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The merging procedures of two ontologies are mostly related to the enrichment of one of the input ontologies, i.e. the knowledge of the aligned concepts from one ontology are copied into the other ontology. As a consequence, the resulting new ontology extends the original knowledge of the base ontology, but the unaligned concepts of the other ontology are not considered in the new extended ontology. On the other hand, there are experts-aided semi-automatic approaches to accomplish the task of including the knowledge that is left out from the resulting merged ontology and debugging the possible concept redundancy. With the aim of facing the posed necessity of including all the knowledge of the ontologies to be merged without redundancy, this article proposes an automatic approach for merging ontologies, which is based on semantic similarity measures and exhaustive searching along of the closest concepts. The authors' approach was compared to other merging algorithms, and good results are obtained in terms of completeness, relationships and properties, without creating redundancy.

Electrical Transformers are complex devices that exhibit an enormous variability depending on the intended power transformation, environmental conditions, standards imposed and customer particularities. Incomplete information or inconsistencies in the specifications can lead to re-processes and higher bid times. This paper presents our experience on using multiple feature models to specify custom Electrical Transformer as a Configuration Process. This process facilitates the elicitation of knowledge from multidisciplinary experts using several feature models, one per domain and per standard and defining relationships among them. This separation of domains eases the analysis and validation of the models. To support the process, we have developed some tools to separate, merge and analyze these models. The final feature models are tested configuring and comparing products from existing company catalogs. We consider that the same strategy can be used in other contexts where experts on multiple disciplines participate.

In this paper we use concepts from graph theory and cellular biology represented as ontologies, to carry out semantic mining tasks on signaling pathway networks. Specifically, the paper describes the semantic enrichment of signaling pathway networks. A cell signaling network describes the basic cellular activities and their interactions. The main contribution of this paper is in the signaling pathway research area, it proposes a new technique to analyze and understand how changes in these networks may affect the transmission and flow of information, which produce diseases such as cancer and diabetes. Our approach is based on three concepts from graph theory (modularity, clustering and centrality) frequently used on social networks analysis. Our approach consists into two phases: the first uses the graph theory concepts to determine the cellular groups in the network, which we will call them communities; the second uses ontologies for the semantic enrichment of the cellular communities. The measures used from the graph theory allow us to determine the set of cells that are close (for example, in a disease), and the main cells in each community. We analyze our approach in two cases: TGF-ß and the Alzheimer Disease.

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