Making and implementing policy at any level of government is fraught with difficulty. The impact of decisions made are not always obvious at the time the policy is formulated or enacted, and any short-comings of the policy become known too late to change it. This is not due to a lack of information, it is due to the difficulty of finding and aggregating the right data out of the sea of information which characterises our modern world. Having once formulated a policy it is then impossible to make useful predictions around its likely impact and effectiveness. Policy specialists lack the resources and the methodology to be able to access most current data and are unable to take into account the views of citizens on policy issues expressed in real time through social network discussions. SENSE4US is creating an integrated package of utilities based on cutting-edge research that meets this need for tools and techniques to support information gathering, analysing and policy modelling in real time. Through close interaction with policy makers around Europe the project will validate results in complex policy-making settings and direct the research towards the support of more timely, more effective and better understood policy creation. The SENSE4US project will tackle these challenges of policy making and implementation, integrating the benefits of both quantitative open data sources and qualitative social media data. We will provide tools enabling policy makers to find and select relevant information; link and homogenise the data; model policy in terms of constraints and intent; validate the policy; discover and incorporate views from NGOs and public; predict social impact of policy; provide decision support; provide understandable visualisation. The ultimate objective of the SENSE4US project is to advance policy modelling and simulation, data analytics and social network discussion dynamics, providing economic and social benefits at all governmental levels across Europe.
Source of funding:
I have studied computer science and computational linguistics at the Universität Erlangen-Nürnberg and at the University of Pennsylvania. I worked in the previous computational linguistics research group at the Universität Freiburg and did my Ph.D. in computer science in the faculty for technology in 1998. Afterwards I joined Universität Stuttgart, Institute IAT & Fraunhofer IAO, before I moved on to the Universität Karlsruhe (now: KIT), where I progressed from project lead, over lecturer and senior lecturer and did my habilitation in 2002. In 2004 I became professor for databases and information systems at Universität Koblenz-Landau, where I founded the Institute for Web Science and Technologies (WeST) in 2009. In parallel, I hold a Chair for Web and Computer Science at University of Southampton since March 2015.
Data represent the world on our computers. While the world is very intriguing, data may be quite boring, if one does not know what they mean. I am interested in making data more meaningful to find interesting insights in the world outside.
How does meaning arise?
- One can model data and information. Conceptual models and ontologies are the foundations for knowledge networks that enable the computer to treat data in a meaningful way.
- Text and data mining as well as information extraction find meaningful patterns in data (e.g. using ontology learning of text clustering) as well as connections between data and its use in context (e.g. using smartphones). Hence, knowledge networks may be found in data.
- Humans communicate information. In order to understand what data and information means, one has to understand social interactions. In the context of social network knowledge networks become meaningful for human consumption.
- Eventually meaning is nothing that exists in the void. Data and information must be communicated to people who may use insights into data and information. Interaction between humans and computers must happen in a way that matches the meaning of data and information.
The World Wide Web is the largest information construct made by mankind to convey meaningful data. Web Science is the discipline that considers how networks of people and knowledge in the Web arise, how humans deal with it and which consequences this has for all of us. The Web is a meaning machine that I want do understand by my research.
Where else you might find me?
I'm Nasir, a member of WeST institute. My hobbies include traveling, photography, jogging and watching cricket. In my free time I like to go out and capture the nature in stills.
On the academic front, I earned my PhD from Institute WeST under the supervision of Professor Dr. Steffen Staab. My research interests encompass web retrieval, web mining and machine learning with specific focus on mining and analysis of social media contents. I worked on topics like content quality and diversification of social contents. My current research activity relates to the application of machine learning techniques for semantic search in linked opened data.