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Faculté des Sciences appliquées
Faculté des Sciences appliquées
MASTER THESIS
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Master Thesis : A Chatbot-Driven Search Engine for Improved Data Accessibility

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Merle, Corentin ULiège
Promotor(s) : Ittoo, Ashwin ULiège
Date of defense : 4-Sep-2023/5-Sep-2023 • Permalink : http://hdl.handle.net/2268.2/18353
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Title : Master Thesis : A Chatbot-Driven Search Engine for Improved Data Accessibility
Translated title : [fr] Assistant de Recherche pour Améliorer l'Accessibilité des Données
Author : Merle, Corentin ULiège
Date of defense  : 4-Sep-2023/5-Sep-2023
Advisor(s) : Ittoo, Ashwin ULiège
Committee's member(s) : Huynh-Thu, Vân Anh ULiège
Debruyne, Christophe ULiège
Jacquerie, Jean-Louis 
Language : English
Number of pages : 100
Keywords : [fr] Chatbot
[fr] Search Engine
[fr] LLM
[fr] Large Language Model
[fr] Knowledge Graph
Discipline(s) : Engineering, computing & technology > Civil engineering
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master : ingénieur civil en science des données, à finalité spécialisée
Faculty: Master thesis of the Faculté des Sciences appliquées

Abstract

[fr] Increasingly, large organizations are faced with the challenge of making data accessible and understandable to non-expert users. Despite the advances in natural language processing and knowledge representation, turning data into natural language responses that can be understood by a general audience remains a significant challenge. Moreover, this issue is exacerbated by the exponential growth of information and the fragmentation of data into isolated silos, which underscores the urgent need for tools to provide more straightforward, single-point data access.
This thesis aims to address these challenges by introducing the use of Enterprise Knowledge Graphs as a unified data structure for consolidating and representing disparate data sources, coupled with SPARCoder, our ontology-aware Text-to- SPARQL fine-tuned Large Language Model based on StarCoder (Li et al. 2023), capable of querying knowledge graphs to retrieve data using natural language. The proposed natural language "search engine" architecture leverages the strengths of Large Language Models in understanding and generating human-like text, combined with the structured representation of information provided by knowledge graphs. In essence, this approach bridges the gap between complex data and end-users, offering a more accessible interface.
In this work, we undertake a comprehensive description of our proposed system, contrasting its advantages and drawbacks with traditional methods of data access and retrieval as well as other state-of-the-art large language models.
Consequently, we assert that the integration of large language models with knowledge graph querying significantly improves data accessibility for non-expert users. The proposed "search engine" prototype not only facilitates a more intuitive and accessible way of interacting with data but also opens up new possibilities for user interaction, leading to more informed and data-driven decision making.


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Author

  • Merle, Corentin ULiège Université de Liège > Master ingé. civ. sc. don. à . fin.

Promotor(s)

Committee's member(s)

  • Huynh-Thu, Vân Anh ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique
    ORBi View his publications on ORBi
  • Debruyne, Christophe ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Représentation et ingénierie des données
    ORBi View his publications on ORBi
  • Jacquerie, Jean-Louis
  • Total number of views 30
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