This article presents elements of the linguistic knowledge graph “Turklang”, developed at the Institute of Applied Semiotics of the Academy of Sciences of Tatarstan and used as a basis for creating a number of linguistic resources and tools: the portal “Turkic Morpheme”, the electronic corpus of the Tatar language “Tugan Tel”, morphoanalyzer. Creating an educational environment requires subject-oriented knowledge graphs, for which methods of general and open graphs are not suitable. This paper describes linguistic knowledge graphs, which reflect, on the one hand, potential capabilities of Turkic languages, and on the other hand, examples of actual use in texts. Peculiarity of these knowledge graphs is that they contain linguistic units of different linguistic levels, and concepts corresponding to meanings of these linguistic units, which are built into the thesaurus of concepts. Structure of this knowledge graph allows to formulate the content of a training course, build an individual educational trajectory, as well as create tests and tools of automated answer grading as part of knowledge control when teaching Turkic languages. This makes it possible to subsequently develop, based on these graphs, training programs taking into account the structural and functional features of the Turkic languages, and also contributes to the implementation of individual goals of students.

The modern information space contains a lot of data, but they are often poorly structured, difficult to find and not always correct. This creates additional difficulties during researches, so digital spaces of scientific knowledge are currently being formed, in particular, based on knowledge graphs.

To ensure the quality of information, such graphs are often filled with data manually, which is time-consuming. Therefore, the creation of a tool that provides the ability to automate process of filling a graph with data, as well as ensures data quality, will simplify and speed up the process of forming digital spaces of scientific knowledge.

Methods for automating the filling of the graph with data are proposed, including parallel control of their integrity. Based on the proposed methods, a software module has been developed, the mechanisms of its functioning and its architecture are described.

Cross-lingual entity alignment algorithms are designed to look for identical real-world objects in multilingual knowledge graphs. This problem occurs, for example, when searching for drugs manufactured in different countries under different names, or when searching for imported equipment. At the moment, there are several open-source libraries that collect implementations of entity alignment algorithms as well as test data sets for various languages. This paper describes experiments with several popular entity alignment algorithms applied to a Russian-English dataset. In addition to translating entity names from Russian to English, experiments on combining the various generators of entity name embeddings with the various generators of relational information embeddings have been conducted. In order to obtain more detailed information about the results of the EA approaches, an assessment by entity types, the number of relationships and attributes have been made. These experiments allowed us to significantly improve the accuracy of several EA algorithms on the English-Russian dataset.

The materials presented at the Conference describe the results of recent years in the following areas: Open access to scientific data and knowledge in Earth Sciences; Data peculiarities in Earth Sciences: new concepts and methods, tools for their collection, integration and processing in different information systems, including systems with intensive use of data; Data mining and mathematical simulation of natural processes in Earth Sciences. Evolution of classical GIS-applications in Earth Sciences; Application to Critical Raw Materials (CRM); social aspects of mining (e.g., the Social Licence to Operate [SLO]); predictive mapping and applications to exploration, landuse and search for extensions of known deposits; Intelligent data analysis, elicitation of facts and knowledge from scientific publications. Thesauruses, ontologies and conceptual modeling. Semantic WEB, linked data. Services. Content semantic structuring. Applications for geosciences, e.g., Ontology-based Dynamic Decision Graphs for Expert systems and decision-aid tools; Application of methods and technologies of the remote sensing in Earth Sciences: from satellites to unmanned aerial vehicles; Information technologies for demonstration and popularization of scientific achievements in Earth Sciences; Applications: environmental risks including mining wastes, natural hazards, water resource management, etc.