In this paper, we explore how Knowledge Graphs (KGs) can potentially benefit Record Linkage (RL). RL is the process of identifying and resolving duplicate records across different data sources, including structured, semi-structured, and unstructured data (e.g, in data lakes). RL is a critical task for information systems that rely on data to make decisions and is used in a wide variety of fields such as healthcare, finance, government and marketing. Due to recent advances in machine learning, there has been a significant progress in building automated RL methods. However, when dealing with vertical applications, featuring specialized domains such as a particular hospital or industry, human experts are still required to enter domain-specific knowledge, making RL prohibitively expensive. Despite KGs can be powerful tools to represent and derive domain-specific knowledge, their application to RL has been overlooked. Inspired by a healthcare case study in the Republic of Cyprus, we aim at filling this gap by identifying challenges and opportunities of using KGs to reduce the effort of solving RL in vertical applications.
Using Knowledge Graphs for Record Linkage: Challenges and Opportunities / Andreou, A. S.; Firmani, D.; Mathew, J. G.; Mecella, M.; Pingos, M.. - 482:(2023), pp. 145-151. (Intervento presentato al convegno 35th International Conference on Advanced Information Systems Engineering tenutosi a Zaragoza; Spain) [10.1007/978-3-031-34985-0_15].
Using Knowledge Graphs for Record Linkage: Challenges and Opportunities
Firmani D.;Mathew J. G.;Mecella M.;
2023
Abstract
In this paper, we explore how Knowledge Graphs (KGs) can potentially benefit Record Linkage (RL). RL is the process of identifying and resolving duplicate records across different data sources, including structured, semi-structured, and unstructured data (e.g, in data lakes). RL is a critical task for information systems that rely on data to make decisions and is used in a wide variety of fields such as healthcare, finance, government and marketing. Due to recent advances in machine learning, there has been a significant progress in building automated RL methods. However, when dealing with vertical applications, featuring specialized domains such as a particular hospital or industry, human experts are still required to enter domain-specific knowledge, making RL prohibitively expensive. Despite KGs can be powerful tools to represent and derive domain-specific knowledge, their application to RL has been overlooked. Inspired by a healthcare case study in the Republic of Cyprus, we aim at filling this gap by identifying challenges and opportunities of using KGs to reduce the effort of solving RL in vertical applications.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
