Based upon findings and results from our recent research (De Vocht et al., 2011) we propose a generic framework concept for researcher profiling with appliance to the areas of ”Science 2.0” and ”Research 2.0”. Intensive growth of users in social networks, such as Twitter generated a vast amount of information. It has been shown in many previous works that social networks users produce valuable content for profiling and recommendations (Reinhardt et al., 2009; Java et al., 2007; De Vocht et al., 2011). Our research focuses on identifying and locating experts for specific research area or topic. In our approach we apply semantic technologies like (RDFb, SPARQLc), common vocabularies (SIOCd, FOAFe, MOATf, Tag Ontologyg) and Linked Datah (GeoNamesi, COLINDAj) (Berners-Lee, 2006; Bizer et al., 2012) .

This short survey represents a first step towards identifying relevant enterprise search engines as possible key enablers for challenges related to Future Workplace like knowledge transfer and individual and organisational information management inside an enterprise collaboration cycle. In the early period of our research we want to deliver a short overview on current state of the art solutions that can contribute to the idea of Future Workplace. We summarised in our literature study most significant parameters from current research on the topic of semantic and enterprise search. Information was collected via product sheets and white paper of the vendors as well using previous studies and accessible information at the web. Four most advanced solutions in this area has been evaluated. We used them to check their appliance for Future Workplace trends and are aiming at expansion of our review for additional solutions in the future.

Research information is widely available on the Web. Peerreviewed research publications as well related meta data from bibliography archives offers a vast of information to investigate about related publications, events and persons for a researcher. Usually the platforms supporting this information exchange have an API that allows access to the structured content or the information is already present as Linked Data. This opens a new way to search and explore research information. With the user interface of “ResXplorer” we help researchers to get an overview by using an approach that visualizes interactively search process in an aligned linked data knowledge base of research related resources. We show that visualizing resources, such as conferences, publications and proceedings, expose affinities between researchers and those resources. We characterize each affinity, between researchers and resources, by the amount of shared interests and other commonalities.

We report about work in progress on tracking the activities and trends of users from logs in a widget based Personal Learning Environment (PLE) using semantic technologies and standards for retrieval. As input for the observations, we are using the data from our self developed PLE with around 4000 active users. Last two years we logged their activities and modeled them with RDF (Resource Description Framework) as base for improvement analysis of existing system. The main objective of this work is to outline how learning environments like PLE can benefit from Semantic Web and its contribution for such efforts like analytics, profiling, recommendations and usability.

The concept of so called Thought Bubbles deals with the problem of finding appropriate new connections within Social Networks, especially Twitter. As a byproduct of exploring new users, Tweets are classified and rated and are used to generate a kind of news feed, which will extend the personal Twitter feed. Each user has several interests that can be classified by first evaluating their Tweets and then by evaluating user related and already existing contacts. By categorizing a user and related connections, one can be placed in an imaginary category specific subset of users, called Thought Bubbles. Following the trace of people who are also active within the same specific Thought Bubble, should reveal interesting and helpful connections between similar minded users.

This work reports about the preliminary results and ongoing research based upon profiling collaborative learning groups of persons within the social micro-blogging platforms like Twitter1 that share potentially common interests on special topic. Hereby the focus is held on spontaneously initiated collaborative learning in Social Media and detection of collaborative learning groups based upon their communication dynamics. Research questions targeted to be answered are: are there any useful data mining algorithms to fulfill the task of pre-selection and clustering of users in social networks, how good do they perform, and what are the metrics that could be used for detection and evaluation in the realm of this task. Basic approach presented here uses as preamble hypothesis that users and their interests in Social Networks can be identified through content generated by them and content they consume. Special focus is held on topic oriented approach as least common bounding point. Those should be also the basic criteria used to detect and outline the learning groups. The aim of this work is to deliver first scientific pre-work for successfully implementation of recommender systems using social network metrics and content features of social network users for the purposes of better learning group communication and information consumption.

We propose a framework to address an important issue in the context of the ongoing adoption of the "Web 2.0" in science and research, often referred to as "Science 2.0" or "Research 2.0". A growing number of people are linked via acquaintances and online social networks such as Twitter1allows indirect access to a huge amount of ideas. These ideas are contained in a massive human information flow [35]. That users of these networks produce relevant data is being shown in many studies [1][2][28][36]. The problem however lies in discovering and verifying such a stream of unstructured data items. Another related problem is locating an expert that could provide an answer to a very specific research question. We are using semantic technologies (RDF2, SPARQL3), common vocabularies(SIOC4, FOAF5, SWRC6) and Linked Data (DBpedia7, GeoNames8, CoLinDa9) [3][4][5] to extract and mine the data about scientific events out of context of microblogs. Hereby we are identifying persons and organization related to them based on entities of time, place and topic. The framework provides an API that allows quick access to the information that is analyzed by our system. As a proof-of-concept we explain, implement and evaluate such a researcher profiling use case. It involves the development of a framework that focuses on the proposition of researches based on topics and conferences they have in common. This framework provides an API that allows quick access to the analyzed information. A demonstration application: "Researcher Affinity Browser" shows how the API supports developers to build rich internet applications for Research 2.0. This application also introduces the concept "affinity" that exposes the implicit proximity between entities and users based on the content users produced. The usability of a demonstration application and the usefulness of the framework itself are investigated with an explicit evaluation questionnaire. This user feedback led to important conclusions about successful achievements and opportunities to further improve this effort.

In this paper we report the use of an application that enables an automatic analyses of social media content. In this early stage of development our work focuses on data from Twitter1 as currently to be the most popular and fastest growing microblogging platform. After an introduction about a general concept the conference tweets of a big e-learning conference are examined. It is aimed to show whether there is a possibility to get significant information from a pool of postings or not. The publication concludes that a keyword extraction can be taken as basis for further investigations and treatment of data.

The increasing availability of game based technologies together with advances in Human-Computer Interaction (HCI) and usability engineering provides new challenges and opportunities to virtual environments in the context of e-Teaching. Consequently, an evident trend is to offer learners with the equivalent of practical learning experiences, whilst supporting creativity for both teachers and learners. Current market surveys showed surprisingly that the Wii remote controller (Wiimote) is more widely spread than standard PCs and is the most used computer input device worldwide, which given its collection of sensors, accelerometers and bluetooth technology, makes it of great interest for HCI experiments in e-Learning/e-Teaching. In this paper we discuss the importance of gestures for teaching and describe the design and development of a low-cost demonstrator kit based on Wiimote enhancing the quality of the lecturing with gestures.

In this paper we report about our current and ongoi ng research efforts aiming at knowledge discovery, offline social data mining and social entity extraction based upon semantic technologies. Furthe r we are aiming to provide the scientific architecture paradigm for building s emantic applications that rely on social data. In this early stage our work focus es on data from Twitter 1 as currently most popular and fastest growing microblo gging platform. In the realm of our research we implemented applications l ike Grabeeter 2 for storing searching and caching the social data and STAT infr astructure that uses semantic standards like RDF (SIOC, FOAF), SPARQL and existing semantic services as Sinidice 3 and Linked Data silos as DBPedia 4 or GeoNames 5 as well. They represent parts of novel architecture paradigm for semantic social applications intended to be introduced here.

In this paper we present an approach for interlinking and RDFising social e-Learning Web 2.0 platforms like ELGG based on semantic tagging and Linked Data principles. A special module called SID (Semantically Interlinked Data) was developed to allow existing tagged and published user generated content an easy entrance into the Web of Data and to enrich it semantically on the other hand. Our approach uses commonly known vocabularies (FOAF, SIOC, MOAT and Tag Ontology) for modelling and generation tasks along with DBPedia as reference dataset for interlinking.

This paper reports on our ongoing work regarding opinion mining from Web-based discussion forums in the realm of the Understanding Advertising (UAd) project. Our approach to opinion mining is to first RDFise discussion forums in SIOC, and in a second phase to interlink the so created data with linked datasets such as DBpedia. We are confident that this should allow a market researcher to formulate queries using domain semantics and hence understand what people think about a certain product or service. The system’s architecture, preliminary results, and the current available demonstrator are discussed in this work.