Knowledge Graph Construction with a <i>Façade</i> : A Unified Method to Access Heterogeneous Data Sources on the Web

TLDR

Data integration is a primary use case for RDF Knowledge Graphs, yet heterogeneous web resources in weak‑semantics formats and the complexity of existing KGC frameworks create significant bottlenecks for engineers. This work proposes Facade‑X, a unified method implemented in SPARQL Anything, that lets KG engineers access diverse web data formats using RDF and SPARQL, thereby simplifying data integration. Facade‑X employs a single RDF‑based meta‑model capable of representing any BNF‑expressible file format and any relational database, and is evaluated against state‑of‑the‑art approaches for usability and performance.

Abstract

Data integration is the dominant use case for RDF Knowledge Graphs. However, Web resources come in formats with weak semantics (for example, CSV and JSON), or formats specific to a given application (for example, BibTex, HTML, and Markdown). To solve this problem, Knowledge Graph Construction (KGC) is gaining momentum due to its focus on supporting users in transforming data into RDF. However, using existing KGC frameworks result in complex data processing pipelines, which mix structural and semantic mappings, whose development and maintenance constitute a significant bottleneck for KG engineers. Such frameworks force users to rely on different tools, sometimes based on heterogeneous languages, for inspecting sources, designing mappings, and generating triples, thus making the process unnecessarily complicated. We argue that it is possible and desirable to equip KG engineers with the ability of interacting with Web data formats by relying on their expertise in RDF and the well-established SPARQL query language [ 2 ]. In this article, we study a unified method for data access to heterogeneous data sources with Facade-X, a meta-model implemented in a new data integration system called SPARQL Anything. We demonstrate that our approach is theoretically sound, since it allows a single meta-model, based on RDF, to represent data from (a) any file format expressible in BNF syntax, as well as (b) any relational database. We compare our method to state-of-the-art approaches in terms of usability (cognitive complexity of the mappings) and general performance. Finally, we discuss the benefits and challenges of this novel approach by engaging with the reference user community.