Search Results for author: Ricardo Usbeck

Found 34 papers, 24 papers with code

BERTologyNavigator: Advanced Question Answering with BERT-based Semantics

no code implementations17 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.

Knowledge Graphs Navigate +4

Leveraging LLMs in Scholarly Knowledge Graph Question Answering

1 code implementation16 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.

Graph Question Answering Language Modelling +4

DBLPLink: An Entity Linker for the DBLP Scholarly Knowledge Graph

1 code implementation14 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.

Entity Embeddings Entity Linking

Biomedical Entity Linking with Triple-aware Pre-Training

no code implementations28 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.

Entity Linking Question Answering

The Role of Output Vocabulary in T2T LMs for SPARQL Semantic Parsing

1 code implementation24 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.

Graph Question Answering Question Answering +1

DBLP-QuAD: A Question Answering Dataset over the DBLP Scholarly Knowledge Graph

1 code implementation23 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).

Question Answering

AmQA: Amharic Question Answering Dataset

no code implementations6 Mar 2023 Tilahun Abedissa, Ricardo Usbeck, Yaregal Assabie

Hence, to foster the research in Amharic QA, we present the first Amharic QA (AmQA) dataset.

Question Answering Reading Comprehension

The Lifecycle of "Facts": A Survey of Social Bias in Knowledge Graphs

no code implementations7 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.

Knowledge Graphs

The Ethical Risks of Analyzing Crisis Events on Social Media with Machine Learning

no code implementations7 Oct 2022 Angelie Kraft, Ricardo Usbeck

Social media platforms provide a continuous stream of real-time news regarding crisis events on a global scale.

Transformer with Tree-order Encoding for Neural Program Generation

1 code implementation30 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.

Code Generation Semantic Parsing

Modern Baselines for SPARQL Semantic Parsing

1 code implementation27 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).

Knowledge Graphs Semantic Parsing

DialoKG: Knowledge-Structure Aware Task-Oriented Dialogue Generation

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.

Dialogue Generation Knowledge Distillation +1

Survey on English Entity Linking on Wikidata

1 code implementation3 Dec 2021 Cedric Möller, Jens Lehmann, Ricardo Usbeck

(3) How do current Entity Linking approaches exploit the specific characteristics of Wikidata?

Entity Linking Knowledge Graphs

SeMantic AnsweR Type prediction task (SMART) at ISWC 2020 Semantic Web Challenge

1 code implementation1 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.

Knowledge Base Question Answering Type prediction +1

Message Passing for Hyper-Relational Knowledge Graphs

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.

Knowledge Graphs Link Prediction

Template-based Question Answering using Recursive Neural Networks

1 code implementation3 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.

Feature Engineering Question Answering +2

Using Multi-Label Classification for Improved Question Answering

1 code implementation24 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.

Classification General Classification +2

Self-Wiring Question Answering Systems

no code implementations6 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.

Question Answering

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