Semantic web approach for expert finding problem

Expert Finding is a real life problem. The need for finding experts on a specific topic in both academic and industrial world is undeniable. Many systems and models have been performed in order to find a solution to this problem. These systems have used the data that has been collected manually in order to arrange an expert database. Since the collection of data is and keeping the data up to date is a big concern, we refer to Semantic Web data in order to collect, organize and analyze the data. We have used DBLP data in order to obtain academicians? profiles and their articles. Moreover we have developed a Google Scholar crawler in order to get citation values of articles. We have examined the previous methods and models that have been implemented to find experts. We have introduced our models and then implemented them with C# and Java programming languages. After the implementation we have tested and verified our solutions with the predefined sets of academicians and their area of interests. With the integration of our models, we have established a hybrid model in order to find top-k experts in a specific area. In order to measure our hybrid model?s performance, we have compared our results with the predefined top-k experts that have been verified.