Abstract
In this work, we present an advanced semantic search engine dedicated to travel offers, allowing the user to create queries in the Natural Language. We started with the Polish language in focus. Search for e-commerce requires a different set of methods and algorithms than search for travel, search for corporate documents, for law documents, for medicine, etc. In travel, the complexity of data is bigger than in other domains, and the search process requires more parameters. In e-commerce, one product has 1 price, while in travel, one product (holiday package) has got tens of thousands of prices depending on time, board type, room type, number of people, children’s age, etc. Providing a search for one middle-size tour operator, we need to search within hundreds of millions documents.
We present a set of methods based on natural language processing to improve the search for travel. We also present our new application for annotating travel offers, prepared in a human-in-the-loop paradigm that enables iterative system improvement. We also show a large dataset containing more than 3,000 manually constructed queries and more than 23,000 manually annotated answers, a large fraction by at least two independent experts, and a semi-automatically constructed ontology of tourism terms in OWL format containing nearly 2,000 concept classes.
This work was supported by the European Regional Development Fund as a part of the 2014-2020 Smart Growth Operational Programme: (1) Intelligent travel search system based on natural language understanding algorithms, project no. POIR.01.01.01-00-0798/19; (2) CLARIN - Common Language Resources and Technology Infrastructure, project no. POIR.04.02.00-00C002/19.
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Gawron, K. et al. (2022). Multi-module Natural Language Search Engine for Travel Offers. In: Bădică, C., Treur, J., Benslimane, D., Hnatkowska, B., Krótkiewicz, M. (eds) Advances in Computational Collective Intelligence. ICCCI 2022. Communications in Computer and Information Science, vol 1653. Springer, Cham. https://doi.org/10.1007/978-3-031-16210-7_6
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