Selecting Ontologies for Reuse: Case of Constructing Hotel Room Ontology

Ojino, Ronald; Mich, Luisa; Mvungi, Nerey H.

doi:10.1007/978-3-030-91305-2_13

Cited by 1 publication

(2 citation statements)

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“…The core of HoROnt was developed via a range of steps using the NeOn methodology, which also suggests exploiting existing ontologies to deal with the complexity of building an ontology. The design and implementation of HoROnt is illustrated in detail in Ojino (2020) and Ojino et al (2021). The explanation given in this section is abbreviated to that necessary to understand HoROnt's extension to create the knowledge base.…”

Section: The Knowledge Base: Populating Hotel Room Ontologymentioning

confidence: 99%

“…This paper proposes a hotel room personalization framework based on the hotel room ontology (HoROnt), and an attached inference engine. To this end, the core ontology, modeling the hotel room domain (Ojino et al, 2021), available at Github (https://github.com/ ronojinx/ontology/blob/main/HotelroomOnt%20(2).owl), is: populated to create a knowledge base storing instances of the ontology concepts; used as input for a new module, a rule-based inference engine. The knowledge base allows to describe both room characteristics and services, and guest profiles.…”

Section: Introductionmentioning

confidence: 99%

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Hotel room personalization via ontology and rule-based reasoning

Ojino

Mich

Mvungi

2022

IJWIS

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Purpose The increasingly competitive hotel industry and emerging customer trends where guests are more discerning and want a personalized experience has led to the need of innovative applications. Personalization is much more important for hotels, especially now in the post-COVID lockdown era, as it challenges their business model. However, personalization is difficult to design and realize due to the variety of factors and requirements to be considered. Differences are both in the offer (hotels and their rooms) and demand (customers’ profiles and needs) in the accommodation domain. As for the implementation, critical issues are in hardware-dependent and vendor-specific Internet of Things devices which are difficult to program. Additionally, there is complexity in realizing applications that consider varying customer needs and context via existing personalization options. This paper aims to propose an ontological framework to enhance the capabilities of hotels in offering their accommodation and personalization options based on a guest’s characteristics, activities and needs. Design/methodology/approach A research approach combining both quantitative and qualitative methods was used to develop a hotel room personalization framework. The core of the framework is a hotel room ontology (HoROnt) that supports well-defined machine-readable descriptions of hotel rooms and guest profiles. Hotel guest profiles are modeled via logical rules into an inference engine exploiting reasoning functionalities used to recommend hotel room services and features. Findings Both the ontology and the inference engine module have been validated with promising results which demonstrate high accuracy. The framework leverages user characteristics, and dynamic contextual data to satisfy guests’ needs for personalized service provision. The semantic rules provide recommendations to both new and returning guests, thereby also addressing the cold start issue. Originality/value This paper extends HoROnt in two ways, to be able to add: instances of the concepts (room characteristics and services; guest profiles), i.e. to create a knowledge base, and logical rules into an inference engine, to model guests’ profiles and to be used to offer personalized hotel rooms. Thanks to the standards adopted to implement personalization, this framework can be integrated into existing reservation systems. It can also be adapted for any type of accommodation since it is broad-based and personalizes varying features and amenities in the rooms.

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Section: The Knowledge Base: Populating Hotel Room Ontologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%