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Semantic Technologies as Enabler
Published in Sarika Jain, Understanding Semantics-Based Decision Support, 2021
Non-native storage techniques are not RDF data-model compliant, as opposed to their native counterparts. Usually non-native stores make a mapping onto a DBMS and then use some indexing in this field, whereas the indexing schemes used by native stores are closer to the RDF data model. The RDF data and schema can be manipulated efficiently in main memory, whereas for persistent storage it has to be first serialized to files. In spite of all this, non-native storage is more reasonable than its native counterparts for very large amounts of data. A fourth category of triple stores has recently come under investigation as possible storage managers for RDF. They are the NOSQL triple stores. For example, CumulusRDF is a NOSQL triple store using Apache Cassandra for the back end and Sesame for to provide SPARQL query facility. Another example is Jena+HBase, which uses Apache HBase for the back end and Jena to provide SPARQL query facility
Ontology-Based Information Retrieval and Matching in IoT Applications
Published in Brojo Kishore Mishra, Raghvendra Kumar, Natural Language Processing in Artificial Intelligence, 2020
M. Lawanya Shri, E. Ganga Devi, Balamurugan Balusamy, Jyotir Moy Chatterjee
“Ontology-based access to information” is implemented successfully and is able to give output. The key aspects such as OWL file, Ontology graph generation took lot of time and resource for converting their algorithm into the application. The overall conclusion one can draw using ontology one can very easily and efficiently show the entities and their relationship and that the application is able to do. Fruit fly optimization is used for Ontology matching and the results prove that it is more efficient and cost-effective. The feature or module for graph generation still needs an extra tool called Protégé that can be included as a module to show the graph from the generated OWL script. A module for running SPARQL (query for ontology) as a GUI can be added to the present application. SPARQL is RDF query language, Using SPARQL one can retrieve and manipulate data stored in RDF format. It is the key technology of semantic web. In SPARQL, queries have triple patterns, conjunction, disjunction, and optional patterns. The ontology editor used called protégé has option to run SPARQL, since protégé is developed in java so the API’s for SPARQL can be used for developing the query module.
Semantic rules for capability matchmaking in the context of manufacturing system design and reconfiguration
Published in International Journal of Computer Integrated Manufacturing, 2023
Eeva Järvenpää, Niko Siltala, Otto Hylli, Hasse Nylund, Minna Lanz
The resource functionalities and their related parameters are formalised by the Capability Model (Järvenpää et al. 2019a). This model defines simple and combined capabilities and formalises their relationships through the cm:hasInputCapability object property. As an example, a robot can have the simple capability ‘Moving’, and similarly, a gripper can have simple capabilities ‘Grasping’ and ‘Releasing’. If a robot and gripper are combined, they can have combined capabilities ‘Pick and Place’ and ‘Transporting’. The instances of cm:Capability are linked to the instances of rm:Device through the rm:hasCapability object property. The formalised relations between the simple and combined capabilities allow computer programs to form potential resource combinations having specific combined capabilities by utilizing information queried from the ontology by SPARQL. SPARQL is a semantic query language for databases, able to retrieve and manipulate data stored in Resource Description Framework (RDF) format and OWL ontologies (W3C SPARQL Working Group 2013). The following section will discuss the rules that allow automatic inference of the parameters of the combined capabilities. The rm:hasCalculatedCapability object property links this combined capability information to the specific device combination.
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).
Towards the encapsulation and decentralisation of OKD-MES services within embedded devices
Published in International Journal of Production Research, 2018
Borja Ramis Ferrer, Jose Luis Martinez Lastra
Furthermore, as described in this Section 4, ontologies will be queried in order to retrieve and update information within SPARQL12 and SPARQL Update,13 respectively. The result of SPARQL queries can be provided in different formats, such as XML or JSON. In addition, there are different forms of SPARQL queries: SELECT, ASK, CONSTRUCT and DESCRIBE. Each form will provide a different type of result. For instance, SELECT queries provide results in tabular form. This is demonstrated through Figure 6 which shows the result of executing a query in the Protégé interface. More precisely, the displayed query is used for checking relevant information about the production of specific products being manufactured in a production line. Moreover, SPARQL Update queries only return an acknowledgment notification as a result in order to indicate if the update operation has been successfully performed. Such kind of updates can perform different actions e.g. ADD, MODIFY or DELETE data graphs of the ontology. The research work (Ramis Ferrer, Iarovyi, et al. 2016) demonstrates the combination of SPARQL and SPARQL Update queries in the industrial domain.