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Our colleague Hidir Arras from patent4science research is co-organizing the 6th PatentSemTech Workshop at #SIGIR2025 in the beautiful city of Padua, Italy! Call for Papers is open 'til April 23: http://ifs.tuwien.ac.at/patentsemtech/
Submit your cutting-edge research, case studies, and demos exploring #AI, #NLP, and #TextMining innovations applied to #IP and related domains.
ReadMe2KG: Github ReadMe to Knowledge Graph #Challenge has been published as part of the Natural Scientific Language Processing and Research Knowledge Graphs #NSLP2025 workshop co-located with #eswc2025. This #NER task aims to complement the NDFI4DataScience KG via information extraction from GitHub README files.
task description: https://nfdi4ds.github.io/nslp2025/docs/readme2kg_shared_task.html
website: https://www.codabench.org/competitions/5396/
@eswc_conf @GenAsefa @shufan @NFDI4DS #NFDIrocks #knowledgegraphs #semanticweb #nlp #informationextraction
![Readme2KG Challenge website screen shot:
The vision of NFDI4DataScience (NFDI4DS) is to support all steps of the complex and interdisciplinary research data lifecycle, including collecting/creating, processing, analyzing, publishing, archiving, and reusing resources in Data Science and Artificial Intelligence. GitHub is a popular platform for hosting and collaborating on software projects. In the context of research, authors can use GitHub repositories to share the datasets, models, and source code of experiments in the paper. These repositories can provide implementation details and facilitate the exploration and reproduction of research results. Each GitHub repository typically includes a README.md file, which serves as an introductory document for the project. READMEs are usually written in Markdown format and provide key information such as the project’s purpose, setup instructions, usage examples, and often links to the original research paper. Aiming to enhance the NDFI4DS-KG[1] with information from GitHub README files, a fine-grained Named Entity Recognition task is proposed.](https://cdn.masto.host/sigmoidsocial/media_attachments/files/113/911/853/539/988/035/original/3685008c425e43d8.png "Readme2KG Challenge website screen shot:
The vision of NFDI4DataScience (NFDI4DS) is to support all steps of the complex and interdisciplinary research data lifecycle, including collecting/creating, processing, analyzing, publishing, archiving, and reusing resources in Data Science and Artificial Intelligence. GitHub is a popular platform for hosting and collaborating on software projects. In the context of research, authors can use GitHub repositories to share the datasets, models, and source code of experiments in the paper. These repositories can provide implementation details and facilitate the exploration and reproduction of research results. Each GitHub repository typically includes a README.md file, which serves as an introductory document for the project. READMEs are usually written in Markdown format and provide key information such as the project’s purpose, setup instructions, usage examples, and often links to the original research paper. Aiming to enhance the NDFI4DS-KG[1] with information from GitHub README files, a fine-grained Named Entity Recognition task is proposed.")
Our colleague @MahsaVafaie is presenting at the #ISWC2024 Doctoral Consortium her work on "German National Socialist Injustice on the #SemanticWeb: from archival records to a knowledge graph"
presentation: https://zenodo.org/records/14070332
@fiz_karlsruhe #PhD #knowledgegraphs #digitalhumanities #dh #AI @LandesarchivBW #iswc #ocr #informationextraction #llms @bmf
Today I' attending the @Textplus #TextplusPlenary2024 at Schloss Mannheim @unimannheim
I will present a poster about our work on #InformationExtraction from tables in old German magazines.
GOT (General OCR Theory) is a 580M end-to-end OCR-2.0 model now on Hugging Face 
"GOT consists of a Vision-Encoder to convert images into transformers images into tokens and a decoder for generating OCR outputs in various formats (e.g., plain text, markdown, Mathpix). GOT is designed to handle complex tasks like sheets, formulas, and geometric shapes."
Model: https://huggingface.co/ucaslcl/GOT-OCR2_0
GitHub: https://github.com/Ucas-HaoranWei/GOT-OCR2.0/
paper: https://arxiv.org/abs/2409.01704
Yesterday after the successful PhD defense of Nicolas Heist at University of Mannheim together with Chris Bizer and Heiko Paulheim. Congratulations Dr. Nico!
Thesis title: Exploiting semi-structured information in Wikipedia for Knowledge Graph Construction
https://www.uni-mannheim.de/dws/news/nicolas-heist-defended-his-thesis/
#knowledgegraphs #wikipedia #dbpedia #informationextraction #llms @fiz_karlsruhe @fizise @KIT_Karlsruhe
Nice overview about LLMs for data annotation including paper references of papers with open source code & data.
Zhen Tan et al, Large Language Models for Data Annotation: A Survey, https://arxiv.org/abs/2402.13446
Information Service Engineering is the denomination of our research group at @fiz_karlsruhe as well as of my chair at @KIT_Karlsruhe
...and this is how #Midjourney imagines how "Information Service Engineering" might look like ;-)
Our research focus lies on on #knowledgegraphs
#informationextraction
#knowledgeengineering
#ontologies #researchdatamanagement
#exploratorysearch #semanticsearch
#nlp #aiart #generativeai @sourisnumerique @tabea @sashabruns @MahsaVafaie @enorouzi
Now you can watch a session of
Principles of Data Science course by Mohammad Hajiaghayi: NLP & Word2Vec
(We will put other sessions of the course in near future)
https://youtube.com/watch?v=Tq9tA989Xys&t=923s
(FEEL FREE TO SUBSCRIBE TO YOUTUBE
@hajiaghayi
FOR FUTURE LESSONS) #InformationExtraction
"Hammer PDF: An Intelligent PDF Reader for Scientific Papers"
... Hammer PDF has the following four innovative features: (1) information extraction ability, which can locate and mark spans like terms and other entities; (2) information extension ability, which can present relevant academic content of a paper, such as citations, references, codes, videos, blogs, ...