Sustainable Linked Data Generation: The Case of DBpedia

Abstract

Abstract. dbpedia ef, the generation framework behind one of the Linked Open Data cloud's central interlinking hubs, has limitations with regard to quality, coverage and sustainability of the generated dataset. dbpedia can be further improved both on schema and data level. Errors and inconsistencies can be addressed by amending (i) the dbpedia ef; (ii) the dbpedia mapping rules; or (iii) Wikipedia itself from which it extracts information. However, even though the dbpedia ef and mapping rules are continuously evolving and several changes were applied to both of them, there are no significant improvements on the dbpedia dataset since its limitations were identified. To address these shortcomings, we propose adapting a different semantic-driven approach that decouples, in a declarative manner, the extraction, transformation and mapping rules execution. In this paper, we provide details regarding the new dbpedia ef, its architecture, technical implementation and extraction results. This way, we achieve an enhanced data generation process, which can be broadly adopted, and that improves its quality, coverage and sustainability.

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

“…18 articles introduce or extend a mapping language for transforming heterogeneous (semi-)structured data into a knowledge graph [98,86,36,30,132,87,83,81,128,15,46,90,41,51,135,104,19]. 16 articles introduce data transformations or align them with a mapping language for heterogeneous (semi-)structured data [99,86,38,132,87,107,70,73,32,15,90,41,40,95,39,7]. 21 articles describe a materialization system [85,86,30,132,107,122,87,83,81,115,45,72,54,46,114,120,64,…”

“…When DBpedia was originally introduced, it had its own mapping language 13 based on the MediaWiki syntax to transform Wikipedia pages into the DBpedia RDF graph. However, not all schema and data transformations were defined using its mapping language but a few of these transformations were embedded in the DBpedia extraction framework [95]. While DBpedia is not generated from heterogeneous data and its mapping language does not refer to heterogeneous data, we thought that it is worth mentioning due to the impact of DBpedia's RDF graph to the broader Semantic Web community.…”

“…The Function Ontology 21 (FnO) [38,39,95] is a semantic description of functions without depending on their implementation (F4). FnO describes each function's parameters and return value to specify how the function should be used.…”

“…Data transformations 5 characteristics were discussed for data transformations (Section 5). We observed that most data transformations are dedicated to a certain schema transformation, but only in the case of RML, there are 3 alternative data transformations proposed (GeoTriples [83,81], Function Ontology (FnO) [38,39,95], and FunUL [73]).…”

“…FnO [38,39,95] and SPARQL Functions [109] are the only data transformations not depending on a specific schema transformation. However, FnO is completely standalone from any schema transformation while SPARQL Functions are only available in a SPARQL query.…”