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Despite encoding enormous amount of rich and valuable data, existing datasources are mostly created independently, being a significant challenge totheir integration. Mapping languages, e.g., RML and R2RML, facilitatedeclarative specification of the process of applying meta-data and integratingdata into a knowledge graph. Mapping rules can also include knowledgeextraction functions in addition to expressing correspondences among datasources and a unified schema. Combining mapping rules and functions representsa powerful formalism to specify pipelines for integrating data into a knowledgegraph transparently. Surprisingly, these formalisms are not fully adapted, andmany knowledge graphs are created by executing ad-hoc programs to pre-processand integrate data. In this paper, we present EABlock, an approach integratingEntity Alignment (EA) as part of RML mapping rules. EABlock includes a block offunctions performing entity recognition from textual attributes and link therecognized entities to the corresponding resources in Wikidata, DBpedia, anddomain specific thesaurus, e.g., UMLS. EABlock provides agnostic and efficienttechniques to evaluate the functions and transfer the mappings to facilitateits application in any RML-compliant engine. We have empirically evaluatedEABlock performance, and results indicate that EABlock speeds up knowledgegraph creation pipelines that require entity recognition and linking instate-of-the-art RML-compliant engines. EABlock is also publicly available as atool through a GitHub repository(https://github.com/SDM-TIB/EABlock) and aDOI(https://doi.org/10.5281/zenodo.5779773).

EABlock: A Declarative Entity Alignment Block for Knowledge Graph Creation Pipelines