1 code implementation • 22 Apr 2024 • Xiaofei Zhu, Liang Li, Stefan Dietze, Xin Luo
To the end, in this paper, we propose a novel model named Multi-level Sequence Denoising with Cross-signal Contrastive Learning (MSDCCL) for sequential recommendation.
no code implementations • 12 Apr 2024 • Raia Abu Ahmad, Jennifer D'Souza, Matthäus Zloch, Wolfgang Otto, Georg Rehm, Allard Oelen, Stefan Dietze, Sören Auer
We design a specific application of the ORKG-Dataset semantic model based on 40 diverse research datasets on scientific information extraction.
no code implementations • 8 Apr 2024 • Wolfgang Otto, Sharmila Upadhyaya, Stefan Dietze
This paper describes our participation in the Shared Task on Software Mentions Disambiguation (SOMD), with a focus on improving relation extraction in scholarly texts through generative Large Language Models (LLMs) using single-choice question-answering.
no code implementations • 2 Apr 2024 • Stephan Linzbach, Dimitar Dimitrov, Laura Kallmeyer, Kilian Evang, Hajira Jabeen, Stefan Dietze
Typically, designing these prompts is a tedious task because small differences in syntax or semantics can have a substantial impact on knowledge retrieval performance.
1 code implementation • 30 Mar 2024 • Marc Feger, Stefan Dietze
Twitter has emerged as a global hub for engaging in online conversations and as a research corpus for various disciplines that have recognized the significance of its user-generated content.
1 code implementation • 15 Dec 2023 • Leon Mlodzian, Zhigang Sun, Hendrik Berkemeyer, Sebastian Monka, Zixu Wang, Stefan Dietze, Lavdim Halilaj, Juergen Luettin
Further, we present nuScenes Knowledge Graph (nSKG), a knowledge graph for the nuScenes dataset, that models explicitly all scene participants and road elements, as well as their semantic and spatial relationships.
no code implementations • 16 Nov 2023 • Wolfgang Otto, Matthäus Zloch, Lu Gan, Saurav Karmakar, Stefan Dietze
Named Entity Recognition (NER) models play a crucial role in various NLP tasks, including information extraction (IE) and text understanding.
Ranked #1 on Scholarly Named Entity Recognition on GSAP-NER
no code implementations • 11 Aug 2023 • Jeff Z. Pan, Simon Razniewski, Jan-Christoph Kalo, Sneha Singhania, Jiaoyan Chen, Stefan Dietze, Hajira Jabeen, Janna Omeliyanenko, Wen Zhang, Matteo Lissandrini, Russa Biswas, Gerard de Melo, Angela Bonifati, Edlira Vakaj, Mauro Dragoni, Damien Graux
Large Language Models (LLMs) have taken Knowledge Representation -- and the world -- by storm.
1 code implementation • 17 Aug 2022 • Fakhri Momeni, Stefan Dietze, Philipp Mayr, Kristin Biesenbender, Isabella Peters
Employing correlation and regression analyses, we describe the relationship between authors affiliated with countries from different income levels, their choice of publishing model, and the citation impact of their papers.
no code implementations • 4 Jul 2022 • Ran Yu, Limock, Stefan Dietze
Web search is among the most frequent online activities.
1 code implementation • 15 Jun 2022 • Salim Hafid, Sebastian Schellhammer, Sandra Bringay, Konstantin Todorov, Stefan Dietze
Scientific topics, claims and resources are increasingly debated as part of online discourse, where prominent examples include discourse related to COVID-19 or climate change.
no code implementations • 7 Jan 2022 • Christian Otto, Markus Rokicki, Georg Pardi, Wolfgang Gritz, Daniel Hienert, Ran Yu, Johannes von Hoyer, Anett Hoppe, Stefan Dietze, Peter Holtz, Yvonne Kammerer, Ralph Ewerth
The emerging research field Search as Learning investigates how the Web facilitates learning through modern information retrieval systems.
1 code implementation • 20 Aug 2021 • David Schindler, Felix Bensmann, Stefan Dietze, Frank Krüger
To the best of our knowledge, SoMeSci is the most comprehensive corpus about software mentions in scientific articles, providing training samples for Named Entity Recognition, Relation Extraction, Entity Disambiguation, and Entity Linking.
no code implementations • 11 Jun 2021 • Christian Otto, Ran Yu, Georg Pardi, Johannes von Hoyer, Markus Rokicki, Anett Hoppe, Peter Holtz, Yvonne Kammerer, Stefan Dietze, Ralph Ewerth
Related work in this field, also called search as learning, has focused on behavioral or text resource features to predict learning outcome and knowledge gain.
no code implementations • 14 Jan 2021 • Renato Stoffalette João, Pavlos Fafalios, Stefan Dietze
Entity linking (EL) is the task of automatically identifying entity mentions in text and resolving them to a corresponding entity in a reference knowledge base like Wikipedia.
no code implementations • 5 Aug 2020 • Masoud Davari, Daniel Hienert, Dagmar Kern, Stefan Dietze
We investigate the relationship between a range of in-session features, in particular, gaze data, with the query terms and train models for predicting query terms.
no code implementations • 29 Jul 2020 • Arjun Roy, Pavlos Fafalios, Asif Ekbal, Xiaofei Zhu, Stefan Dietze
In this context, stance detection aims at identifying the position (stance) of a document towards a claim.
1 code implementation • 25 Jun 2020 • Dimitar Dimitrov, Erdal Baran, Pavlos Fafalios, Ran Yu, Xiaofei Zhu, Matthäus Zloch, Stefan Dietze
Publicly available social media archives facilitate research in the social sciences and provide corpora for training and testing a wide range of machine learning and natural language processing methods.
no code implementations • 13 Dec 2018 • Renato Stoffalette João, Pavlos Fafalios, Stefan Dietze
Entity Linking (EL) is the task of automatically identifying entity mentions in a piece of text and resolving them to a corresponding entity in a reference knowledge base like Wikipedia.
no code implementations • 23 Oct 2018 • Nilamadhaba Mohapatra, Vasileios Iosifidis, Asif Ekbal, Stefan Dietze, Pavlos Fafalios
Entity relatedness has emerged as an important feature in a plethora of applications such as information retrieval, entity recommendation and entity linking.
no code implementations • 1 Mar 2018 • Nicolas Tempelmeier, Elena Demidova, Stefan Dietze
Nevertheless, given the scale and diversity of Web markup data, nodes that provide missing information can be obtained from the Web in large quantities, in particular for categorical properties.