Enabling Scalable Multi-channel Communication through Semantic Technologies

With the advance of the Web in the direction Social Media the number of communication possibilities has exponentially increased bringing new challenges and opportunities for companies to build and shape their reputation online as well as to engage and maintain the relationships to their customers. In this paper we describe how semantic technologies enable scalable, effective and efficient on-line communication. We illustrate four different ways in which semantics can be used for this purpose. First, we discuss semantic analysis of communication items based on 'classical' semantic, such as natural language processing. Second, we look at semantics as a channel, viewing Linked Open Data vocabularies not only as terminological assets but as communication channels. Third, semantics provide the methodologies and tools for content modeling by means of ontologies. Finally, semantics through semantic matchmaking enable semi-automatic assignment and distribution of content to channels and vice-versa

An approach for evaluation of social media monitoring tools

Social networks are important means for communication, engaging millions of users around the globe. For enterprises in particular, being present and aware of what is discussed on these communication channels about their products and services has become a must. Social media monitoring tools enable enterprises to have access to real customers' opinions, complaints and questions at real time in a highly scalable way. As the number of social monitoring tools has rapidly increased in the last years, enterprises are faced with the difficult tasks of choosing the right tool for their needs. This paper proposes a structured evaluation framework comprising a set of evaluation criteria that can be used to analyze social monitoring tools from three perspectives: the concepts they implement, the technologies used and the user interface they provide. To exemplify the usefulness of our evaluation framework we analyze a set of social monitoring tools after briefly describing them