China
Africa
Explore chapters and articles related to this topic
Ontology Modeling
Published in Archana Patel, Narayan C. Debnath, Bharat Bhushan, Semantic Web Technologies, 2023
The language SPARQL Protocol and RDF Query Language (SPARQL) is designed to retrieve information contained in an RDF data model. Though there are several other XML query languages used in querying XML, considering that XML is lower in the semantic web technology stack compared to RDF, it becomes pertinent to have different query languages for RDF. The SPARQL leverages the RDF-based serialized representation of its knowledge to hide the complexity of language constructs, and execute queries. Moreover, there is no canonical RDF serialization for some OWL constructs. SQWRL was built upon SWRL and it replaces the consequent part of SWRL rule with a query. Using the built-in functions of SWRL, the SQWRL is able to extend the rule language as an effective query mechanism. Its advantage over SPARQL when used to query OWL implies that the OWL-based ontology need not be serialized to RDF since the constructs of OWL applies to those of SQWRL.
Semantic Web Technology–Based Secure System for IoT-Enabled E-Healthcare Services
Published in Bhawana Rudra, Anshul Verma, Shekhar Verma, Bhanu Shrestha, Futuristic Research Trends and Applications of Internet of Things, 2022
Nikita Malik, Sanjay Kumar Malik
The Semantic Web is Sir Tim Berners Lee’s vision of a highly intelligent or meaningful web system that aims at associating meaning with the data for the machines to be able to understand and process it globally. This web of linked data provides a better representation of knowledge and serves in decision making, scheduling, and other tasks efficiently by requiring minimum human involvement (Berners Lee, Hendler, & Lassila, 2001). Semantic Web Technologies (SWTs) are the web technologies supporting semantic web, and form a part of its layered architecture or stack. The SWTs contextualize and give meaning to the data, enabling its linking, automation, sharing, reuse and integration across various applications. RDF (Resource Description Framework) and Ontologies are the two most prominently used SWTs for graph-like knowledge representation and common understanding, SWRL (Semantic Web Rule Language) for rules to reason over the knowledge base, and SPARQL (SPARQL Protocol and RDF Query Language) for querying and accessing this shared data (Dragoni, Solanki, & Blomqvist, 2017).
Computer Networks
Published in Vivek Kale, Agile Network Businesses, 2017
The Semantic Web is an extension of the current Web in which information is given well-defined meaning, enabling computers and people to work in cooperation. In the lower part of the architecture, we find three building blocks that can be used to encode text (Unicode), to identify resources on the Web (URLs), and to structure and exchange information (XML). The Resource Description Framework (RDF) is a simple yet powerful data model and language for describing Web resources. The SPARQL Protocol and RDF Query Language (SPARQL) is the de facto standard used to query RDF data. While RDF and RDF Schema provide a model for representing Semantic Web data and for structuring semantic data using simple hierarchies of classes and properties, respectively, the SPARQL language and protocol provide the means to express queries and retrieve information from across diverse Semantic Web data sources. The need for a new language is motivated by the different data models and semantics at the level of XML and RDF, respectively.
Co-simulation of complex engineered systems enabled by a cognitive twin architecture
Published in International Journal of Production Research, 2022
Yuanfu Li, Jinwei Chen, Zhenchao Hu, Huisheng Zhang, Jinzhi Lu, Dimitris Kiritsis
The simulation results of multiple FMUs in one specific simulation scenario are usually stored in one data set, resulting in some obstacles to the extract and analysis of data. Since the digital entity creator usually names a variable with a simplified name, it is difficult for the non-related sub-system creator to comprehend several variables. Therefore, it is necessary to both obtain the simulation results and analyze the simulation result of a specific variable. The ontology can store the knowledge about the model variables and realise the correspondence and description of the simulation results. Thus, the reasoning capability in the CT is fulfilled by reasoning with ontology. The SPARQL is used to reason about simulation results, which are time-series data. SPARQL (SPARQL Protocol and RDF Query Language) is the standard language for querying RDF data (Pérez, Arenas, and Gutierrez 2009).
Flexible, decentralised access control for smart buildings with smart contracts
Published in Cyber-Physical Systems, 2022
Leepakshi Bindra, Kalvin Eng, Omid Ardakanian, Eleni Stroulia
Brick describes a building through a collection of triples (subject – predicate – object), following the Resource Description Framework (RDF) data model. Each triple consists of two entities connected with a relationship, which can be feeds, controls, hasPart, hasPoint, or isLocationOf. The collection of such triples forms a directed graph, where nodes represent the entities and edges represent the relationships between them. Figure 1 shows a subset of Brick entities and their relationships in an example building. The RDF syntax allows for using SPARQL (the RDF query language) to reason about various entities and relationships. For example, it is possible to retrieve sensors and control points that are located in a specific room or floor of a building, and are used to control the operation of a given Variable Air Volume (VAV) system.
API deployment for big data management towards sustainable energy prosumption in smart cities-a layered architecture perspective
Published in International Journal of Sustainable Energy, 2020
Bokolo Anthony Jnr, Sobah Abbas Petersen, Dirk Ahlers, John Krogstie
This layer comprises of relational and non-relational databases such as MySQL database, Couch database, and meta-data records that describe the data which is accessible over the RESTful APIs (Patti et al. 2014). This layer also includes data which is provided by external sources as well as imported data (Tcholtchev et al. 2012). These data sources include metering devices, energy sensor data, public energy data, energy trading data extracted from the web or provided by government, public establishments and other organisations, for instance the municipal energy provider (Chaturvedi and Kolbe 2018). All these processed data are catalogued and stored as datasets by data space layer in order to provide energy prosumers with services that are based on these data accessed by APIs in various forms making the data space layer to be self-sustainable (Khan, Kiani, and Soomro 2014). Then the data is mapped using standardised resource description semantics, for example via a Resource Description Framework (RDF) store which has all the required links established between resources and artefacts, and then SPARQL, an RDF query language is employed to retrieve and manipulate data stored in database such as Cassandra and Couch database (Silva et al. 2018).