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Lookup NU author(s): Ollie Fox, Dr Giacomo BergamiORCiD, Professor Graham MorganORCiD
This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by Springer Verlag, 2024.
For re-use rights please refer to the publisher's terms and conditions.
This paper proposes LaSSI, a Natural Language Processing (NLP) pipeline contextualising verified Artificial Intelligence (AI) by transforming text via Montague Grammars (MGs). We are approaching from the point-of-view of graphs and logic, in which we achieve explainable sentence similarity in terms of Knowledge Base (KB) expansion and possible worlds semantics. Experiments in the present paper excel current state-of-the-art, Graph Retrieval-Augmented Generation (RAG)-based technologies, through a novel method surpassing vector-based and graph-based sentence similarity metrics.
Author(s): Fox OR, Bergami G, Morgan G
Editor(s): Chbeir R; Ilarri S; Manolopoulos Y; Revesz PZ; Bernardino J; Leung CK
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: The 28th International Database Engineered Applications Symposium IDEAS 2024
Year of Conference: 2024
Pages: 106-121
Print publication date: 16/03/2025
Online publication date: 16/03/2025
Acceptance date: 30/07/2024
Date deposited: 04/10/2024
ISSN: 0302-9743
Publisher: Springer Verlag
URL: https://doi.org/10.1007/978-3-031-83472-1_8
DOI: 10.1007/978-3-031-83472-1_8
ePrints DOI: 10.57711/10pf-9423
Notes: 9783031834721 ebook ISBN.
Library holdings: Search Newcastle University Library for this item
Series Title: Lecture Notes in Computer Science
ISBN: 9783031834714
