Knowledge Graph (KG) is the network which contains some topic-based entities, called
nodes, and the associated information among the entities. Here, the concept in the …

The graph model is nowadays largely adopted to model a wide range of knowledge and
data, spanning from social networks to knowledge graphs (KGs), representing a successful …

Abstract Knowledge Graphs (KGs) currently contain a vast amount of structured information
in the form of entities and relations. Because KGs are often constructed automatically by …

Abstract Recent success of Knowledge Graph has spurred widespread interests in methods
for the problem of Knowledge Graph completion. However, efforts to understand the quality …

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 …

Completion methods learn models to infer missing (subject, predicate, object) triples in
knowledge graphs, a task known as link prediction. The training phase is based on samples …

Graph embedding, which constructs vector representation of nodes in a network, has shown
effectiveness in many graph analysis tasks, such as node classification, node clustering, and …

While RDF data are graph shaped by nature, most traditional Machine Learning (ML)
algorithms expect data in a vector form. To transform graph elements to vectors, several …

In link prediction evaluation, an embedding model assigns plausibility scores to unseen
triples in a knowledge graph using an input partial triple. Performance metrics like mean …

Data integration tasks such as the creation and extension of knowledge graphs involve the
fusion of heterogeneous entities from many sources. Matching and fusion of such entities …