Knowledge Enriched Schema Matching Framework for Heterogeneous Data Integration

Abstract

Schema matching is a process of creating the correspondences and mappings from the various schemas, which is a critical phase of migrating and integrating heterogeneous databases from multiple sources. However, the semantic heterogeneity in various schemas brings some obstacles while establishing the correspondences between source schema and target schema, hence human interventions and domain knowledge are required to tackle some complex mapping tasks for heterogeneous data integration. To reduce human intervention and improve the ability to handle complex matching tasks, we present a knowledge-enriched schema matching framework. In this framework, the schema matching task is treated as a classification problem, thereby, a schema matching network is designed as a classifier to give the mapping result. In particular, the external knowledge bases are injected into the schema matching network to capture the background knowledge and provide the common knowledge to handle the semantic heterogeneity of complex mapping tasks. Additionally, the main components of the presented framework and their roles are analyzed, and the feasibility of our framework and the future work are highlighted.