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
Series
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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