Abstract Knowledge graphs (KGs) are widely used for modeling scholarly communication, informing scientometric analysis, and supporting a variety of intelligent services to explore the literature and predict research dynamics. However, they often suffer from incompleteness (eg, missing affiliations, references, research topics), which ends up reducing the scope and quality of the resulting analysis. This [...]

Abstract Performing link prediction using knowledge graph embedding (KGE) models is a popular approach for knowledge graph completion. Such link predictions are performed by measuring the likelihood of links in the graph via a transformation function that maps nodes via edges into a vector space. Since the complex structure of the real world is [...]

Abstract Knowledge graphs represent the meaning of properties of real-world entities and relationships among them in a natural way. Exploiting semantics encoded in knowledge graphs enables the implementation of knowledge-driven tasks such as semantic retrieval, query processing, and question answering, as well as solutions to knowledge discovery tasks including pattern discovery and link prediction. [...]

Abstract Scholars often need to search for matching, high-profile scientific events to publish their research results. Information about topical focus and quality of events is not made sufficiently explicit in the existing communication channels where events are announced. Therefore, scholars have to spend a lot of time on reading and assessing calls for papers [...]