Towards Exploring Literals to Enrich Data Linking in Knowledge Graphs

Abstract

Knowledge graph completion is still a challenging solution that uses techniques from distinct areas to solve many different tasks. Most recent works, which are based on embedding models, were conceived to improve an existing knowledge graph using the link prediction task. However, even considering the ability of these solutions to solve other tasks, they did not present results for data linking, for example. Furthermore, most of these works focuses only on structural information, i.e., the relations between entities. In this paper, we present an approach for data linking that enrich entity embeddings in a model with their literal information and that do not rely on external information of these entities. The key aspect of this proposal is that we use a blocking scheme to improve the effectiveness of the solution in relation to the use of literals. Thus, in addition to the literals from object elements in a triple, we use other literals from subjects and predicates. By merging entity embeddings with their literal information it is possible to extend many popular embedding models. Preliminary experiments were performed on real-world datasets and our solution showed competitive results to the performance of the task of data linking.