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Personalized search and browsing is increasingly vital especially for enterprises to able to reach their customers. Key challenge in supporting personalization is the need for rich metadata such as cognitive metadata about documents. As we consider size of large knowledge bases, manual annotation is not scalable and feasible. On the other hand, automatic mining of cognitive metadata is challenging since it is very difficult to understand underlying intellectual knowledge about documents automatically. To alleviate this problem, we introduce a novel metadata extraction framework, which is based on fuzzy information granulation and fuzzy inference system for automatic cognitive metadata mining. The user evaluation study shows that our approach provides reasonable precision rates for difficulty, interactivity type, and interactivity level on the examined 100 documents. In addition, proposed fuzzy inference system achieves improved results compared to a rule-based reasoner for document difficulty metadata extraction (11% improvement).

In December 2019, Covid-19 epidemic was identified in Wuhan, China. Covid-19 may cause fatality especially among elderly, and people with chronic health problems. After human to human transmissions of the disease, it has rapidly spread throughout China, and then the outbreak has reached to neighboring countries in Asia. Now, the spread of the virus is accelerating in the world, and increasing number of new cases has been reported daily in Europe, Middle East, Africa and America regions. Recently, World Health Organization (WHO) also announced Covid-19 as a Pandemic. As of 3 April, worldwide around more than 1 million cases and around 60,000 fatalities are reported. Thus, forecasting regional and worldwide outbreak size of Covid-19 is important in order to take necessary actions regarding to preparedness plans and mitigation interventions. In this work, we design a deep learning model, which is an effective artificial intelligence method, to provide regional and worldwide forecasts. Particularly for worldwide, our approach predicts the cumulative number of cases, cumulative number of deaths and daily new cases. For Europe and Middle East regions, we predict the cumulative number of cases, and for Mainland China we predict daily new cases and the cumulative number of deaths. We predict the next 10 days based on the previously reported actual time series data of Covid-19. For worldwide forecasts, we use the data provided by Worldometers. For Europe and Middle East forecasts, we use the data provided by World Health Organization, and for China Mainland forecasts, the data is obtained from Chinese Centre for Disease Control and Prevention. This is the first time that a deep learning model has been employed for Covid-19 spread prediction, solely based on the known reported cases of Covid-19. The proposed deep learning architecture consists of Long Short Term Memory (LSTM) layer, dropout layer, and fully connected layers to predict regional and worldwide forecasts. We evaluate our approach with Root Mean Square Error (RMSE) metric. For forecasting, we use the network models that give the minimum RMSE on the last 3 days of actual data. Networks, which achieves the minimum RMSE on the last 3 days, are used to predict the next 10 days. Every day, the spread and situations are changing. Our approach can take into account these real-time changes; the deep learning model can be re-trained with new daily data and perform real-time forecasting. Results show that the proposed deep learning model is promising, it can predict possible scenarios regionally and globally for the spread of Covid-19.