Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …

Abstract Knowledge Graph (KG) provides high-quality structured knowledge for various
downstream knowledge-aware tasks (such as recommendation and intelligent question …

Abstract Knowledge graphs are multi-relational data that contain massive entities and
relations. As an effective graph representation technique based on deep learning, graph …

Science communication has a number of bottlenecks that include the rising number of
published research papers and its non-machine-accessible and document-based paradigm …

The tourism and hospitality sectors have become increasingly important in the last few years
and the companies operating in this field are constantly challenged with providing new …

This paper aims at medical text classification, where texts describe medicines, diseases, or
other medical topics. This field is still challenging since medical texts contain intensive …

In the context of mass personalization, the dynamic and erratic nature of customer
requirements (CRs) is increasingly pronounced. Amidst intensifying market competition …

In the last few years, we have witnessed the emergence of several knowledge graphs that
explicitly describe research knowledge with the aim of enabling intelligent systems for …

The decision-making problems based on fuzzy inference systems have received much
attention from the worldwide scientific community. The M-CFIS-FKG model is considered …

Abstract Knowledge Graph Completion seeks to find missing elements in a Knowledge
Graph, usually edges representing some relation between two concepts. One possible way …