Ontologies for Web of Things: A Pragmatic Review

Kolchin, Maxim; Klimov, Nikolay; Andreev, Alexey; Shilin, Ivan; Garayzuev, Daniil; Mouromtsev, Dmitry; Заколдаев, Д А

doi:10.1007/978-3-319-24543-0_8

Cited by 15 publications

(6 citation statements)

References 14 publications

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“…En la Tabla 2 se hizo un estudio comparativo de las principales características que implementaron los proyectos enfocados en la interoperabilidad semántica para la IoT. En general, se busca un modelo semántico que capture el contexto-conocimiento por medio de ontologías de dominio [45] y a la vez, permita definir los mecanismos de interacción ente los objetos de la IoT. Los modelos son implementados en Gateways semánticos [46], como agregadores naturales de los dispositivos en una red o entorno de interacción.…”

Section: Arquitecturas Iotunclassified

La Búsqueda de Interacción Semántica en la Web de las Cosas: Una Revisión

Zambrano

Ramírez-González

Builes³

et al. 2020

Investigación e Innovación en Ingenierías

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Objetivo: Presentar un estado del arte alrededor de problemas que surgen al crear servicios complejos de interacción entre sensores en la Web, como: descubrimiento automático, combinación de datos heterogéneos, coordinación y cooperación entre sensores, solucionados con el desarrollo de middlewares semánticos para la Web de las Cosas, sin lograr una cooperación transparente entre ellos, profundizando en aportes en la interacción máquina a máquina. Metodología: La investigación fue de tipo documental a través de un análisis bibliográfico de los últimos 12 años, identificando núcleos temáticos, criterios de selección, fichas descriptivas y realizando abstracción de estas para su desarrollo. Resultados y conclusiones: Los resultados exhiben por núcleo temático las principales componentes del tema, permitiendo una discusión alrededor de los retos por resolver en esta área de investigación, analizando enfoques presentes en el estado actual del conocimiento relacionados con la interacción semántica de la Web de las cosas (WoT).

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Section: Arquitecturas Iotunclassified

La Búsqueda de Interacción Semántica en la Web de las Cosas: Una Revisión

Zambrano

Ramírez-González

Builes³

et al. 2020

Investigación e Innovación en Ingenierías

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“…We propose to derive a popularity measure based on corresponding scientific publications associated with an ontology. We are inspired to follow this approach as a large number of ontologies for WoT application domains emerge from research projects, as evidenced in [1,10,13,16]. Furthermore, it overcomes several limitations of other approaches: (i) as previously discussed, LOD does not provide a reliable source for ontology reuse in WoT application domains; (ii) deriving relevance through user click logs requires access to closed back-ends of existing ontology search engines with a large user base; (iii) human labeling is costly and, unlike mining relevance from scholarly data, does not come with the benefit of being reproducible.…”

Section: Relevance Mining Approachmentioning

confidence: 99%

“…The large number of available ontologies and the fast-paced developments in domains often make it difficult to find and select the most appropriate ontologies. For the WoT case, this is evidenced through extensive surveys in the literature [1,10,13,16]. This does not only concern ontologies with regard to sensors and sensor network setups, but further to sensor observations [13] (e.g., in the context of smart city use cases with regard to the environment, transportation, health, homes, and factories).…”

Section: Introductionmentioning

confidence: 99%

Popularity-Driven Ontology Ranking Using Qualitative Features

Kolbe¹,

Kubler²,

Traon³

2019

Lecture Notes in Computer Science

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Efficient ontology reuse is a key factor in the Semantic Web to enable and enhance the interoperability of computing systems. One important aspect of ontology reuse is concerned with ranking most relevant ontologies based on a keyword query. Apart from the semantic match of query and ontology, the state-of-the-art often relies on ontologies' occurrences in the Linked Open Data (LOD) cloud to determine relevance. We observe that ontologies of some application domains, in particular those related to Web of Things (WoT), often do not appear in the underlying LOD datasets used to define ontologies' popularity, resulting in ineffective ranking scores. This motivated us to investigate -based on the problematic WoT case -whether the scope of ranking models can be extended by relying on qualitative attributes instead of an explicit popularity feature. We propose a novel approach to ontology ranking by (i) selecting a range of relevant qualitative features, (ii) proposing a popularity measure for ontologies based on scholarly data, (iii) training a ranking model that uses ontologies' popularity as prediction target for the relevance degree, and (iv) confirming its validity by testing it on independent datasets derived from the state-of-the-art. We find that qualitative features help to improve the prediction of the relevance degree in terms of popularity. We further discuss the influence of these features on the ranking model.

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“…Other ontologies such as Semanco [49] or the Aemet Network of Ontologies [5] have also been analysed, but are not reflected in this paper. Some of the consulted surveys to identify these ontologies have been [23] and [44]. An interesting comparison between different IoT ontologies is also covered in [69].…”

Section: Existing Ontologies In the Fieldmentioning

confidence: 99%

Semantic prediction assistant approach applied to energy efficiency in Tertiary buildings

Esnaola-Gonzalez

Bermúdez

Fernández

et al. 2018

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Fulfilling occupants' comfort whilst reducing energy consumption is still an unsolved problem in most of tertiary buildings. However, the expansion of the Internet of Things (IoT) and Knowledge Discovery in Databases (KDD) techniques lead to research this matter. In this paper the EEPSA (Energy Efficiency Prediction Semantic Assistant) process is presented, which leverages the Semantic Web Technologies (SWT) to enhance the KDD process for achieving energy efficiency in tertiary buildings while maintaining comfort levels. This process guides the data analyst through the different KDD phases in a semiautomatic manner and supports prescriptive HVAC control strategies. That is, temperature of a space is predicted simulating the activation of HVAC systems at different times and intensities, so that the facility manager can choose the strategy that best fits both the user's comfort needs and energy efficiency. Furthermore, results show that the proposed solution improves the accuracy of predictions.

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Ontologies for Web of Things: A Pragmatic Review

Cited by 15 publications

References 14 publications

La Búsqueda de Interacción Semántica en la Web de las Cosas: Una Revisión

La Búsqueda de Interacción Semántica en la Web de las Cosas: Una Revisión

Popularity-Driven Ontology Ranking Using Qualitative Features

Semantic prediction assistant approach applied to energy efficiency in Tertiary buildings

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