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Local administrations generate large quantities of data due to the processes followed to attend administrative governance issues and the needs of its citizenry. Sadly, in most cases this data is not fully exploited and remains within the institutions, making their reutilization very difficult. Currently, open data initiatives have gained ground wor...
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Addressing a strategy for publishing open and digital research data, this article presents the approach for streamlining and automating the process of storage and conversion of research data to those of semantically queryable data on the web. As the use case for demonstrating and evaluating the digitalization process, the primary datasets from low‐...
Citations
... We propose the domain-specific Hydrogen Ontology (HOLY ) as a structural backbone for the hydrogen sector's R, S, and D processes, serving as a continuously-growing knowledge base for strategic foresight purposes. The development of HOLY is based on the Linked Open Terms (LOT ) approach [9], an established methodology in the semantic literature used in different domains (e.g., in agriculture [10], information and communication technology [11], environmental management and sustainability [12], and in industrial context [13]). HOLY is already being used by the Fraunhofer Institute for Integrated Systems (IIS) for R, S, and D of market insights in the Atlant-H 5 project. ...
... Therefore, synonyms, definitions, and examples for each class are included. 12 Cf., https://energy.ec.europa.eu/topics/energy-systems-integration/hydrogen_en. 13 Cf., https://www.ballard.com/fuel-cell-solutions/fuel-cell-power-products. ...
This paper presents the Hydrogen Ontology ( HOLY ), a domain ontology modeling the complex and dynamic structures of hydrogen-based markets. The hydrogen economy has become a politically and economically crucial sector for the transition to renewable energy, accelerating technological and socio-economic innovations. However, the attainment of market insights requires a large variety of informational concepts which are predominantly found in unstructured text data. HOLY provides the necessary structure for the representation of these concepts. Through a top-down approach, HOLY defines taxonomies based on a hierarchical structure of products and applications. In addition, to ensure reusability, the ontology incorporates components from established ontologies in its structure. As a result, HOLY consists of over 100 classes defining information about organizations, projects, components, products, applications, markets, and indicators. Hence, our work contributes to the systemic modeling of the hydrogen domain with a focus on its value chain. Formally, we represent and validate the ontology with Semantic Web Technologies. HOLY includes lexical-semantic information (e.g., synonyms, hyponyms, definitions, and examples) to simplify data integration into knowledge acquisition systems. Therefore, we provide a foundation for the retrieval, storage, and delivery of market insights. A first application based on HOLY at the Fraunhofer IIS offers an up-to-date market overview of developments in the fuel cell environment.
... The previous work [1] explains that the CIM layer in the ReM-AM middleware is composed by the sound ontology from Ref. [21], and the acoustic vocabulary developed in Ref. [22] (see Figure 1a). They have two description hierarchies: part-of or has, based on the inclusion relationship; and is-a, based on the integration relationship. ...
Purpose
The Reflective Middleware for Acoustic Management (ReM-AM), based on the Middleware for Cloud Learning Environments (AmICL), aims to improve the interaction between users and agents in a Smart Environment (SE) using acoustic services, in order to consider the unpredictable situations due to the sounds and vibrations. The middleware allows observing, analyzing, modifying and interacting in every state of a SE from the acoustics. This work details an extension of the ReM-AM using the ontology-driven architecture (ODA) paradigm for acoustic management.
Design/methodology/approach
This work details an extension of the ReM-AM using the ontology-driven architecture (ODA) paradigm for acoustic management. In this paper are defined the different domains of knowledge required for the management of the sounds in SEs, which are modeled using ontologies.
Findings
This work proposes an acoustics and sound ontology, a service-oriented architecture (SOA) ontology, and a data analytics and autonomic computing ontology, which work together. Finally, the paper presents three case studies in the context of smart workplace (SWP), ambient-assisted living (AAL) and Smart Cities (SC).
Research limitations/implications
Future works will be based on the development of algorithms for classification and analysis of sound events, to help with emotion recognition not only from speech but also from random and separate sound events. Also, other works will be about the definition of the implementation requirements, and the definition of the real context modeling requirements to develop a real prototype.
Practical implications
In the case studies is possible to observe the flexibility that the ReM-AM middleware based on the ODA paradigm has by being aware of different contexts and acquire information of each, using this information to adapt itself to the environment and improve it using the autonomic cycles. To achieve this, the middleware integrates the classes and relations in its ontologies naturally in the autonomic cycles.
Originality/value
The main contribution of this work is the description of the ontologies required for future works about acoustic management in SE, considering that what has been studied by other works is the utilization of ontologies for sound event recognition but not have been expanded like knowledge source in an SE middleware. Specifically, this paper presents the theoretical framework of this work composed of the AmICL middleware, ReM-AM middleware and the ODA paradigm.
... Ontologies may be created manually from scratch by combining existing ontologies or learned automatically or semiautomatically using an ontology learning process [65]. According to Espinoza-Arias et al. [66], methods for developing ontologies are intended to assist developers through the entire process, to transform the art of building ontologies into an engineering process. The technique employed by the researchers to build the ontology is identified and presented by this criterion. ...
Knowledge management (KM) comprises several processes, and one of the most important is the knowledge sharing activities. The ability of an organization to manage its organizational knowledge, specifically in the context of knowledge sharing, may enhance the organization’s overall performance. Various approaches and technologies have been introduced to assist the process in achieving that target. Ontology as one of the knowledge representation methods has been becoming popular to assist knowledge sharing in the organization. Previous reviews have mainly focused on general KM issues, with little emphasis on the use of ontology in knowledge sharing. Thus, this article reviews several ontology-based KM tools that can support knowledge-sharing activities to provide some insight into future research in this area. Thirteen ontology-based KM tools were reviewed using ten elements’ comparison criteria: the motivation, domain, source of knowledge, type of knowledge, knowledge extraction, knowledge input process, knowledge retrieval process, knowledge sharing technology, source of ontology component, and ontology methodology. The review found that several elements can be further studied to improve KM implementation in the organization, especially on the knowledge sharing dimension. This includes simplifying the knowledge extraction and retrieval process to explore various knowledge domains from implicit knowledge sources. The review’s outcome also includes proposed components and functions of an ideal ontology-based KM tool.
... Though important for economic growth and efficient transport, this has led to adverse effects on the environment and human health (Brookhouser et al. 1992;Ingle et al. 2005;Öhrström et al. 2006;Banerjee 2012;Khan et al. 2008). While considering the present situation of vehicular traffic noise and its adverse health effects, an accurate forecasting measure would help to analyse and reduce its pollution (Arias et al. 2019). ...
Noise pollution has been a global concern among the scientific community as it can cause long term and short-term adverse effects on human health. Vehicular traffic is one of the major causes of noise pollution. In the present work, an efficient methodology to predict the traffic noise level (Leq dBA) based upon vehicular traffic volume, percentage of heavy vehicles and average speed of vehicles has been proposed. To predict the noise level, adaptive neuro fuzzy inference system (ANFIS) has been developed and a detailed comparative analysis has been performed with conventional soft-computing techniques such as neural networks (NN), generalized linear model (GLM), random forests (RF), Decision Trees and Support Vector Machine (SVM). Implementation of ANFIS proof-of-concept model on testing data has resulted in higher accuracy for noise level prediction within 0.5 dBA and yielded significantly lower value of root mean square Error as compared to the conventional techniques. The results of current study signify the efficacy of the proposed method in prediction of traffic noise level and validate its suitability in planning mitigation measures for the new and existing roads. In order to analyse the performance of proposed technique, a case study of the highway locations near the city of Patiala in India has been presented.
... On an estimate, particulate matter particle pollution caused 620000 non-natural deaths in India in the year 2010 itself (Surendra, 2016). Another form of pollution that comes with vehicular emissions is noise pollution, and facts show that noise pollution due to traffic is also a severe concern posing adverse environmental and health effects (Espinoza-Arias et al., 2019;Singh et al., 2020). The excessive traffic on urban roads in cities and highways causes sound pollution, which is triggering hearing ailments in regular traffic commuters (Cai et al., 2019). ...
BACKGROUND AND OBJECTIVES:
The study provides an assessment of fuel wastage, particulate matter particles pollution, and noise pollution at three toll booths near district Varanasi, India. The objective of the study is to analyze the effects of vehicle idling conditions on road tolls in terms of pollution and fuel wastage.
METHODS:
The study used mathematical formulation on queuing observations for assessment of fuel wastage due to vehicle idling at toll booths. Handheld device HT-9600 Air Particle counter was used for getting the readings of PM2.5 and PM10. SL10 noise meter of Extech Instruments was used for measuring the noise levels at the selected three toll booths of Dafi Toll Booth, Lalanagar Toll Booth, and Mohania Toll Booth.
FINDING:
The study assessed a greater extent of fuel wastage at all the three toll booths with maximum fuel wastage at Dafi Toll booth due to vehicle idling. In terms of air pollution, severe levels of particulate matter particles were observed over all the three toll booths. The noise levels over the three toll booths were also observed significantly high.
CONCLUSION:
The study suggested that serious measures are required to control and regulate toll booths to avoid vehicle idling, which will lead to savings of fuel and air and noise pollution.
The environmental damage caused by air pollution has recently become the focus of city council policies. The concept of the green city has emerged as an urban solution by which to confront environmental challenges worldwide and is founded on air pollution levels that have increased meaningfully as a result of traffic in urban areas. Local governments are attempting to meet environmental challenges by developing public traffic policies such as air pollution protocols. However, several problems must still be solved, such as the need to link smart cars to these pollution protocols in order to find more optimal routes. We have, therefore, attempted to address this problem by conducting a study of local policies in the city of Madrid (Spain) with the aim of determining the importance of the vehicle routing problem (VRP), and the need to optimise a set of routes for a fleet. The results of this study have allowed us to propose a framework with which to dynamically implement traffic constraints. This framework consists of three main layers: the data layer, the prediction layer and the event generation layer. With regard to the data layer, a dataset has been generated from traffic data concerning the city of Madrid, and deep learning techniques have then been applied to this data. The results obtained show that there are interdependencies between several factors, such as weather conditions, air quality and the local event calendar, which have an impact on drivers’ behaviour. These interdependencies have allowed the development of an ontological model, together with an event generation system that can anticipate changes and dynamically restructure traffic restrictions in order to obtain a more efficient traffic system. This system has been validated using real data from the city of Madrid.
Ontology Engineering has captured much attention during the last decades leading to the proliferation of numerous works regarding methodologies, guidelines, tools, resources, etc. including topics which are still being investigated. Even though, there are still many open questions when addressing a new ontology development project, regarding how to manage the overall project and articulate transitions between activities or which tasks and tools are recommended for each step. In this work we propose the Linked Open Terms (LOT) methodology, an overall and lightweight methodology for building ontologies based on existing methodologies and oriented to semantic web developments and technologies. The LOT methodology focuses on the alignment with industrial development, in addition to academic and research projects, and software development, that is making ontology development part of the software industry. This methodology includes lessons learnt from more than 20 years in ontological engineering and its application on 18 projects is reported.
