Using SWRL and ontological reasoning for the personalization of context-aware assistive services

Skillen, Kerry-Louise; Chen, Liming; Nugent, Chris; Donnelly, Mark; Burns, William C. G.; Solheim, Ivar

doi:10.1145/2504335.2504386

Cited by 5 publications

(4 citation statements)

References 22 publications

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“…To support hotel room personalization, the hotel room framework needs rules to represent domain expert knowledge; to this end, a number of rules have been defined and implemented in an inference engine for HoROnt. Rule-based reasoning is applied as it enables a more functional representation of guest profiles and allows the creation of highly expressive personalization components (Skillen et al , 2013). The personalization of services via rule-based reasoning enables inference of additional services and tailoring of services to suit changing guest needs.…”

Section: The Inference Engine: Rules Definition and Validationmentioning

confidence: 99%

“…Popular recommendation techniques include: collaborative filtering, content-based recommenders, demographic algorithms; knowledge-based recommendation algorithms; and hybrid recommendation systems. Collaborative filtering approaches collect a person’s historical data, such as system interactions and provide recommendations based on similarities with other users (Skillen et al , 2013). They provide a quick and straightforward approach but suffer from the cold start problem (due to very little data available related to the user or item) and privacy issues.…”

Section: Related Workmentioning

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 Inference Engine: Rules Definition and Validationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Hotel room personalization via ontology and rule-based reasoning

Ojino

Mich

Mvungi

2022

IJWIS

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“…Dentro de los ambientes asistidos encontramos el uso de ontologías enfocado a varios aspectos: apoyo la toma de decisiones, como en [3]; darle a sistemas inteligentes una estructura semántica, que pueda convertir estos desarrollos en proyectos fiables, deterministas y escalables, por ejemplo en [20]; o para personalizar los servicios de asistencia creando un modelo ontológico de perfil de usuarios, como en [21].…”

Section: Ontologíasunclassified

Estructuras de Representación del Conocimiento en Ambientes Asistidos

Ruíz¹,

Castillo²,

Gutiérrez

et al. 2014

RCS

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El envejecimiento progresivo de la población es un tema que está llamando la atención de muchos países. Los ambientes asistidos han surgido como una filosofía que proporciona un apoyo considerable a la vida cotidiana de las personas con necesidades específicas (edad avanzada, así como las personas con discapacidad), ayudándolas a mantener una vida independiente y digna. Al ser sistemas críticos por llevar el cuidado de la salud, es conveniente usar metodologías o herramientas que puedan mejorar la fiabilidad de los sistemas. Las ontologías, como herramientas de representación del conocimiento, aportan numerosas ventajas, tales como dar una semántica formal de los datos intercambiados, proporcionando un mecanismo para la representación y el intercambio de dicha información estructurada. Este artículo expone una aplicación de ontologías diseñadas para apoyar el desarrollo de entornos asistidos.

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“…Chen et al [103] modelled smart home domain knowledge at two levels of abstraction, generic activity knowledge and user profile (the specific way to perform an activity). Skillen et al [104] proposed a user profile ontology to provide the personalization for context-aware assistive services, the rules were created by SWRL and used alongside the user profile for the purposes of application personalization. Martin et al [105] combined ontologies that represent information based on a user profile along with machine learning techniques to infer user preferences automatically.…”

Section: Knowledge-driven Based Activity Recognitionmentioning

confidence: 99%

Framework for activity sensing in smart home

Ni¹

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The ability to accurately identify the different activities of daily living (ADLs) is considered as one of the basis to foster new technological solutions inside the home, including services to prolong independent living of the elderly. However, the automated detection of activities is a challenging research area. Existing activity monitoring systems suffer from some practical limitations, such as acceptance, due to privacy issues, as well as technical limitations, such as the complexity of activity recognition, imbalanced data classes or lack of common and suitable representations of the context information.This Thesis proposes a framework for sensing activities in smart home. The main novelty of this research work lies in the application of different approaches simultaneously to obtain a more accurate and proper detection of activities in real environments. The final framework that represents the mixture of the aforementioned approaches includes the contributions described in the following. Firstly, a characterization of the main activities considered in smart home scenarios is proposed which are targeted to older people's independent living. Secondly, it is proposed to use an ensemble of heterogeneous classifiers to recognize twelve activities, including very short-duration transitional activities usually discarded in most of the previous works. We also specify a novel segmentation method that dynamically detects the appropriate starting position of windows from accelerometer data, for the purposes of activity recognition. Thirdly, a human activity ontology is presented to support explicit and flexible representation of reasoning based on activity context information. This ontology has been developed by following the NeON methodology and it is linked with existing technologies capable to handle real-life aspects such as time, space, relations between activities, etc. III ResumenLa habilidad de identificar de forma precisa las distintas actividades de la vida diaria (ADL, Activity of Daily Life) es considerada como una de las bases que potencian el desarrollo de nuevas soluciones tecnológicas dentro del hogar, incluyendo los servicios para prolongar la vida independiente de las personas de edad avanzada. Sin embargo la detección automática de actividades es un área que aún está en las fases iniciales de investigación. Los sistemas de monitorización de actividades existentes sufren de limitaciones prácticas como la aceptación de los sensores por parte de los usuarios debido a la pérdida de privacidad, y limitaciones tecnológicas como el reconocimiento de actividades complejas, datos no preparados para poder ser procesados adecuadamente o la falta de esquemas de representación de información de contexto comunes y adecuados Esta tesis presenta un marco de trabajo para la detección de actividades en el hogar digital. La principal novedad de la tesis radica en la aplicación de distintas aproximaciones de forma simultánea para obtener una detección de actividades más precisa y acorde a escenarios reales.El ...

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Using SWRL and ontological reasoning for the personalization of context-aware assistive services

Cited by 5 publications

References 22 publications

Hotel room personalization via ontology and rule-based reasoning

Hotel room personalization via ontology and rule-based reasoning

Estructuras de Representación del Conocimiento en Ambientes Asistidos

Framework for activity sensing in smart home

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