Mining Scholarly Semantic Networks from the Web

With the increased usage of the Web and its availability of data, various scholarly information is now available on the Web. Extraction, aggregation, and visualization of such information is crucial for using our collective scholarly knowledge and expertise. Semantic network technology is prominent in structuring such knowledge. Our research project is designed to construct a scholarly semantic network from publicly available data that will be useful for researchers, business people in industry, and public servants in governmental organizations who are expected to make decisions appropriately. In this paper, we propose a practical architecture to construct a scholarly semantic network that integrates different scholarly entities such as researchers, papers, and keywords by taking into consideration ontological and web mining perspectives. We also provide an overview of our social network extraction system, which is used as an underpinning to build the architecture. The system, called POLYPHONET, employs several advanced web mining and semantic technologies to extract relations of researchers, to detect groups of researchers, and to obtain keywords for a researcher. The public installation of the system at several academic conferences provides evidence of the system´s usability and potential to facilitate the discovery of scholarly knowledge.