Michael Färber is the deputy professor (W3) of the research group Web Science at the KIT-institute AIFB since October 1, 2020.
Michael's research interests:
- natural language processing,
- machine learning, and
- knowledge representation (e.g., knowledge graphs).
Current research foci:
- scholarly data mining and
- C-Rex: http://c-rex.org
- ... recommends citations for given texts.
- PaperHunter: http://paperhunter.net
- ... provides, among other things, the sentences in which searched papers are cited.
- ScholarSight: http://scholarsight.org
- ... allows the exploration of trends from scientific concepts.
- Linked Crunchbase: http://linked-crunchbase.org
- ... allows to query information about startups and innovative companies in the Semantic Web format RDF.
Recently created data sets:
- DSKG: http://dskg.org
- ...a knowledge graph representing datasets.
- unarXive: http://unarxive.org
- ... contains the full texts of all papers on arXive.org with further annotations.
- Microsoft Academic Knowledge Graph: http://ma-graph.org
- ... a knowledge graph containing the metadata of almost all publications in all scientific disciplines.
- FAIRnets: https://doi.org/10.5281/zenodo.3885249
- ...a knowledge graph with metadata about neural networks.
Open Positions and Theses
Open student assistant job (Hiwi) in the area of machine learning, natural language processing, and/or Semantic Web technologies: 
Michael Färber has supervised around 40 Bachelor/Master theses. Current calls for Bachelor/Master thesis:
|Thema4420||Wie fair sind Forscher? Eine Analyse von Zerrungen bzgl. Zitaten in wissenschaftlichen Publikationen|
|Thema4421||Implementing an Approach for Linking Text to the Knowledge Graph Wikidata|
|Thema4423||Automatically Recommending Citations for Texts Using Neural Networks|
|Thema4554||Google, Microsoft, & Co. – How Big is the Influence of Enterprises on Computer Science Research?|
|Thema4574||Deep Learning + Knowledge Graphs|
|Thema4742||Übersicht über aktuelle Forschung zu Verzerrungen in der Wissenschaft|
All topics are open to English and German-speaking students.
Many of the thesis topics can also be written at a partner institution abroad (e.g. in Japan, Italy, France) and funded by the DAAD, given that the application is made one year in advance. More information under Web_Science/DAAD-Stipendium/en.
- Semantic Search, Knowledge Representation And Reasoning, Machine Learning, Text Mining, Semantical Annotation, Information Extraction, Natural Language Processing, Digital Libraries, Knowledge Discovery, Data Mining, Artificial Intelligence, Data Science, Semantic Web, Trustworthy AI