Towards a Semantic Linked Data Retrieval Model

2014 ◽  
pp. 300-309 ◽  
Author(s):  
Van Bich Nguyen ◽  
Dang Tuan Nguyen
Keyword(s):  
Linked Data ◽  
Data Retrieval ◽  
Retrieval Model

A taxonomically distributed data retrieval model

1998 ◽  
Vol 110 (1-3) ◽  
pp. 198-205
Author(s):  
M. Marquina ◽  
R. Ramos Pollán ◽  
A. Taddei
Keyword(s):  
Data Retrieval ◽  
Distributed Data ◽  
Retrieval Model

Hierarchical Data Retrieval Model For Big Data

2013 ◽  
Vol 12 (24) ◽  
pp. 8176-8180
Author(s):  
Sijin Chen ◽  
Shao Bo Wu ◽  
Xue Ying Gao
Keyword(s):  
Big Data ◽  
Data Retrieval ◽  
Hierarchical Data ◽  
Retrieval Model

scholarly journals An Agglomerative-adapted Partition Approach for Large-scale Graphs

2019 ◽  
Vol 4 (1) ◽  
pp. 3
Author(s):  
Chen Tao ◽  
Rongrong Shan ◽  
Hui Li ◽  
Dongsheng Wang ◽  
Wei Liu
Keyword(s):  
Machine Learning ◽  
Linked Data ◽  
Large Scale ◽  
Distributed Storage ◽  
Data Retrieval ◽  
Knowledge Bases ◽  
Machine Learning Algorithms ◽  
Merging Process ◽  
Partition Triples ◽  
Single Graph

In recent years, an increasing number of knowledge bases have been built using linked data, thus datasets have grown substantially. It is neither reasonable to store a large amount of triple data in a single graph, nor appropriate to store RDF in named graphs by class URIs, because many joins can cause performance problems between graphs. This paper presents an agglomerative-adapted approach for large-scale graphs, which is also a bottom-up merging process. The proposed algorithm can partition triples data in three levels: blank nodes, associated nodes, and inference nodes. Regarding blank nodes and classes/nodes involved in reasoning rules, it is better to store with an optimal neighbor node in the same partition instead of splitting into separate partitions. The process of merging associated nodes needs to start with the node in the smallest cost and then repeat it until the final number of partitions is met. Finally, the feasibility and rationality of the merging algorithm are analyzed in detail through bibliographic cases. In summary, the partitioning methods proposed in this paper can be applied in distributed storage, data retrieval, data export, and semantic reasoning of large-scale triples graphs. In the future, we will research the automation setting of the number of partitions with machine learning algorithms.

A Research on Linked Data-Based Chinese Semantic Retrieval Model

2014 ◽  
pp. 561-567
Author(s):  
Qingling Chang ◽  
Yuanchun Zhou ◽  
Shiting Xu ◽  
Jianhui Li ◽  
Baoping Yan
Keyword(s):  
Linked Data ◽  
Semantic Retrieval ◽  
Retrieval Model

Web Service Discovery Using Bio-Inspired Holistic Matching Based Linked Data Clustering Model for RDF Data

2019 ◽  
Vol 12 ◽  
Author(s):  
Manish Kumar Mehrotra ◽  
Suvendu Kanungo
Keyword(s):  
Semantic Web ◽  
Web Service ◽  
Data Clustering ◽  
Linked Data ◽  
Service Discovery ◽  
Data Retrieval ◽  
Data Representation ◽  
Web Service Discovery ◽  
Clustering Model ◽  
Rdf Data

: Resource description framework (RDF) is the de-facto standard language model for semantic data representation on Semantic Web. Designing an efficient management of RDF data with huge volume and efficient querying techniques are the primary research areas in semantic web. So far, several RDF management methods have been offered with data storage designs and query processing algorithms for data retrieval. We propose a Bio-inspired Holistic Matching based Linked Data Clustering (BHM-LDC) which works based on RDF data storing, clustering the linked data and web service discovery. Initially the BHM-LDC algorithm store the RDF dataset as graph based linked data. Then, an Integrated Holistic Entity Matching based Distributed Genetic Algorithm (IHEM-DGA) is proposed to cluster the linked data. Finally, modified sub-graph matching based Web Service Discovery Algorithm uses the clustered triples to find the best web services. The performance of the proposed web service discovery approach is established by business RDF dataset.

A Data Management Tool for Reporting Productivity

1983 ◽  
Vol 50 (3) ◽  
pp. 73-76 ◽  
Author(s):  
Maureen Wright
Keyword(s):  
Occupational Therapy ◽  
Diagnostic Category ◽  
Data Retrieval ◽  
Management Tool ◽  
Retrieval Model ◽  
Paediatric Hospital ◽  
Diagnostic Categories ◽  
Input And Output ◽  
Therapy Services ◽  
Therapy Department

Hospital-based occupational therapy departments in the 1980's must be able to report productivity levels in terms of cost-effectiveness. Only the most efficient programmes are likely to succeed in this era of budgetary constraints. A data retrieval model, operating in a hospital-based occupational therapy department is described. The model reports departmental productivity and data for making cost estimates related to direct and indirect patient care by diagnostic category. In addition, the system shows individual therapists' input and output. This data system has made possible monthly management statements that provide an overview of productivity and indicate the complexity of occupational therapy services for specific diagnostic categories within an acute-care paediatric hospital.

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