Entity alignment experiments on Russian-English dataset with unmatchable entities

In recent years, interest in knowledge graphs (KG) has increased exponentially in both scientific and industrial communities. The KGs play an important role in the AI applications such as natural language processing including question-answering systems, recommender systems, and search engines. Integration of different KGs is one of the most pressing problems and is used, for example, to develop complex digital twins of industrial systems. One of the components of the KG integration problem is the entity alignment (EA) problem, which attempts to identify entities in different KGs describing the same real-world object. A special case of this problem is the problem of cross-language entity alignment, which is closely related to the problem of import substitution, such as finding equivalent drugs, spare parts, or devices for the Internet of Things. Unfortunately, in real KGs, many entities may have no equivalents in other KGs. This paper describes entity alignment experiments using the example of a Russian-English dataset with unmatchable entities.