Ontology-based Data Validation and Cleaning for Linked Data: Promoting Reliable and Accurate Knowledge Graphs Introduction: In the ever-growing realm of Big Data, the availability and accessibility of vast amounts of information bring both opportunities and challenges. One of the key challenges is ensuring the quality and reliability of the data, especially in the context of Linked Data and knowledge graphs. Traditional data validation and cleaning methods might fall short when dealing with complex and interconnected datasets. However, ontology-based approaches offer a promising solution for enhancing data quality and promoting accurate knowledge representation. What is Ontology? To grasp the significance of ontology-based data validation and cleaning, let's first understand ontology itself. In simple terms, an ontology is a structured and formal representation of knowledge, defining concepts, relationships, and constraints within a specific domain. Ontologies provide a common and standardized language for describing and organizing information, enabling better understanding and facilitating interoperability among different systems and datasets. The Challenges of Linked Data: Linked Data is an approach that aims to create a global web of interconnected data by using standardized formats and protocols.
