  Knowledge graphs (KGs), i.e. representation of information as a semantic
graph, provide a significant test bed for many tasks including question
answering, recommendation, and link prediction. Various amount of scholarly
metadata have been made vailable as knowledge graphs from the diversity of data
providers and agents. However, these high-quantities of data remain far from
quality criteria in terms of completeness while growing at a rapid pace. Most
of the attempts in completing such KGs are following traditional data
digitization, harvesting and collaborative curation approaches. Whereas,
advanced AI-related approaches such as embedding models - specifically designed
for such tasks - are usually evaluated for standard benchmarks such as Freebase
and Wordnet. The tailored nature of such datasets prevents those approaches to
shed the lights on more accurate discoveries. Application of such models on
domain-specific KGs takes advantage of enriched meta-data and provides accurate
results where the underlying domain can enormously benefit. In this work, the
TransE embedding model is reconciled for a specific link prediction task on
scholarly metadata. The results show a significant shift in the accuracy and
performance evaluation of the model on a dataset with scholarly metadata. The
newly proposed version of TransE obtains 99.9% for link prediction task while
original TransE gets 95%. In terms of accuracy and Hit@10, TransE outperforms
other embedding models such as ComplEx, TransH and TransR experimented over
scholarly knowledge graphs
