Graph-Based Named Entity Management System with Ontology-Supported Matching Capabilities
TL;DR
This work presents a Named Entity Management System (NEMS) that integrates entity lifecycle management with scalable matching in a unified workflow for knowledge graph creation, and embeds matching and validation during ingestion, combining attribute-level similarity with graph-structured and ontology-aware signals to guide merge decisions.
Abstract
: Entity Matching (EM) is a core challenge in data integration, requiring the identification of records that refer to the same real-world entity across heterogeneous sources. Practical experience shows that overall performance depends on end-to-end system design rather than isolated algorithms: candidate generation, threshold calibration, provenance tracking, consolidation policies, and iterative error analysis often determine effectiveness. Ontologies, knowledge graphs, and persistent identifiers provide semantic context and stable references, but introduce additional complexity in handling uncertainty and evolving representations. We present a Named Entity Management System (NEMS) that integrates entity lifecycle management with scalable matching in a unified workflow for knowledge graph creation. Instead of treating reconciliation as post-processing, NEMS embeds matching and validation during ingestion, combining attribute-level similarity with graph-structured and ontology-aware signals to guide merge decisions. By integrating canonical identifiers, provenance tracking, and configurable decision thresholds, NEMS enables conservative merging, incremental updates, and explainable outcomes. The architecture accommodates diverse matching paradigms while leveraging structural context, providing a robust foundation for scalable and consistent entity integration.