Knowledge Graph Construction from Hetereogeneous Data Sources Exploiting Declarative Mapping Rules

Keywords: mapping rules, optimizations, mapping translation, Morph-CSV, Morph-GraphQL, FunMap, SDM-RDFizer, GTFS-Madrid-Bench

Abstract

Over the last years, a large and constant growth of data have been made available on the Web. These data are published in many different formats following several schemes. The Semantic Web, and more in detail the Knowledge Graphs, have gained momentum as a result of this explosion of available data and the demand of expressive models to integrate factual knowledge spread across various data sources. Although these results endorse the success of Semantic Web technologies, they also exhort the development of computational tools to scale up knowledge graphs to the large data growth expected for the next years. The proposal of robust methods able to integrate these data sources across the Web is the first step that has to be solved to start seeing the Web as an integrated overall database.

This thesis addresses the problem of constructing knowledge graphs exploiting declarative mapping rules. The contributions presented in this document are:

• A complete evaluation framework for knowledge graph construction engines.
• The concept of mapping translation and its desirable properties.
• Optimizations and enhancements during the access to heterogeneous data sources in the construction of virtual knowledge graphs exploiting the mapping translation concept.
• Optimizations in the construction of materialized knowledge graphs over complex data integration scenarios translating mapping rules among different specifications.

The final conclusions of this thesis reflect that the optimization of the construction of knowledge graphs at scale has been approached for the first time using translations between mapping languages, a novel concept in the state of the art. This has been accompanied by a complete evaluation framework that allows the identification of weakness and strengthens of these engines. Finally, the future lines of work reflect the need to continue researching new methods and techniques that ensure a wide adoption of this type of technology on a large scale in the industry.