5 citations · 6 across the 6 of their papers we have counts for
6 papers · 1 filter
Text-to-SPARQL Generation with Reinforcement Learning: A GRPO-based Approach on DBLP
Jann Pfeifer, Debayan Banerjee, Ricardo Usbeck
Knowledge graph question answering seeks to translate natural language questions into executable queries over knowledge graphs, but existing approaches often rely on large models o…
DBLPLink 2.0 -- An Entity Linker for the DBLP Scholarly Knowledge Graph
Debayan Banerjee, Tilahun Abedissa Taffa, Ricardo Usbeck
In this work we present an entity linker for DBLP's 2025 version of RDF-based Knowledge Graph. Compared to the 2022 version, DBLP now considers publication venues as a new entity t…
Hybrid-SQuAD: Hybrid Scholarly Question Answering Dataset
Tilahun Abedissa Taffa, Debayan Banerjee, Yaregal Assabie +1
Existing Scholarly Question Answering (QA) methods typically target homogeneous data sources, relying solely on either text or Knowledge Graphs (KGs). However, scholarly informatio…
Scholarly Question Answering using Large Language Models in the NFDI4DataScience Gateway
Hamed Babaei Giglou, Tilahun Abedissa Taffa, Rana Abdullah +4
This paper introduces a scholarly Question Answering (QA) system on top of the NFDI4DataScience Gateway, employing a Retrieval Augmented Generation-based (RAG) approach. The NFDI4D…
BERTologyNavigator: Advanced Question Answering with BERT-based Semantics
Shreya Rajpal, Ricardo Usbeck
The development and integration of knowledge graphs and language models has significance in artificial intelligence and natural language processing. In this study, we introduce the…
Leveraging LLMs in Scholarly Knowledge Graph Question Answering
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-…