Wiki-TabNER:Advancing Table Interpretation Through Named Entity Recognition

no code implementations • 7 Mar 2024 • Aneta Koleva, Martin Ringsquandl, Ahmed Hatem, Thomas Runkler, Volker Tresp

Finally, we propose a prompting framework for evaluating the newly developed large language models (LLMs) on this novel TI task.

Entity Linking named-entity-recognition +1

Adversarial Attacks on Tables with Entity Swap

no code implementations • 15 Sep 2023 • Aneta Koleva, Martin Ringsquandl, Volker Tresp

The recently proposed tabular language models have reported state-of-the-art results across various tasks for table interpretation.

Column Type Annotation Representation Learning

Active Learning with Tabular Language Models

no code implementations • 8 Nov 2022 • Martin Ringsquandl, Aneta Koleva

Despite recent advancements in tabular language model research, real-world applications are still challenging.

Active Learning Computational Efficiency +5

Named Entity Recognition in Industrial Tables using Tabular Language Models

no code implementations • 29 Sep 2022 • Aneta Koleva, Martin Ringsquandl, Mark Buckley, Rakebul Hasan, Volker Tresp

Specialized transformer-based models for encoding tabular data have gained interest in academia.

Data Augmentation Inductive Bias +4

Generating Table Vector Representations

no code implementations • 28 Oct 2021 • Aneta Koleva, Martin Ringsquandl, Mitchell Joblin, Volker Tresp

High-quality Web tables are rich sources of information that can be used to populate Knowledge Graphs (KG).

Knowledge Graphs Transfer Learning

Knowledge Graphs Evolution and Preservation -- A Technical Report from ISWS 2019

no code implementations • 22 Dec 2020 • Nacira Abbas, Kholoud Alghamdi, Mortaza Alinam, Francesca Alloatti, Glenda Amaral, Claudia d'Amato, Luigi Asprino, Martin Beno, Felix Bensmann, Russa Biswas, Ling Cai, Riley Capshaw, Valentina Anita Carriero, Irene Celino, Amine Dadoun, Stefano De Giorgis, Harm Delva, John Domingue, Michel Dumontier, Vincent Emonet, Marieke van Erp, Paola Espinoza Arias, Omaima Fallatah, Sebastián Ferrada, Marc Gallofré Ocaña, Michalis Georgiou, Genet Asefa Gesese, Frances Gillis-Webber, Francesca Giovannetti, Marìa Granados Buey, Ismail Harrando, Ivan Heibi, Vitor Horta, Laurine Huber, Federico Igne, Mohamad Yaser Jaradeh, Neha Keshan, Aneta Koleva, Bilal Koteich, Kabul Kurniawan, Mengya Liu, Chuangtao Ma, Lientje Maas, Martin Mansfield, Fabio Mariani, Eleonora Marzi, Sepideh Mesbah, Maheshkumar Mistry, Alba Catalina Morales Tirado, Anna Nguyen, Viet Bach Nguyen, Allard Oelen, Valentina Pasqual, Heiko Paulheim, Axel Polleres, Margherita Porena, Jan Portisch, Valentina Presutti, Kader Pustu-Iren, Ariam Rivas Mendez, Soheil Roshankish, Sebastian Rudolph, Harald Sack, Ahmad Sakor, Jaime Salas, Thomas Schleider, Meilin Shi, Gianmarco Spinaci, Chang Sun, Tabea Tietz, Molka Tounsi Dhouib, Alessandro Umbrico, Wouter van den Berg, Weiqin Xu

Although linked open data (LOD) is one knowledge graph, it is the closest realisation (and probably the only one) to a public FAIR Knowledge Graph (KG) of everything.

Common Sense Reasoning Knowledge Graphs