Declarative RDF construction from in-memory data structures with RML

Ioannis Dasoulas, David Chaves-Fraga, Daniel Garijo, Anastasia Dimou

Keywords: Knowledge Graphs, Mapping Languages, RML

Abstract: Knowledge graphs are often constructed from heterogeneous data sources using declarative mapping languages. Mapping languages define rules that apply ontology terms to raw data to describe how a knowledge graph should be constructed from these raw data. While most mapping languages and systems support knowledge graph construction from different data formats, e.g., CSV, XML or JSON, and different types of data sources, e.g., files, Web APIs or databases, there is still no support for mapping in-memory data structures to knowledge graphs, i.e. data which is temporarily stored in RAM. Currently, this data must first be stored in HDD, locally or in the cloud, for RDF construction systems to access them and construct a knowledge graph. However, writing these data to HDD and reading from HDD is a computationally expensive and redundant task. In this paper, we propose a method to construct RDF graphs from data produced by a software process and stored in RAM. We introduce an extension of RML’s Logical Source to describe data structures produced by software, and exemplify our proposal with Python data structures. We extend a well-known RML system, Morph-KGC, to show the feasibility of our method and validate this extension with two use cases: OpenML, which translates machine learning executions into RDF, and SOMEF, which extracts software metadata from its documentation, converting them to triples. This proposal simplifies the construction of RDF graphs from in-memory data structures stored temporarily in RAM and enables the integration of data stored both in RAM and HDD.