Ontology has been recently proposed as an appropriate formalism to model software project management antipatterns, in order to encode antipatterns in a computer understandable form and introduce antipatterns to the Semantic Web. However, given two antipattern ontologies, the same entity can be described using different terminology. Therefore, the detection of similar antipattern ontologies is a difficult task. In this paper, we introduce a three-layered antipattern semantic social network, which involves the social network, the antipattern ontology network and the concept network. Social Network Analysis (SNA) techniques can be used to assist software project managers in finding similar antipattern ontologies. For this purpose, SNA measures are extracted from one layer of the semantic social network to another and this knowledge is used to infer new links between antipattern ontologies. The level of uncertainty associated with each new link is represented using Bayesian Networks (BNs). Furthermore, BNs address the issue of quantifying the uncertainty of the data collected regarding antipattern ontologies for the purposes of the conducted analysis. Finally, BNs are used to augment SNA by taking into account meta-information in their calculations. Hence, other knowledge not included in the social network can be used in order to search the social network for further inference. The benefits of using an antipattern semantic social network are illustrated using an example community of software project management antipattern ontologies.

The success of a software system strongly depends on the ability of turning a precise domain analysis into a concrete architecture. Even if the domain model relies on sound ontological bases, there is often a wide semantic gap between the conceptual model and the concrete components that should reify it. To fill the semantic gap, relevant domain concepts should be engineered by identifying the corresponding architectural abstractions, which can be realized by concrete software components. Space plays a crucial role in many application domains, but surprisingly, related architectural abstractions have not emerged yet. This paper proposes space-related abstractions derived from the application of classical software engineering principles; in particular, the information hiding principle that leads to an operational definition of space. Basic abstractions are refined to deal with architectural aspects. As the underlying software engineering principles are close to principles that underlie the definition of space ontologies, the conjecture is that the proposed space architectural abstractions might be the basis for a formalization in ontological terms.

Engineering knowledge is a specific kind of knowledge that is oriented to the production of particular classes of artifacts, is typically related to disciplined design methods, and takes place in tool-intensive contexts. As a consequence, representing engineering knowledge requires the elaboration of complex models that combine functional and structural representations of the resulting artifacts with process and methodological knowledge. The different categories used in the engineering domain vary in their status and in the way they should be manipulated when building applications that support engineering processes. These categories include artifacts, activities, methods and models. This paper surveys existing models of engineering knowledge and discusses an upper ontology that abstracts the categories that crosscut different engineering domains. Such an upper model can be reused for particular engineering disciplines. The process of creating such elaborations is reported on the particular case study of Software Engineering as a concrete application example.