Entity matching (EM) finds data instances that refer to the same real-world entity. Most EM
solutions perform blocking then matching. Many works have applied deep learning (DL) to …

With Knowledge Graphs (KGs) at the center of numerous applications such as recommender
systems and question-answering, the need for generalized pipelines to construct and …

Entity resolution (ER) approaches typically consist of a blocker and a matcher. They share
the same goal and cooperate in different roles: the blocker first quickly removes obvious non …

Millions of websites use the schema. org vocabulary to annotate structured data describing
products, local businesses, or events within their HTML pages. Integrating schema. org data …

Owing to the heterogeneity and incomplete information present in various domain
knowledge graphs, the alignment of distinct source entities that represent an identical real …

Data integration is utilized to integrate heterogeneous data from multiple sources,
representing a crucial step to improve information value in data analysis and mining …

One of the more significant recent major progress in computer science is the coevolution of
deep learning and the Semantic Web. This subject includes research from various …

Data integration is an important component of Big Data analytics. One of the key challenges
in data integration is record linkage, that is, matching records that represent the same real …

Entity Matching (EM) is crucial for identifying equivalent data entities across different
sources, a task that becomes increasingly challenging with the growth and heterogeneity of …

Deep entity resolution (ER) identifies matching entities across data sources using
techniques based on deep learning. It involves two steps: a blocker for identifying the …