1 code implementation • EMNLP 2021 • Rostislav Nedelchev, Jens Lehmann, Ricardo Usbeck
The automatic evaluation of open-domain dialogues remains a largely unsolved challenge.
Ranked #1 on Dialogue Evaluation on USR-PersonaChat
no code implementations • 17 Jan 2024 • Shreya Rajpal, Ricardo Usbeck
The development and integration of knowledge graphs and language models has significance in artificial intelligence and natural language processing.
1 code implementation • 16 Nov 2023 • Tilahun Abedissa Taffa, Ricardo Usbeck
This paper presents a scholarly Knowledge Graph Question Answering (KGQA) that answers bibliographic natural language questions by leveraging a large language model (LLM) in a few-shot manner.
1 code implementation • 14 Sep 2023 • Debayan Banerjee, Arefa, Ricardo Usbeck, Chris Biemann
In this work, we present a web application named DBLPLink, which performs entity linking over the DBLP scholarly knowledge graph.
no code implementations • 28 Aug 2023 • Xi Yan, Cedric Möller, Ricardo Usbeck
However, a difficulty of linking the biomedical entities using current large language models (LLM) trained on a general corpus is that biomedical entities are scarcely distributed in texts and therefore have been rarely seen during training by the LLM.
1 code implementation • 24 May 2023 • Debayan Banerjee, Pranav Ajit Nair, Ricardo Usbeck, Chris Biemann
In this work, we analyse the role of output vocabulary for text-to-text (T2T) models on the task of SPARQL semantic parsing.
1 code implementation • 23 Mar 2023 • Debayan Banerjee, Pranav Ajit Nair, Ricardo Usbeck, Chris Biemann
To further improve the results, we instruct the model to produce a truncated version of the KG embedding for each entity.
1 code implementation • 23 Mar 2023 • Debayan Banerjee, Sushil Awale, Ricardo Usbeck, Chris Biemann
In this work we create a question answering dataset over the DBLP scholarly knowledge graph (KG).
no code implementations • 6 Mar 2023 • Tilahun Abedissa, Ricardo Usbeck, Yaregal Assabie
Hence, to foster the research in Amharic QA, we present the first Amharic QA (AmQA) dataset.
no code implementations • 7 Oct 2022 • Angelie Kraft, Ricardo Usbeck
Social media platforms provide a continuous stream of real-time news regarding crisis events on a global scale.
no code implementations • 7 Oct 2022 • Angelie Kraft, Ricardo Usbeck
Knowledge graphs are increasingly used in a plethora of downstream tasks or in the augmentation of statistical models to improve factuality.
1 code implementation • 30 May 2022 • Klaudia-Doris Thellmann, Bernhard Stadler, Ricardo Usbeck, Jens Lehmann
While a considerable amount of semantic parsing approaches have employed RNN architectures for code generation tasks, there have been only few attempts to investigate the applicability of Transformers for this task.
1 code implementation • 13 May 2022 • Longquan Jiang, Ricardo Usbeck
Existing approaches on Question Answering over Knowledge Graphs (KGQA) have weak generalizability.
1 code implementation • 27 Apr 2022 • Debayan Banerjee, Pranav Ajit Nair, Jivat Neet Kaur, Ricardo Usbeck, Chris Biemann
In this work, we focus on the task of generating SPARQL queries from natural language questions, which can then be executed on Knowledge Graphs (KGs).
1 code implementation • Findings (NAACL) 2022 • Md Rashad Al Hasan Rony, Ricardo Usbeck, Jens Lehmann
Task-oriented dialogue generation is challenging since the underlying knowledge is often dynamic and effectively incorporating knowledge into the learning process is hard.
1 code implementation • ACL 2022 • Md Rashad Al Hasan Rony, Liubov Kovriguina, Debanjan Chaudhuri, Ricardo Usbeck, Jens Lehmann
Secondly, it should consider the grammatical quality of the generated sentence.
1 code implementation • 31 Jan 2022 • Aleksandr Perevalov, Dennis Diefenbach, Ricardo Usbeck, Andreas Both
The ability to have the same experience for different user groups (i. e., accessibility) is one of the most important characteristics of Web-based systems.
Ranked #1 on Knowledge Base Question Answering on QALD-9-Plus
1 code implementation • LREC 2022 • Aleksandr Perevalov, Xi Yan, Liubov Kovriguina, Longquan Jiang, Andreas Both, Ricardo Usbeck
In particular, the latest and most-used datasets in the KGQA community, LC-QuAD and QALD, miss providing central and up-to-date points of trust.
no code implementations • 7 Dec 2021 • Nandana Mihindukulasooriya, Mohnish Dubey, Alfio Gliozzo, Jens Lehmann, Axel-Cyrille Ngonga Ngomo, Ricardo Usbeck, Gaetano Rossiello, Uttam Kumar
The Semantic Answer Type and Relation Prediction Task (SMART) task is one of the ISWC 2021 Semantic Web challenges.
1 code implementation • 3 Dec 2021 • Cedric Möller, Jens Lehmann, Ricardo Usbeck
(3) How do current Entity Linking approaches exploit the specific characteristics of Wikidata?
1 code implementation • 11 Mar 2021 • Daniel Vollmers, Rricha Jalota, Diego Moussallem, Hardik Topiwala, Axel-Cyrille Ngonga Ngomo, Ricardo Usbeck
In this paper, we present a novel QA approach, dubbed TeBaQA.
1 code implementation • COLING 2020 • Rostislav Nedelchev, Jens Lehmann, Ricardo Usbeck
Computer-based systems for communication with humans are a cornerstone of AI research since the 1950s.
1 code implementation • 1 Dec 2020 • Nandana Mihindukulasooriya, Mohnish Dubey, Alfio Gliozzo, Jens Lehmann, Axel-Cyrille Ngonga Ngomo, Ricardo Usbeck
Each year the International Semantic Web Conference accepts a set of Semantic Web Challenges to establish competitions that will advance the state of the art solutions in any given problem domain.
1 code implementation • EMNLP 2020 • Mikhail Galkin, Priyansh Trivedi, Gaurav Maheshwari, Ricardo Usbeck, Jens Lehmann
We also demonstrate that existing benchmarks for evaluating link prediction (LP) performance on hyper-relational KGs suffer from fundamental flaws and thus develop a new Wikidata-based dataset - WD50K.
Ranked #2 on Link Prediction on JF17K
no code implementations • LREC 2020 • Rostislav Nedelchev, Ricardo Usbeck, Jens Lehmann
Dialogue systems for interaction with humans have been enjoying increased popularity in the research and industry fields.
1 code implementation • LREC 2020 • Georg Rehm, Dimitrios Galanis, Penny Labropoulou, Stelios Piperidis, Martin Welß, Ricardo Usbeck, Joachim köhler, Miltos Deligiannis, Katerina Gkirtzou, Johannes Fischer, Christian Chiarcos, Nils Feldhus, Julián Moreno-Schneider, Florian Kintzel, Elena Montiel, Víctor Rodríguez Doncel, John P. McCrae, David Laqua, Irina Patricia Theile, Christian Dittmar, Kalina Bontcheva, Ian Roberts, Andrejs Vasiljevs, Andis Lagzdiņš
With regard to the wider area of AI/LT platform interoperability, we concentrate on two core aspects: (1) cross-platform search and discovery of resources and services; (2) composition of cross-platform service workflows.
1 code implementation • 3 Apr 2020 • Ram G Athreya, Srividya Bansal, Axel-Cyrille Ngonga Ngomo, Ricardo Usbeck
When the top-2 most likely templates were considered the model achieves an accuracy of 0. 945 on the LC-QuAD dataset and 0. 786 on the QALD-7 dataset.
1 code implementation • 29 May 2018 • Diego Moussallem, Ricardo Usbeck, Michael Röder, Axel-Cyrille Ngonga Ngomo
A plethora of Entity Linking (EL) approaches has recently been developed.
1 code implementation • WS 2018 • Axel-Cyrille Ngonga Ngomo, Michael Röder, Diego Moussallem, Ricardo Usbeck, René Speck
The main advantage of our benchmarks is that they can be readily generated at any time.
1 code implementation • 24 Oct 2017 • Ricardo Usbeck, Michael Hoffmann, Michael Röder, Jens Lehmann, Axel-Cyrille Ngonga Ngomo
In particular, we develop a multi-label classification-based metasystem for question answering over 6 existing systems using an innovative set of 14 question features.
1 code implementation • 17 Jul 2017 • Diego Moussallem, Ricardo Usbeck, Michael Röder, Axel-Cyrille Ngonga Ngomo
Currently, the best performing approaches rely on trained mono-lingual models.
no code implementations • 6 Nov 2016 • Ricardo Usbeck, Jonathan Huthmann, Nico Duldhardt, Axel-Cyrille Ngonga Ngomo
Based on these descriptions, our approach will be able to automatically compose QA systems using a data-driven approach automatically.
no code implementations • LREC 2014 • Michael R{\"o}der, Ricardo Usbeck, Sebastian Hellmann, Daniel Gerber, Andreas Both
Extracting Linked Data following the Semantic Web principle from unstructured sources has become a key challenge for scientific research.
no code implementations • LREC 2014 • Mohamed Sherif, S Coelho, ro, Ricardo Usbeck, Sebastian Hellmann, Jens Lehmann, Martin Br{\"u}mmer, Andreas Both
In the last couple of years the amount of structured open government data has increased significantly.