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Creating a Large Knowledge Graph about Scientific Publications for Innovation Forecast




Informationen zur Arbeit

Abschlussarbeitstyp: Bachelor, Master
Betreuer: Michael Färber
Forschungsgruppe: Web Science
Partner: metaphacts
Archivierungsnummer: 4648
Abschlussarbeitsstatus: Offen
Beginn: 15. März 2022
Abgabe: unbekannt

Weitere Informationen

What is the goal?

The goal is to create an RDF knowledge graph about scientific publications in a simi-lar way as described in [1][2]. This includes the following steps:

1. Transforming the data of OpenAlex.org into RDF (considering existing vocabulary in the linked open data cloud).

2. Improving the knowledge graph (e.g., machine learning-based author name dis-ambiguation).

3. Automatically interlinking the knowledge graph with existing knowledge graphs in the linked open data cloud (e.g., Wikidata).


The contents of the Bachelor’s/Master’s thesis are already well defined.


The student will get the chance to publish the work as a research paper and to pro-vide the knowledge graph on the web in collaboration with industry partners. In this way, the student can gain insights into the scientific communities and practices of IT companies.


What are the prerequisites?

You should have interest in knowledge graphs and data mining, as well as solid programming skills (e.g., in Python) to deal with large data files.


[1] http://www.semantic-web-journal.net/system/files/swj2779.pdf

[2] http://dbis.informatik.uni-freiburg.de/content/team/faerber/papers/MAKG_ISWC2019.pdf


Ausschreibung: Download (pdf)