activity
20242026
most citedRetrieval-Augmented Generation-based Relation Extraction

6 citations · 10 across the 9 of their papers we have counts for

collaborators

9 papers

cs.AI2026

Reversing Arrows in Large Language Models

Sefika Efeoglu, Adrian Paschke

Large language models (LLMs) have achieved strong performance on text-to-knowledge graph generation and related tasks. Nevertheless, it is still unclear whether they accurately mod…

cs.CL2025

Post-Training Language Models for Continual Relation Extraction

Sefika Efeoglu, Adrian Paschke, Sonja Schimmler

Real-world data, such as news articles, social media posts, and chatbot conversations, is inherently dynamic and non-stationary, presenting significant challenges for constructing…

cs.AI2025

Semantic Web and Creative AI -- A Technical Report from ISWS 2023

Raia Abu Ahmad, Reham Alharbi, Roberto Barile +74

The International Semantic Web Research School (ISWS) is a week-long intensive program designed to immerse participants in the field. This document reports a collaborative effort p…

cs.CL2024★ 2 cited

Relation Extraction with Fine-Tuned Large Language Models in Retrieval Augmented Generation Frameworks

Sefika Efeoglu, Adrian Paschke

Information Extraction (IE) is crucial for converting unstructured data into structured formats like Knowledge Graphs (KGs). A key task within IE is Relation Extraction (RE), which…

cs.DL2024

Converter: Enhancing Interoperability in Research Data Management

Sefika Efeoglu, Zongxiong Chen, Sonja Schimmler +1

Research Data Management (RDM) is essential in handling and organizing data in the research field. The Berlin Open Science Platform (BOP) serves as a case study that exemplifies th…

cs.DL2024

A Continual Relation Extraction Approach for Knowledge Graph Completeness

Sefika Efeoglu

Representing unstructured data in a structured form is most significant for information system management to analyze and interpret it. To do this, the unstructured data might be co…

cs.AI2024★ 2 cited

GraphMatcher: A Graph Representation Learning Approach for Ontology Matching

Sefika Efeoglu

Ontology matching is defined as finding a relationship or correspondence between two or more entities in two or more ontologies. To solve the interoperability problem of the domain…

cs.CL2024

Fine-Grained Named Entities for Corona News

Sefika Efeoglu, Adrian Paschke

Information resources such as newspapers have produced unstructured text data in various languages related to the corona outbreak since December 2019. Analyzing these unstructured…

cs.CL2024★ 6 cited

Retrieval-Augmented Generation-based Relation Extraction

Sefika Efeoglu, Adrian Paschke

Information Extraction (IE) is a transformative process that converts unstructured text data into a structured format by employing entity and relation extraction (RE) methodologies…