Entity Resolution (ER), a core task of Data Integration, detects different entity profiles that
correspond to the same real-world object. Due to its inherently quadratic complexity, a series …

MapReduce is a programming system for distributed processing of large-scale data in an
efficient and fault tolerant manner on a private, public, or hybrid cloud. MapReduce is …

In recent years, several knowledge bases have been built to enable large-scale knowledge
sharing, but also an entity-centric Web search, mixing both structured data and text …

Hadoop and Spark are widely used distributed processing frameworks for large-scale data
processing in an efficient and fault-tolerant manner on private or public clouds. These big …

Keys for graphs aim to uniquely identify entities represented by vertices in a graph. We
propose a class of keys that are recursively defined in terms of graph patterns, and are …

In this paper, we propose a semantic-aware blocking framework for entity resolution (ER).
The proposed framework is built using locality-sensitive hashing (LSH) techniques, which …

Efficiency techniques are an integral part of Entity Resolution, since its infancy. In this
survey, we organized the bulk of works in the field into Blocking, Filtering and hybrid …

Entity Resolution (ER) aims to identify different descriptions in various Knowledge Bases
(KBs) that refer to the same entity. ER is challenged by the Variety, Volume and Veracity of …

An increasing number of entities are described by interlinked data rather than documents on
the Web. Entity Resolution (ER) aims to identify descriptions of the same real-world entity …

Offer alignment is a key step in a product knowledge graph construction pipeline. It aims to
align retailer offers of the same product for better coverage of product details. With the rapid …