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Knowledge graphs (KGs), i.e. representation of information as a semanticgraph, provide a significant test bed for many tasks including questionanswering, recommendation, and link prediction. Various amount of scholarlymetadata have been made vailable as knowledge graphs from the diversity of dataproviders and agents. However, these high-quantities of data remain far fromquality criteria in terms of completeness while growing at a rapid pace. Mostof the attempts in completing such KGs are following traditional datadigitization, harvesting and collaborative curation approaches. Whereas,advanced AI-related approaches such as embedding models - specifically designedfor such tasks - are usually evaluated for standard benchmarks such as Freebaseand Wordnet. The tailored nature of such datasets prevents those approaches toshed the lights on more accurate discoveries. Application of such models ondomain-specific KGs takes advantage of enriched meta-data and provides accurateresults where the underlying domain can enormously benefit. In this work, theTransE embedding model is reconciled for a specific link prediction task onscholarly metadata. The results show a significant shift in the accuracy andperformance evaluation of the model on a dataset with scholarly metadata. Thenewly proposed version of TransE obtains 99.9% for link prediction task whileoriginal TransE gets 95%. In terms of accuracy and Hit@10, TransE outperformsother embedding models such as ComplEx, TransH and TransR experimented overscholarly knowledge graphs

Soft Marginal TransE for Scholarly Knowledge Graph Completion