The use of Semantic Web technologies for decision support – a survey

Blomqvist, Eva

doi:10.3233/sw-2012-0084

Cited by 81 publications

(45 citation statements)

References 72 publications

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“…Towards this, Semantic Web technologies [18][19][20] provide a rigorous solution to automatically integrate disparate information sources and databases. These technologies and principles have been created by the partners of the World Wide Web Consortium (W3C) [21], so as to enable the expression of both data and rules for reasoning about the data.…”

Section: Definition Of the Target Variables And Data Processingmentioning

confidence: 99%

Cohort Harmonization and Integrative Analysis From a Biomedical Engineering Perspective

Κούρου

Pezoulas

Georga

et al. 2019

IEEE Rev. Biomed. Eng.

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In this review the critical parts and milestones for data harmonization, from the biomedical engineering perspective, are outlined. The need for data sharing between heterogeneous sources pave the way for cohort harmonization; thus, fostering data integration and interdisciplinary research. Unmet needs in chronic as well as in other diseases, can be addressed based on the integration of patient health records and the sharing of information of the clinical picture and outcome. The stratification of patients, the determination of various clinical and outcome features and the identification of novel biomarkers for the different phenotypes of the disease characterize the impact of cohort harmonization in patient-centered clinical research and in precision medicine. Subsequently, the establishment of matching techniques and ontologies for the creation of data schemas are also presented. The exploitation of web technologies and data-collection tools support the opportunities to achieve new levels of integration and interoperability. Ethical and legal issues which arise when sharing and harmonizing individual-level data are discussed in order to evaluate the harmonization potential. Use cases that shape and test the harmonization approach are explicitly analyzed along with their significant results on their research objectives. Finally, future trends and directions are discussed and critically reviewed towards a roadmap in cohort harmonization for clinical medicine.

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Section: Definition Of the Target Variables And Data Processingmentioning

confidence: 99%

Cohort Harmonization and Integrative Analysis From a Biomedical Engineering Perspective

Κούρου

Pezoulas

Georga

et al. 2019

IEEE Rev. Biomed. Eng.

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“…An overview of existing intelligent assistants for data analysis is provided in [68]. In [7] it has been noted that SWT can also have a potential impact in the Decision Support.…”

Section: Semantic Web Technologies For Kddmentioning

confidence: 99%

“…The initial SSN ontology was aligned with DOLCE ultra-lite (DUL) ontology 7 and built around a central Ontology Design Pattern (ODP) called Stimulus-Sensor-Observation (SSO) pattern, describing the relationships between sensors, stimulus, and observations.…”

Section: Ssn Ontologymentioning

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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“…The most relevant use of Semantic Web in DSS context is the "use ontologies and rules as a means of making DSS more intelligent" in data analysis [13]. Much of the works could be viewed as continuation of the Expert Systems family of DSS, because they are using the SW standards such as RDF or OWL as an evolution of the use of raw data into knowledge representation [14,15,16,17,18].…”

Section: Dss and The Semantic Webmentioning

confidence: 99%

The Semantic Web as a Platform Against Risk and Uncertainty in Agriculture

Espinoza

Fernández

Torres

2017

Collaboration in a Data-Rich World

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In this article, we discuss existing literature on DSS in agriculture, on DSS that use data available in the Semantic Web, and on Semantic Web initiatives focusing on agriculture information. Our goal is to assess the readiness of the Semantic Web as a platform to empower DSS that can keep risk and uncertainty in agriculture under control. Key agricultural activities targeted by DSS reported in literature are nutrient management, insect and pest management, land use and planning, environmental change and forecasting, and water and drought management. The most relevant use of Semantic Web in DSS, is in data analysis, as a means of making DSS more intelligent. There are initiatives to produce vocabularies and semantic repositories in the domain of agriculture. However, data and models are still isolated in specific domain repositories, and interoperability is still weak

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The use of Semantic Web technologies for decision support – a survey

Cited by 81 publications

References 72 publications

Cohort Harmonization and Integrative Analysis From a Biomedical Engineering Perspective

Cohort Harmonization and Integrative Analysis From a Biomedical Engineering Perspective

Semantic prediction assistant approach applied to energy efficiency in Tertiary buildings

The Semantic Web as a Platform Against Risk and Uncertainty in Agriculture

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