Building IoT-Based Applications for Smart Cities: How Can Ontology Catalogs Help?

Gyrard, Amélie; Zimmermann, Antoine; Sheth, Amit P.

doi:10.1109/jiot.2018.2854278

Cited by 61 publications

(31 citation statements)

References 40 publications

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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%

“…Data collection: the data for training and validation is collected from the LOV4IoT catalog 13 . 455 ontology files related to WoT applications could be downloaded through the catalog (each file being treated as a separate ontology).…”

Section: Ranking Model Training and Validationmentioning

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%

“…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). At the core of many state-of-the-art tools that facilitate ontology reuse -such as repositories, search engines and recommender systems -lies the ranking of ontologies for a user query in the form of keywords.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Relevance Mining Approachmentioning

confidence: 99%

Section: Ranking Model Training and Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Popularity-Driven Ontology Ranking Using Qualitative Features

Kolbe¹,

Kubler²,

Traon³

2019

Lecture Notes in Computer Science

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“…In this context, the use of ODPs for smart city models may facilitate ontology development. In addition, as explained in [20], taking into account that the use of ontology catalogs for the IoT and smart city domains is important in order to provide the developers a way to find, choose and reuse the ontologies that fit their needs, providing a list of ODPs to support core domain representation for smart city data may be also helpful. To the best of our knowledge, there is not a list of ODPs for the smart city domain, and the available ODPs in the general ODP catalogs (http://ontologydesignpatterns.org) do not cover all the usual ODPs that may be used in the context of the smart cities well.…”

Section: Introductionmentioning

confidence: 99%

Ontological Representation of Smart City Data: From Devices to Cities

et al. 2018

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Existing smart city ontologies allow representing different types of city-related data from cities. They have been developed according to different ontological commitments and hence do not share a minimum core model that would facilitate interoperability among smart city information systems. In this work, a survey has been carried out in order to study available smart city ontologies and to identify the domains they are representing. Taking into account the findings of the survey and a set of ontological requirements for smart city data, a list of ontology design patterns is proposed. These patterns aim to be easily replicated and provide a minimum set of core concepts in order to guide the development of smart city ontologies.

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