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

De Leo V.;reforgiato recupero diego.
;
Secchi L.
2023-01-01

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.
2023
Inglese
DL4KG 2023. Deep Learning for Knowledge Graphs 2023. Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG 2023) co-located with the 21th International Semantic Web Conference (ISWC 2023). Athens, November 6-10, 2023
CEUR-WS
3559
6
2023 Workshop on Deep Learning for Knowledge Graphs, DL4KG 2023
Esperti anonimi
6-10 November 2023
Athens, Greece
scientifica
BERT; Classification Tasks; Feature Engineering; Hospitality; Knowledge Graphs; Natural Language Processing; Tourism
4 Contributo in Atti di Convegno (Proceeding)::4.1 Contributo in Atti di convegno
Cadeddu, A.; Chessa, A.; De Leo, V.; Fenu, G.; Motta, E.; Osborne, F.; reforgiato recupero, Diego.; Salatino, A.; Secchi, L.
273
9
4.1 Contributo in Atti di convegno
open
info:eu-repo/semantics/conferencePaper
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