Work

Demo: Soccer Information Retrieval Via Natural Queries using SoccerRAG

Year: 2024

Type: conference-paper

Abstract: The rapid evolution of digital sports media necessitates sophisticated information retrieval systems that can efficiently parse extensive multimodal datasets. This paper demonstrates SoccerRAG, an innovative framework designed to harness the power of Retrieval Augmented Generation (RAG) and Large Language Models (LLMs) to extract soccer-related information through natural language queries. By leveraging a multimodal dataset, SoccerRAG supports dynamic querying and automatic data validation, enhancing user interaction and accessibility to sports archives. We present a novel interactive user interface (UI) based on the Chainlit framework which wraps around the core functionality, and enable users to interact with the SoccerRAG framework in a chatbot-like visual manner.

Source: International Conference on Content-Based Multimedia Indexing (CBMI)

DOI: 10.1109/cbmi62980.2024.10859233

Authors: Aleksander Theo Strand, Sushant Gautam, Cise Midoglu, Pål Halvorsen

Institutions: OsloMet – Oslo Metropolitan University, Simula Metropolitan Center for Digital Engineering, ForzaSys (Norway)

Language: English

Topic: Information Retrieval and Search Behavior

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Norges Forskningsråd

Award: G6492773548