Web Intelligence, Mining and Semantics 

Abstract: The document-centric workflows in science have reached (or already exceeded) the limits of adequacy. This is emphasized by recent discussions on the increasing proliferation of scientific literature and the reproducibility crisis. This presents an opportunity to rethink the dominant paradigm of document-centric scholarly information communication and transform it into knowledge-based information flows by representing and expressing information through semantically rich, interlinked knowledge graphs. At the core of knowledge-based information flows is the creation and evolution of information models that establish a common understanding of information communicated between stakeholders as well as the integration of these technologies into the infrastructure and processes of search and information exchange in the research library of the future. By integrating these models into existing and new research infrastructure services, the information structures that are currently still implicit and deeply hidden in documents can be made explicit and directly usable. This has the potential to revolutionize scientific work as information and research results can be seamlessly interlinked with each other and better matched to complex information needs. Furthermore, research results become directly comparable and easier to reuse. As our main contribution, we propose the vision of a knowledge graph for science, present a possible infrastructure for such a knowledge graph as well as our early attempts towards an implementation of the infrastructure.

Abstract: We describe our work in the collection and analysis of massive data describing the connections between participants to online social networks. Alternative approaches to social network data collection are defined and evaluated in practice, against the popular Facebook Web site. Thanks to our ad-hoc, privacy-compliant crawlers, two large samples, comprising millions of connections, have been collected; the data is anonymous and organized as an undirected graph. We describe a set of tools that we developed to analyze specific properties of such social-network graphs, i.e., among others, degree distribution, centrality measures, scaling laws and distribution of friendship.

Abstract: This work introduces an ontology for incorporating Ambient Intelligence in Smart Buildings. The ontology extends and benefits from existing ontologies in the field, but also adds classes needed to sufficiently model every aspect of a service-oriented smart building system. Namely, it includes concepts modeling all functionality (i.e. services, operations, inputs, outputs, logic, parameters and environmental conditions), QoS (resources, QoS parameters), hardware (smart devices, sensors and actuators, appliances, servers) users and context (user profiles, moods, location, rooms etc.). The ontology is instantiated and put to use at the Smart Building setting of the International Hellenic University, enabling knowledge representation in machine-interpretable form and hence is expected to enhance service-based intelligent applications.

Abstract: The technology of the Internet of Things(IoT) has flourished in various industries and market around the world. The agricultural environment is one of the areas to benefit from IoT technologies. LoRa is one of the most used network radios in the IoT network technology infrastructure. LoRa is often used in agricultural IoT not only for its long-range but also due to very low power usage and significant cost advantage. This paper provides a study of LoRa networks in IoT technology in an agricultural environment. The study has been conducted for between a LoRa gateway and multiple sensor nodes. Due to the radio propagation issues encountered with forestry and other types of agriculture, we used a small drone with a gateway mounted on its fuselage that collects sensor data from nodes on the ground to test the feasibility of this communications method. The scenario tested to figure out whether the gateway attached on a drone that hovers over farm field can gather data from nodes on the ground. The aim of this project is to help farmers get environmental data over the geographically large farm field, and also the locations where are difficult or dangerous for them to access. The research took in a place in a tree farm near Purdue University, Indiana.