EABlock

Abstract

Despite encoding enormous amount of rich and valuable data, existing data sources are mostly created independently, being a significant challenge to their integration. Mapping languages, e.g., RML and R2RML, facilitate declarative specification of the process of applying meta-data and integrating data into a knowledge graph. Mapping rules can also include knowledge extraction functions in addition to expressing correspondences among data sources and a unified schema. Combining mapping rules and functions represents a powerful formalism to specify pipelines for integrating data into a knowledge graph transparently. Surprisingly, these formalisms are not fully adapted, and many knowledge graphs are created by executing ad-hoc programs to pre-process and integrate data. In this paper, we present EABlock, an approach integrating Entity Alignment (EA) as part of RML mapping rules. EABlock includes a block of functions performing entity recognition from textual attributes and link the recognized entities to the corresponding resources in Wikidata, DBpedia, and domain specific thesaurus, e.g., UMLS. EABlock provides agnostic and efficient techniques to evaluate the functions and transfer the mappings to facilitate its application in any RML-compliant engine. We have empirically evaluated EABlock performance, and results indicate that EABlock speeds up knowledge graph creation pipelines that require entity recognition and linking in state-of-the-art RML-compliant engines. EABlock is also publicly available as a tool through a GitHub repository and a DOI.

Declarative RDF graph generation from heterogeneous (semi-)structured data: A systematic literature review

“…Last, it is worth mentioning two systems: Morph-KGC [5] and EABlock [71], which were accepted while this journal was under review. Morph-KGC supports CSV, TSV, JSON and XML data, as well as relational databases.…”

Knowledge graphs for enhancing transparency in health data ecosystems1

“…Exemplar SPARQL queries are presented in Section A. Query in Listing 1 retrieves metadata about the RML mapping rules that define a particular class, while query in Listing 2 collects the functions included in the RML mapping rules. These functions are expressed in FnO and are part of the toolbox EABlock 26 [28,29]. This toolbox includes functions that solve entity alignment over biomedical textual attributes.…”

“…These DE4LungCancer classes correspond to relations in the UMLS Semantic Network. 29 The entities corresponding to scientific publications have been annotated with 12,485,564 terms from UMLS. Together with the ones extracted by the Scientific Open DE (Section 4.3), these annotations establish the entity alignments required for the data integration process in the DE4LungCancer KG.…”

Cross-Language Entity Alignment Based on Dual-Relation Graph and Neighbor Entity Screening

“…Approaches based on GCN, such as [9,10,[25][26][27][28][29][30][31][32][33][34][35][36][37][38], have enhanced the embedding of entities and their neighbors' information, and they only require a small number of aligned seed-pairs to transfer similar information to the whole graph. As compared to the traditional entity-alignment methods, approaches based on GCNs not only require relatively less human involvement in the process of feature construction, but also such approaches could be extended to large knowledge graphs.…”

“…Therefore, it has been typical to rely on the entity's neighboring entities to provide more useful information to support the implementation of entity alignment. However, the existing methods [26][27][28][29] directly overlooked situations where the head and tail entities had the same entity name in the relation triple when obtaining the neighboring entities of each entity, but this situation could exist in the real corpus. For example, with relational triples (Bob, know, Bob), it was obvious that the head and tail entities in this triple had the same name, but it could not be determined that the two entities referred to the same person or thing in the real world.…”