  The incompleteness of Knowledge Graphs (KGs) is a crucial issue affecting the
quality of AI-based services. In the scholarly domain, KGs describing research
publications typically lack important information, hindering our ability to
analyse and predict research dynamics. In recent years, link prediction
approaches based on Knowledge Graph Embedding models became the first aid for
this issue. In this work, we present Trans4E, a novel embedding model that is
particularly fit for KGs which include N to M relations with N$\gg$M. This is
typical for KGs that categorize a large number of entities (e.g., research
articles, patents, persons) according to a relatively small set of categories.
Trans4E was applied on two large-scale knowledge graphs, the Academia/Industry
DynAmics (AIDA) and Microsoft Academic Graph (MAG), for completing the
information about Fields of Study (e.g., 'neural networks', 'machine learning',
'artificial intelligence'), and affiliation types (e.g., 'education',
'company', 'government'), improving the scope and accuracy of the resulting
data. We evaluated our approach against alternative solutions on AIDA, MAG, and
four other benchmarks (FB15k, FB15k-237, WN18, and WN18RR). Trans4E outperforms
the other models when using low embedding dimensions and obtains competitive
results in high dimensions.
