RML family of RDF-based knowledge graph construction languages

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Perustieteiden korkeakoulu | Bachelor's thesis
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Date

2024-04-26

Department

Major/Subject

Data Science

Mcode

SCI3095

Degree programme

Aalto Bachelor’s Programme in Science and Technology

Language

en

Pages

23+4

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Abstract

Knowledge graphs play a significant role in structuring large data sets. They help to improve data retrieval and analysis and are utilized widely in AI and data-driven fields. The Resource Description Framework (RDF) is a standard that enables data interoperability on the web by structuring data consistently. It is also a common choice for knowledge graph construction. The data on the web exists in a vast variety of formats. To incorporate the data in these formats into a knowledge graph, they must first be translated to RDF. RML is a mapping language that enables the translation of heterogeneous data to RDF. RML gained wide attention in the information architecture field, and numerous extensions to the language have been developed since it was introduced. Attempts have been made to provide a state-of-the-art review for several mapping languages, but none of them have been focused exclusively on the RML family of languages. This thesis reviews RML tools and language extensions. After the review, the RML language extensions are compared across several characteristics.

Description

Supervisor

Korpi-Lagg, Maarit

Thesis advisor

Kesäniemi, Joonas

Keywords

RDF, RML, knowledge graphs

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https://urn.fi/URN:NBN:fi:aalto-202406114181

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[kand] Perustieteiden korkeakoulu / SCI