Semantic Representation for Health Data with Support to Spatial and Temporal Information
Health data is one of information that has a close relationship with the spatial and temporal aspects. Spatial and temporal information has now become a more important component in health data analysis. This is based on the fact that there are an increasing aspect of geographical position and time constraints in public health data. Therefore, health data analysis can produce more meaningful information if the spatial and temporal information are well represented. The present model of spatial temporal representation generally offers model that satisfies only one form of representation, either spatial or temporal. This paper proposes a model of health data representation that supports the aspect of spatial temporal information using semantic web approach. The spatial aspect is represented using RCC8 topology relation and compass direction classification. While the temporal aspect is represented using Allen temporal relation. Data representation is implemented in the form of ontology documents. The resulting ontology has DL expressivity of SHOIF(D), and has an average inference ratio of 15%.
Keywords: health data representation, spatial and temporal representation, semantic web, ontology
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ABOUT THE AUTHORS
Dewa Rama
Dewa Rama is a student for master degree in informatics engineering from Bina Nusantara University. Major in software engineering, information retrieval, and knowledge management.
Habibullah Akbar
Habibullah Akbar is a post graduate lecturer in Bina Nusantara University. Major in software engineering, data mining, machine learning, information retrieval, computer vision, and business intelligence.
Dewa Rama
Dewa Rama is a student for master degree in informatics engineering from Bina Nusantara University. Major in software engineering, information retrieval, and knowledge management.
Habibullah Akbar
Habibullah Akbar is a post graduate lecturer in Bina Nusantara University. Major in software engineering, data mining, machine learning, information retrieval, computer vision, and business intelligence.
