Discovering Entity Profiles Candidate for Entity Resolution on Linked Open Data Halal Food Products

Entity resolution is a common task in the Web of data. In the majority, the recent studies of this field aim to discover appropriate entity profiles candidate to reduce the likelihood of missing matches and to place matching entity profiles in the same blocks. We proposed a method to discover entity profiles candidate for entity resolution. We utilize Node2vec graph embedding to get entity representations and perform link prediction. We employed a graph database to generate the nodes and relations from the RDF triple dataset file. Then, the nodes and relations were transformed into vectors and saved to the vector embedding file. We calculate vector similarity between an entity source vector and all of the entity vectors on the embedding file. The vector similarity produces a set of relevant entities. The top-k results are selected as entity profiles candidate that present the most similar entities to the entity source. Finally, we do an entity resolution task by utilizing string similarity comparisons between the pair of entity source and entity profile attribute values with predetermined parameters and threshold. We assign owl:sameAs property for matches entities. The results show 87%, 80%, and 83% for precision, recall, and F-measure evaluation score, respectively.