scholarly journals Semantics-Preserving RDB2RDF Data Transformation Using Hierarchical Direct Mapping

2020 ◽  
Vol 10 (20) ◽  
pp. 7070
Author(s):  
Hee-Gook Jun ◽  
Dong-Hyuk Im
Keyword(s):  
Relational Databases ◽  
Semantic Information ◽  
Data Transformation ◽  
Transformation Method ◽  
Information Loss ◽  
Mapping Method ◽  
Data Consistency ◽  
Data Generation ◽  
Direct Mapping ◽  
Rdf Data

Direct mapping is an automatic transformation method used to generate resource description framework (RDF) data from relational data. In the field of direct mapping, semantics preservation is critical to ensure that the mapping method outputs RDF data without information loss or incorrect semantic data generation. However, existing direct-mapping methods have problems that prevent semantics preservation in specific cases. For this reason, a mapping method is developed to perform a semantics-preserving transformation of relational databases (RDB) into RDF data without semantic information loss and to reduce the volume of incorrect RDF data. This research reviews cases that do not generate semantics-preserving results, and the corresponding problems into categories are arranged. This paper defines lemmas that represent the features of RDF data transformation to resolve those problems. Based on the lemmas, this work develops a hierarchical direct-mapping method to strictly abide by the definition of semantics preservation and to prevent semantic information loss, reducing the volume of incorrect RDF data generated. Experiments demonstrate the capability of the proposed method to perform semantics-preserving RDB2RDF data transformation, generating semantically accurate results. This work impacts future studies, which should involve the development of synchronization methods to achieve RDF data consistency when original RDB data are modified.

Semantics-preserving optimisation of mapping multi-column key constraints for RDB to RDF transformation

2020 ◽  
pp. 016555152092080 ◽  
Author(s):  
Hee-Gook Jun ◽  
Dong-Hyuk Im ◽  
Hyoung-Joo Kim
Keyword(s):  
Extraction Method ◽  
Semantic Information ◽  
Transformation Method ◽  
Information Loss ◽  
Data Generation ◽  
Semantic Data ◽  
Rdf Data ◽  
Description Framework ◽  
Transformation Methods ◽  
Resource Description

The relational database (RDB) to resource description framework (RDF) transformation is a major semantic information extraction method because most web data are managed by RDBs. Existing automatic RDB-to-RDF transformation methods generate RDF data without losing the semantics of original relational data. However, two major problems have been observed during the mapping of multi-column key constraints: repetitive data generation and semantic information loss. In this article, we propose an improved RDB-to-RDF transformation method that ensures mapping without the aforementioned problems. Optimised rules are defined to generate an accurate semantic data structure for a multi-column key constraint and to reduce repetitive constraint data. Experimental results show that the proposed method achieves better accuracy in transforming multi-column key constraints and generates compact semantic results without repetitive data.

Modeling and Mapping Implementation of Substation Knowledge Ontology Based on Protégé

2012 ◽  
Vol 198-199 ◽  
pp. 786-789
Author(s):  
Tie Feng Zhang ◽  
Shu Juan Han ◽  
Jian Wei Gu
Keyword(s):  
Information Sharing ◽  
Semantic Information ◽  
Information Model ◽  
Mapping Method ◽  
Basic Knowledge ◽  
Web Ontology Language ◽  
Ontology Language ◽  
Master Station ◽  
Knowledge Ontology

Based on the basic knowledge of ontology and protégé, and the deficiency of semantic expression in the IEC61850 and IEC61970 Standard, this paper puts forward a mapping method from SCL to CIM, adopting Web Ontology Language OWL to build the semantic information model of SCL and CIM of substation knowledge ontology. In substation model, this mapping method could solve the problem of information sharing and interoperation between digitized substation and dispatch master station, and lay a foundation for further research on fusion of the two standards.

scholarly journals Data validation and schema interoperability

2020 ◽  
Author(s):  
Leyla Jael Garcia Castro ◽  
Jerven Bolleman ◽  
michel dumontier ◽  
Simon Jupp ◽  
Jose Emilio Labra-Gayo ◽  
...  
Keyword(s):  
Background Knowledge ◽  
The Other ◽  
Data Consistency ◽  
Data Validation ◽  
Constraint Language ◽  
Rdf Data

Validating RDF data becomes necessary in order to ensure data compliance against the conceptualization model it follows, e.g., schema or ontology behind the data, and improve data consistency and completeness. There are different approaches to validate RDF data, for instance, JSON schema, particularly for data in JSONLD format, as well as Shape Expression and Shapes Constraint Language, which can be used with other serialization as well, e.g., RDF/XML or Turtle. Currently, no validation approach is prevalent regarding others, selection commonly depends on data characteristics, background knowledge and personal preferences . In some cases, the approaches are interchangeable; however, that is not always the case, making it necessary to identify a subset among them that can be seamlessly translated from one to another. During the NBDC/DBCLS 2019 BioHackathon, we worked on a variety of topics related to RDF data validation, including (i) development of ShEx shapes for a number of datasets, (ii) development of a tool to semi-automatically create ShEx shapes, (iii) improvements to the RDFShape tool, and (iv) enabling validation schema conversion from one format to the other. Here we report on our BioHackathon achievements.

scholarly journals Vehicle Location Prediction Based on Spatiotemporal Feature Transformation and Hybrid LSTM Neural Network

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 84
Author(s):  
Yuelei Xiao ◽  
Qing Nian
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Transformation Method ◽  
Information Loss ◽  
Location Prediction ◽  
The Neural Network ◽  
Vehicle Location ◽  
Vehicle Trajectory ◽  
Taxi Trajectory

Location prediction has attracted much attention due to its important role in many location-based services. The existing location prediction methods have large trajectory information loss and low prediction accuracy. Hence, they are unsuitable for vehicle location prediction of the intelligent transportation system, which needs small trajectory information loss and high prediction accuracy. To solve the problem, a vehicle location prediction algorithm was proposed in this paper, which is based on a spatiotemporal feature transformation method and a hybrid long short-term memory (LSTM) neural network model. In the algorithm, the transformation method is used to convert a vehicle trajectory into an appropriate input of the neural network model, and then the vehicle location at the next time is predicted by the neural network model. The experimental results show that the trajectory information of an original taxi trajectory is basically reserved by its shadowed taxi trajectory, and the trajectory points of the predicted taxi trajectory are close to those of the shadowed taxi trajectory. It proves that our proposed algorithm effectively reduces the information loss of vehicle trajectory and improves the accuracy of vehicle location prediction. Furthermore, the experimental results also show that the algorithm has a higher distance percentage and a shorter average distance than the other predication models. Therefore, our proposed algorithm is better than the other prediction models in the accuracy of vehicle location predication.

scholarly journals An Improved Deep Residual Network-Based Semantic Simultaneous Localization and Mapping Method for Monocular Vision Robot

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Jianjun Ni ◽  
Tao Gong ◽  
Yafei Gu ◽  
Jinxiu Zhu ◽  
Xinnan Fan
Keyword(s):  
Semantic Information ◽  
Hot Spot ◽  
Simultaneous Localization And Mapping ◽  
Mapping Method ◽  
Monocular Vision ◽  
Visual Slam ◽  
Camera Tracking ◽  
Residual Network ◽  
Semantic Map ◽  
Localization And Mapping

The robot simultaneous localization and mapping (SLAM) is a very important and useful technology in the robotic field. However, the environmental map constructed by the traditional visual SLAM method contains little semantic information, which cannot satisfy the needs of complex applications. The semantic map can deal with this problem efficiently, which has become a research hot spot. This paper proposed an improved deep residual network- (ResNet-) based semantic SLAM method for monocular vision robots. In the proposed approach, an improved image matching algorithm based on feature points is presented, to enhance the anti-interference ability of the algorithm. Then, the robust feature point extraction method is adopted in the front-end module of the SLAM system, which can effectively reduce the probability of camera tracking loss. In addition, the improved key frame insertion method is introduced in the visual SLAM system to enhance the stability of the system during the turning and moving of the robot. Furthermore, an improved ResNet model is proposed to extract the semantic information of the environment to complete the construction of the semantic map of the environment. Finally, various experiments are conducted and the results show that the proposed method is effective.

Nested Optional Join for Efficient Evaluation of SPARQL Nested Optional Graph Patterns

2010 ◽  
pp. 281-308
Author(s):  
Artem Chebotko ◽  
Shiyong Lu
Keyword(s):  
Semantic Web ◽  
Relational Databases ◽  
Real Life ◽  
Query Languages ◽  
Efficient Algorithms ◽  
Relational Algebra ◽  
Nested Loops ◽  
Web Data Management ◽  
Rdf Data ◽  
Rdf Query Languages

Relational technology has shown to be very useful for scalable Semantic Web data management. Numerous researchers have proposed to use RDBMSs to store and query voluminous RDF data using SQL and RDF query languages. This chapter studies how RDF queries with the so called well-designed graph patterns and nested optional patterns can be efficiently evaluated in an RDBMS. The authors propose to extend relational algebra with a novel relational operator, nested optional join (NOJ), that is more efficient than left outer join in processing nested optional patterns of well-designed graph patterns. They design three efficient algorithms to implement the new operator in relational databases: (1) nested-loops NOJ algorithm, NL-NOJ, (2) sort-merge NOJ algorithm, SM-NOJ, and (3) simple hash NOJ algorithm, SH-NOJ. Using a real life RDF dataset, the authors demonstrate the efficiency of their algorithms by comparing them with the corresponding left outer join implementations and explore the effect of join selectivity on the performance of these algorithms.

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