Semantic modelling of user interests based on cross-folksonomy analysis

The continued increase in Web usage, in particular participation in folksonomies, reveals a trend towards a more dynamic and interactive Web where individuals can organise and share resources. Tagging has emerged as the de-facto standard for the organisation of such resources, providing a versatile and reactive knowledge management mechanism that users find easy to use and understand. It is common nowadays for users to have multiple profiles in various folksonomies, thus distributing their tagging activities. In this paper, we present a method for the automatic consolidation of user profiles across two popular social networking sites, and subsequent semantic modelling of their interests utilising Wikipedia as a multi-domain model. We evaluate how much can be learned from such sites, and in which domains the knowledge acquired is focussed. Results show that far richer interest profiles can be generated for users when multiple tag-clouds are combine

Predicting discussions on the social semantic web

Social Web platforms are quickly becoming the natural place for people to engage in discussing current events, topics, and policies. Analysing such discussions is of high value to analysts who are interested in assessing up-to-the-minute public opinion, consensus, and trends. However, we have a limited understanding of how content and user features can influence the amount of response that posts (e.g., Twitter messages) receive, and how this can impact the growth of discussion threads. Understanding these dynamics can help users to issue better posts, and enable analysts to make timely predictions on which discussion threads will evolve into active ones and which are likely to wither too quickly. In this paper we present an approach for predicting discussions on the Social Web, by (a) identifying seed posts, then (b) making predictions on the level of discussion that such posts will generate. We explore the use of post-content and user features and their subsequent e!ects on predictions. Our experiments produced an optimum F1 score of 0.848 for identifying seed posts, and an average measure of 0.673 for Normalised Discounted Cumulative Gain when predicting discussion levels

Modeling Service Relationships for Service Networks

Abstract. The last decade has seen an increased interest in the study of networks in many fields of science. Examples are numerous, from sociol-ogy to biology, and to physical systems such as power grids. Nonetheless, the field of service networks has received less attention. Previous research has mainly tackled the modeling of single service systems and service compositions, often focusing only on studying temporal relationships be-tween services. The objective of this paper is to propose a computational model to represent the various types of relationships which can be es-tablished between services systems to model service networks. This work acquires a particular importance since the study of service networks can bring new scientific discoveries on how service-based economies operate at a global scale. Key words: service relationship, service system, business service, open service, service network, semantic Web.