In the Big Data era, where variety is the most dominant dimension, the RDF data model enables the creation and integration of actionable knowledge from heterogeneous data sources. However, the RDF data model allows for describing entities under various contexts, e.g., people can be described from its demographic context, but as well from their professional contexts. Context-aware description poses challenges during entity matching of RDF datasets the match might not be valid in every context. To perform a contextually relevant entity matching, the specific context under which a data-driven task, e.g., data integration is performed, must be taken into account. However, existing approaches only consider inter-schema and properties mapping of different data sources and prevent users from selecting contexts and conditions during a data integration process. We devise COMET, an entity matching technique that relies on both the knowledge stated in RDF vocabularies and a context-based similarity metric to map contextually equivalent RDF graphs. COMET follows a two-fold approach to solve the problem of entity matching in RDF graphs in a context-aware manner.
In the first step, COMET computes the similarity measures across RDF entities and resorts to the Formal Concept Analysis algorithm to map contextually equivalent RDF entities. Finally, COMET combines the results of the first step and executes a 1-1 perfect matching algorithm for matching RDF entities based on the combined scores. We empirically evaluate the performance of COMET on testbed from DBpedia. The experimental results suggest that COMET accurately matches equivalent RDF graphs in a context-dependent manner.

The Lecture was delivered at the Big Data Analytics Summer School 2020 by Dr. Diego Collarana Vargas, Fraunhofer IAIS.