Experience in Integration of University Knowledge Bases Based on Semantic Web Technologies

Abstract

The paper is devoted to the study of optimal algorithms for virtual integration of university knowledge bases in the field of computer science and programming with external data sources in Russian and English. Data from external sources can be presented in RDF, OWL, XML, HTML, JSON, CSV formats, in the form of relational, graph databases, or not structured at all. The proposed algorithms will provide a methodological and technological basis for creating problem-oriented knowledge bases as artificial intelligence systems, as well as prerequisites for the development of semantic technologies for acquiring new knowledge on the Internet without direct human participation. Testing of the studied machine learning algorithms is carried out by the method of sliding control (cross-validation) on specialized text corpora. The novelty of the presented study is due to the application of the Paretos optimality principle for multicriteria evaluation and ranking of the studied algorithms in the absence of a priori information about the comparative significance of the criteria. The project is implemented in accordance with semantic web standards. The architecture of the semantic web portal and usage examples are given. The proposed software solutions are based on cloud computing using DBaaS and PaaS service models to ensure scalability of data warehouses and network services. The created software is publicly available and can be freely used.