Bibliography on Semantic social networks/Réseaux socio-sémantiques (2017-06-06)

Semantic social networks are social networks using ontologies for characterising resources shared within the network. It has been postulated that, in such networks, it is possible to discover social affinities between network members through measuring the similarity between the ontologies or part of ontologies they use. Using similar ontologies should reflect the cognitive disposition of the subjects. The main concern of this paper is the methodological aspect of experimenting in order to validate or invalidate such an hypothesis. Indeed, given the current lack of broad semantic social networks, it is difficult to rely on available data and experiments have to be designed from scratch. For that purpose, we first consider experimental settings that could be used and raise practical and methodological issues faced with analysing their results. We then describe a full experiments carried out according to some identified modalities and report the obtained results. The results obtained seem to invalidate the proposed hypothesis. We discuss why this may be so.

Relating ontologies is very important for many ontology-based applications and more important in open environments like the semantic web. The relations between ontology entities can be obtained by ontology matching and represented as alignments. Hence, alignments must be taken into account in ontology management. This chapter establishes the requirements for alignment management. After a brief introduction to matching and alignments, we justify the consideration of alignments as independent entities and provide the life cycle of alignments. We describe the important functions of editing, managing and exploiting alignments and illustrate them with existing components.

Computer manipulated social networks are usually built from the explicit assertion by users that they have some relation with other users or by the implicit evidence of such relations (e.g., co-authoring). However, since the goal of social network analysis is to help users to take advantage of these networks, it would be convenient to take more information into account. We introduce a three-layered model which involves the network between people (social network), the network between the ontologies they use (ontology network) and a network between concepts occurring in these ontologies. We explain how relationships in one network can be extracted from relationships in another one based on analysis techniques relying on this network specificity. For instance, similarity in the ontology network can be extracted from a similarity measure on the concept network. We illustrate the use of these tools for the emergence of consensus ontologies in the context of semantic peer-to-peer systems.

We have designed a three-layered model which involves the networks between people, the ontologies they use, and the concepts occurring in these ontologies. We propose how relationships in one network can be extracted from relationships in another one based on analysis techniques relying on this network specificity. For instance, similarity in the ontology layer can be extracted from a similarity measure on the concept layer.

We have been focusing on three-layered socialized semantic space, consisting of social, ontology, and concept layers. In this paper, we propose a new measurement of semantic centrality of people, meaning the power of semantic bridging, on this architecture. Thereby, the consensual ontologies are discovered by semantic alignment-based mining process in the ontology and concept layer. It is represented as the maximal semantic substructures among personal ontologies of semantically interlinked community. Finally, we have shown an example of semantic centrality applied to resource annotation on social network, and discussed our assumptions used in formulation of this measurement.