Entity resolution: a novel graph embedding approach using RandomDeep

The exponential growth of digital information necessitates robust methods for entity resolution to ensure data quality and integration across datasets. This paper presents three novel node embedding algorithms for entity resolution in graph databases: RandomDeep, refined embedding and combined embedding. RandomDeep integrates iterative deepening depth first search with deep learning to capture structural and semantic characteristics. Refined embedding enhances initial graph convolutional (GCN) embeddings through random walk-based refinement. Combined embedding merges outputs from complementary algorithms to produce versatile representations adaptable to diverse graph structures. A two-stage graph summarisation technique supports this approach: initially as a blocking method to reduce computational complexity, and later during merging to consolidate redundant nodes. Evaluation datasets (DBLP-Scholar, Amazon-Google, Cora and Yellow-Yelp) demonstrate the methods' effectiveness, with area under cover precision and recall values ranging from 0.50 to 0.97 and F-measure values between 0.67 and 0.94. These results showcase accurate, efficient entity resolution in graph databases.