybrids and improved open pollinated varieties were planted by less than 40% of the farmers. The Natal-8-row landrace had characteristics similar to landraces from eastern and southern Africa and closely resembled Hickory King, a landrace still popular in Southern Africa. The local landrace was preferred for its taste, recycled seed, tolerance to abiotic stresses and yield stability. Preferred characteristics of maize varieties were high yield and prolificacy, disease resistance, early maturity, white grain colour, and drying and shelling qualities. Farmers were willing to grow hybrids if the cost of seed and other inputs were affordable and their preferences were considered. Our results show that breeding opportunities exist for improving the farmers’ local varieties and maize breeders can take advantage of these preferred traits and incorporate them into existing high yielding varieties.