Leveraging Knowledge Graphs with Large Language Models for Classification Tasks in the Tourism Domain

Cadeddu, Andrea; Chessa, Alessandro; De Leo, Vincenzo; Fenu, Gianni; Motta, Enrico; Osborne, Francesco; Reforgiato Recupero, Diego; Salatino, Angelo and Secchi, Luca (2023). Leveraging Knowledge Graphs with Large Language Models for Classification Tasks in the Tourism Domain. In: Deep Learning for Knowledge Graphs 2023. CEUR Workshop Proceedings Vol. 3559 (Alam, Mehwish; Buscaldi, Davide; Cochez, Michael; Osborne, Francesco and Reforgiato Recupero, Diego eds.), article no. 4.

URL: https://ceur-ws.org/Vol-3559/paper-4.pdf

Abstract

Online platforms, serving as the primary conduit for travelers to seek, compare, and secure travel accommodations, require a profound understanding of user dynamics to craft competitive and enticing offerings. Concurrently, recent advancements in Natural Language Processing, particularly large language models, have made substantial strides in capturing the complexity of human language. Simultaneously, knowledge graphs have become a formidable instrument for structuring and categorizing information. This paper introduces a cutting-edge deep learning methodology integrating large language models with domain-specific knowledge graphs to classify tourism offers. It aims at aiding hospitality operators in understanding their accommodation offerings’ market positioning, taking into account the visit propensity and user review ratings, with the goal of optimizing the offers themselves and enhancing their appeal. Comparative analysis against alternative methods on two datasets of London accommodation offers attests to our approach’s effectiveness, demonstrating superior results.

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