Big Data and Machine Learning in Government Projects: Expert Evaluation Case

In this paper, we present the Expert Hub System, which was designed to help governmental structures find the best experts in different areas of expertise for better reviewing of the incoming grant proposals. In order to define the areas of expertise with topic modeling and clustering, and then to relate experts to corresponding areas of expertise and rank them according to their proficiency in certain areas of expertise, the Expert Hub approach uses the data from the Directorate of Science and Technology Programmes. Furthermore, the paper discusses the use of Big Data and Machine Learning in the Russian government project

Efficient Algorithms for Constructing Multiplex Networks Embedding

Network embedding has become a very promising techniquein analysis of complex networks. It is a method to project nodes of anetwork into a low-dimensional vector space while retaining the structureof the network based on vector similarity. There are many methods ofnetwork embedding developed for traditional single layer networks. Onthe other hand, multilayer networks can provide more information aboutrelationships between nodes. In this paper, we present our random walkbased multilayer network embedding and compare it with single layerand multilayer network embeddings. For this purpose, we used severalclassic datasets usually used in network embedding experiments and alsocollected our own dataset of papers and authors indexed in Scopus

Efficient Algorithms for Constructing Multiplex Networks Embedding

Network embedding has become a very promising techniquein analysis of complex networks. It is a method to project nodes of anetwork into a low-dimensional vector space while retaining the structureof the network based on vector similarity. There are many methods ofnetwork embedding developed for traditional single layer networks. Onthe other hand, multilayer networks can provide more information aboutrelationships between nodes. In this paper, we present our random walkbased multilayer network embedding and compare it with single layerand multilayer network embeddings. For this purpose, we used severalclassic datasets usually used in network embedding experiments and alsocollected our own dataset of papers and authors indexed in Scopus

Introducing Government Contracts to Technology Forecasting

Nowadays, technology forecasting has become a multidisciplinary field employing various methods for detecting patterns in data sources in order to forecast trends and future state of different technologies. Technology forecasting is widely used by decision-makers for evaluating grant and contract proposals. Although there are some production-grade systems for technology forecasting for English, Russian patent databases and citation indexes are isolated from the global ones. This makes technology forecasting in Russia more complicate. In this research, we introduce government contracts as new possible parameter for technology forecasting. We think that government contracts indicate government's interest in certain area of research or technology and thus may influence technology trends. We analyzed Russian government contracts; however, we consider this parameter suitable for technology forecasting systems in other languages as most of fundamental research conducted in most countries is sponsored by government. We study government contracts utilizing an information retrieval system pipeline exploiting latent semantic analysis and word2vec. Keywords: Technology Forecasting, Information Retrieval, Government Contract, Trend Analysis JEL Classifications: I38, O3

Big Data and Machine Learning in Government Projects: Expert Evaluation Case

In this paper, we present the Expert Hub System, which was designed to help governmental structures find the best experts in different areas of expertise for better reviewing of the incoming grant proposals. In order to define the areas of expertise with topic modeling and clustering, and then to relate experts to corresponding areas of expertise and rank them according to their proficiency in certain areas of expertise, the Expert Hub approach uses the data from the Directorate of Science and Technology Programmes. Furthermore, the paper discusses the use of Big Data and Machine Learning in the Russian government project