Recently, knowledge graph embeddings (KGEs) have received significant attention, and
several software libraries have been developed for training and evaluation. While each of …

Over the decades, fashions in Computational Linguistics have changed again and again,
with major shifts in motivations, methods and applications. When digital computers first …

Parallel and multiprocessing algorithms break down significant numerical problems into
smaller subtasks, reducing the total computing time on multiprocessor and multicore …

In knowledge graph representation learning, link prediction is among the most popular and
influential tasks. Its surge in popularity has resulted in a panoply of orthogonal embedding …

Various applications in computational linguistics and artificial intelligence employ semantic
similarity to solve challenging tasks, such as word sense disambiguation, text classification …

Various applications in computational linguistics and artificial intelligence rely on high-
performing word sense disambiguation techniques to solve challenging tasks such as …

To improve the performance of knowledge graph-based question answering system
(KGQA), several approaches have been developed to construct a semantic parser based on …

Abstract Knowledge graph (KG) embedding methods are at the basis of many KG-based
data mining tasks, such as link prediction and node clustering. However, graphs may …

Recently, uncertain knowledge graphs (UKGs), where each relation between entities is
associated with a confidence score, have gained much attention. Compared with traditional …

Exercise recommendation is an integral part of enabling personalized learning. Giving
appropriate exercises can facilitate learning for learners. The programming problem …