While advances in computing resources have made processing enormous amounts of data
possible, human ability to identify patterns in such data has not scaled accordingly. Efficient …

A large number of published datasets (or sources) that follow Linked Data principles is
currently available and this number grows rapidly. However, the major target of Linked Data …

The dominant paradigm for relation prediction in knowledge graphs involves learning and
operating on latent representations (ie, embeddings) of entities and relations. However …

Knowledge graph (eg Freebase, YAGO) is a multi-relational graph representing rich factual
information among entities of various types. Entity alignment is the key step towards …

This paper is a survey of the research topics in the field of Semantic Web, Linked Data and
Web of Data. This study looks at the contributions of this research community over its first …

Given a terabyte-scale graph distributed across multiple machines, how can we summarize
it, with much fewer nodes and edges, so that we can restore the original graph exactly or …

Digital libraries content and quality of services are badly affected by the author name
ambiguity problem in the citations and it is considered as one of the hardest problems faced …

In this paper, we introduce AttendEM, a framework for entity matching (EM), ie, pairwise
identification of duplicates across databases. Eschewing the prevalent focus on text …

Linking data by finding matching instances in different datasets requires considering many
characteristics, such as structural heterogeneity, implicit knowledge, and URI (Uniform …

Product matching is a central task within e-commerce applications such as price comparison
portals and online market places. State-of-the-art product matching methods achieve F1 …