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Abstract. This paper conducts an empirical analysis of a conceptual model quality framework for evaluating the quality of process models. 194 participants were trained in the concepts of the quality framework, and then used it to evaluate models represented in a workflow modelling language. A randomised, double-blind design was used, and the results evaluated using a combination of quantitative and qualitative techniques. An analysis was also conducted of the framework's likelihood of adoption in practice, which is an issue rarely addressed in IS design research. The study provides strong support for the validity of the framework and suggests that it is likely to be adopted in practice, but raises questions about its reliability. The research findings provide clear direction for further research to improve the framework.

The overall goal addressed in this paper is to improve semantic interoperability in heterogeneous systems by means of establishing mappings between relevant domain ontologies. The mappings are discovered based on the technique of semantic enrichment through extension analysis, i.e. using instance information of the ontology to enrich the original ontology and further to calculate similarities between concepts in two ontologies. Text categorization is used to automatically assign instance to the concepts in the ontology. Information retrieval techniques are used to calculate similarity between concepts. Based on the similarities measure, a heuristic method is used to establish mapping assertions for the two ontologies. The method is illustrated using a product catalogue scenario.

Abstract. Ontology learning is the application of automatic tools to extract ontology concepts and relationships from domain text. Whereas ontology learning tools have been fairly successful in extracting concept candidates, it has proven difficult to detect relationships with the same level of accuracy. This paper discusses the use of association rules to extract relationships in the project management domain. We evaluate the results and compare them to another method based on tf.idf scores and cosine similarities. The findings confirm the usefulness of association rules, but also expose some interesting differences between association rules and cosine similarity methods in ontology relationship learning.

Knowledge management in general, and Internet-based knowledge management in particular, is one of the foremost strategic directions being investigated and adopted by corporations today. The promises of better decision making, faster turnaround times, improved organizational communication, and higher levels of cooperation and interaction among personnel, have all combined to create a holy grail kind of aura. Yet, like the grail the goals here are elusive, and the road to reaching them is long and fraught with pitfalls. Each of us, as individuals, performs a variety of functions that can be termed knowledge management. We remember things: names, numbers, experiences, and procedures. We know how to do things such as ride a bike; bake a cake; calculate a derivative; fix a flat tire. We know where to find information that we don’t remember on our own: we write things down; file them; enter them in a PDA. Some of us do it better, some of us are chronically disorganized, but at the end of the day each of us is performing his or her own knowledge management function.