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ECIR2026
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CancerRAGent: Evidence-Linked and Safety-Guided Oncology Question Answering
This abstract has open access
Abstract Summary
Abstract ID :
NKDR143
Submission Type
Demos
Submission Topics
Applications Machine Learning and Large Language Models Recommender systems Search and ranking
Associated Sessions
Poster Session (Demos)
Author
Co-Authors
TV
Trung Vo
Japan Advanced Institute of Science and Technology
AT
An Trieu
Japan Advanced Institute of Science and Technology
VT
Vu Tran
Senior Lecturer
,
Japan Advanced Institute Of Science And Technology
YM
Yuji Matsumoto
RIKEN Center for Advanced Intelligence Project (AIP)
LN
Le-Minh Nguyen
Japan Advanced Institute of Science and Technology
Abstracts With Same Type
Abstract ID
Abstract Title
Abstract Topic
Submission Type
Primary Author
NKDR166
CitiLink: Enhancing Municipal Transparency and CitizenEngagement through Searchable Meeting Minutes
Applications Machine Learning and Large Language Models Search and ranking Societally-motivated IR research
Demos
Rodrigo Silva
NKDR168
Context Engineering for Agentic Data Science
Demos
Rishiraj Saha Roy
NKDR169
Creating Specialized RAG-Based Search Engines Using the Open Web Index
Demos
Mr. Alexander Nussbaumer
NKDR156
Enhancing Job Search Effectiveness with LLM-Powered Context-Aware Query Reformulation
Applications Machine Learning and Large Language Models Search and ranking System aspects
Demos
Quang Hieu Vu
NKDR209
GutBrainKB: Exploring the Gut¬CBrain Interaction through aReliable Biomedical KB
Demos
Ornella Irrera
NKDR159
ImageSeek: A Hybrid Text-to-Image Image Retrieval Systemfor Domain-Specific Collections
Applications Machine Learning and Large Language Models Search and ranking
Demos
Rodrigo Duarte
NKDR160
LectureChat: Hybrid RAG over Wikipedia and Multilingual Lectures
Applications Conversational search and recommender systems Societally-motivated IR research
Demos
Markos Dimitsas
NKDR163
MedNuggetizer: Confidence-Based Information Nugget Extraction from Medical Documents
Demos
Gregor Donabauer
NKDR27
OmniRec: The All-In-One Solution for Reproducible and Interoperable Recommender Systems Experimentation
Evaluation researchMachine Learning and Large Language ModelsRecommender systems
Demos
Lukas Wegmeth
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