Domain Ontology for Utility Infrastructure: Coupling the Semantics of CityGML Utility Network ADE and Domain Glossaries

2021 ◽  
Vol 35 (5) ◽  
pp. 04021011
Author(s):  
Xin Xu ◽  
Hubo Cai
Author(s):  
F. Fossatti ◽  
G. Agugiaro ◽  
L. olde Scholtenhuis ◽  
A. Dorée

Abstract. The organisational data models that support the information needs of utility network managers are proprietary and domain-specific, while the emerging national standards in this field often lack lifecycle data representation capabilities. However, multiple types of utility networks can be comprehensively represented with the free and open-source Utility Network Application Domain Extension (ADE) of the international standard CityGML. The Operation & Maintenance (O&M) Domain Ontology is a proposed extended version of the Utility Network ADE that allows for consistent and comprehensive processing, storage and exchange of O&M-related utility network data. So far, this ontology has not yet been implemented in a spatial-relational database. Consequently, the support it offers during routine utility asset management tasks has remained untested. This paper, therefore, tests the support of the O&M domain ontology for asset management and proposes a database implementation of this data model. To this end, it models and loads two utility networks from the campus of the University of Twente, the Netherlands. It tests the ontology’s support for asset management by simulating a street reconstruction project and retrieving necessary project information in relation to a utility’s (a) maintenance history and performance, and (b) site conditions and valve locations. Results show that the implemented model supports projects with rapid, comprehensive, and consistent information about semantic details of utilities. Such data needs yet to be collected and registered systematically to enable future data-driven asset management practices.


2013 ◽  
Vol E96.B (3) ◽  
pp. 730-736 ◽  
Author(s):  
Liru LU ◽  
Hiroshi HARADA ◽  
Ryuhei FUNADA ◽  
Chin-Sean SUM

2019 ◽  
Author(s):  
Bhavan Kumar B ◽  
Vishal B L S R K ◽  
Bhargav K.R. ◽  
Revanth V ◽  
Chintakani Sai Gireesh
Keyword(s):  

2020 ◽  
Author(s):  
Emma Chavez ◽  
Vanessa Perez ◽  
Angélica Urrutia

BACKGROUND : Currently, hypertension is one of the diseases with greater risk of mortality in the world. Particularly in Chile, 90% of the population with this disease has idiopathic or essential hypertension. Essential hypertension is characterized by high blood pressure rates and it´s cause is unknown, which means that every patient might requires a different treatment, depending on their history and symptoms. Different data, such as history, symptoms, exams, etc., are generated for each patient suffering from the disease. This data is presented in the patient’s medical record, in no order, making it difficult to search for relevant information. Therefore, there is a need for a common, unified vocabulary of the terms that adequately represent the diseased, making searching within the domain more effective. OBJECTIVE The objective of this study is to develop a domain ontology for essential hypertension , therefore arranging the more significant data within the domain as tool for medical training or to support physicians’ decision making will be provided. METHODS The terms used for the ontology were extracted from the medical history of de-identified medical records, of patients with essential hypertension. The Snomed-CT’ collection of medical terms, and clinical guidelines to control the disease were also used. Methontology was used for the design, classes definition and their hierarchy, as well as relationships between concepts and instances. Three criteria were used to validate the ontology, which also helped to measure its quality. Tests were run with a dataset to verify that the tool was created according to the requirements. RESULTS An ontology of 310 instances classified into 37 classes was developed. From these, 4 super classes and 30 relationships were obtained. In the dataset tests, 100% correct and coherent answers were obtained for quality tests (3). CONCLUSIONS The development of this ontology provides a tool for physicians, specialists, and students, among others, that can be incorporated into clinical systems to support decision making regarding essential hypertension. Nevertheless, more instances should be incorporated into the ontology by carrying out further searched in the medical history or free text sections of the medical records of patients with this disease.


2021 ◽  
Author(s):  
Adelina Ivanova ◽  
Boryana Deliyska ◽  
Vladislav Todorov

2019 ◽  
Vol 18 (03) ◽  
pp. 953-979 ◽  
Author(s):  
Lingling Zhang ◽  
Minghui Zhao ◽  
Zili Feng

In the era of big data, how to obtain useful knowledge from online news and utilize it as an important basis to make investment decision has become the hotspot of industrial and academic research. At present, there have been research and practice on explicit knowledge acquisition from news, but tacit knowledge acquisition is still under exploration. Based on the general mechanism of domain knowledge, knowledge reasoning, and knowledge discovery, this paper constructs a framework for discovering tacit knowledge from news and applying the knowledge to stock forecasting. The concrete work is as follows: First, according to the characteristics of financial field and the conceptual cube, the conceptual structure of industry–company–product is constructed, and the framework of domain ontology is put forward. Second, with the construction of financial field ontology, the financial news knowledge management framework is proposed. Besides, with the application of attributes in ontology and domain rules extracted from news text, the knowledge reasoning mechanism of financial news is constructed to achieve financial news knowledge discovery. Finally, news knowledge that reflects important information about stock changes is integrated into the traditional stock price forecasting model and the newly proposed model performs well in the empirical analysis of polyester industry.


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