Semantic Data Acquisition by Traversing Class–Class Relationships Over Linked Open Data

2016 ◽  
pp. 136-151 ◽  
Author(s):  
Atsuko Yamaguchi ◽  
Kouji Kozaki ◽  
Kai Lenz ◽  
Yasunori Yamamoto ◽  
Hiroshi Masuya ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Open Data ◽  
Linked Open Data ◽  
Semantic Data

Iq Semantic: Semantic Web Intelligent Querying Framework

2020 ◽  
Vol 26 (3) ◽  
pp. 103-107
Author(s):  
Ilie Cristian Dorobăţ ◽  
Vlad Posea
Keyword(s):  
Natural Language Processing ◽  
Semantic Web ◽  
Natural Language ◽  
Language Processing ◽  
Open Data ◽  
Linked Open Data ◽  
Semantic Data ◽  
The Web

AbstractThe continuous expansion of the semantic web and of the linked open data cloud meant more semantic data are available for querying from endpoints all over the web. We propose extending a standard SPARQL interface with UI and Natural Language Processing features to allow easier and more intelligent querying. The paper describes some usage scenarios for easy querying and launches a discussion on the advantages of such an implementation.

Europeana no Linked Open Data: conceitos de Web Semântica na dimensão aplicada das humanidades digitais

2017 ◽  
Vol 12 (2) ◽  
Author(s):  
Caio Saraiva Coneglian ◽  
José Eduardo Santarem Segundo
Keyword(s):  
Linked Data ◽  
Open Data ◽  
Linked Open Data

O surgimento de novas tecnologias, tem introduzido meios para a divulgação e a disponibilização das informações mais eficientemente. Uma iniciativa, chamada de Europeana, vem promovendo esta adaptação dos objetos informacionais dentro da Web, e mais especificamente no Linked Data. Desta forma, o presente estudo tem como objetivo apresentar uma discussão acerca da relação entre as Humanidades Digitais e o Linked Open Data, na figura da Europeana. Para tal, utilizamos uma metodologia exploratória e que busca explorar as questões relacionadas ao modelo de dados da Europeana, EDM, por meio do SPARQL. Como resultados, compreendemos as características do EDM, pela utilização do SPARQL. Identificamos, ainda, a importância que o conceito de Humanidades Digitais possui dentro do contexto da Europeana.Palavras-chave: Web semântica. Linked open data. Humanidades digitais. Europeana. EDM.Link: https://periodicos.ufsc.br/index.php/eb/article/view/1518-2924.2017v22n48p88/33031

scholarly journals A Hybrid Approach Combining R*-Tree and k-d Trees to Improve Linked Open Data Query Performance

2021 ◽  
Vol 11 (5) ◽  
pp. 2405
Author(s):  
Yuxiang Sun ◽  
Tianyi Zhao ◽  
Seulgi Yoon ◽  
Yongju Lee
Keyword(s):  
Flash Memory ◽  
Query Language ◽  
Hybrid Approach ◽  
Open Data ◽  
Main Memory ◽  
Linked Open Data ◽  
Index Structure ◽  
Identification Algorithm ◽  
Distributed Computing Systems ◽  
Query Performance

Semantic Web has recently gained traction with the use of Linked Open Data (LOD) on the Web. Although numerous state-of-the-art methodologies, standards, and technologies are applicable to the LOD cloud, many issues persist. Because the LOD cloud is based on graph-based resource description framework (RDF) triples and the SPARQL query language, we cannot directly adopt traditional techniques employed for database management systems or distributed computing systems. This paper addresses how the LOD cloud can be efficiently organized, retrieved, and evaluated. We propose a novel hybrid approach that combines the index and live exploration approaches for improved LOD join query performance. Using a two-step index structure combining a disk-based 3D R*-tree with the extended multidimensional histogram and flash memory-based k-d trees, we can efficiently discover interlinked data distributed across multiple resources. Because this method rapidly prunes numerous false hits, the performance of join query processing is remarkably improved. We also propose a hot-cold segment identification algorithm to identify regions of high interest. The proposed method is compared with existing popular methods on real RDF datasets. Results indicate that our method outperforms the existing methods because it can quickly obtain target results by reducing unnecessary data scanning and reduce the amount of main memory required to load filtering results.

Enrichment of EHR with Linked Open Data for Risk Factors Identification

2019 ◽  
Author(s):  
Svetla Boytcheva ◽  
Galia Angelova ◽  
Zhivko Angelov ◽  
Dimitar Tcharaktchiev ◽  
Vlayko Vodenicharov
Keyword(s):  
Risk Factors ◽  
Open Data ◽  
Linked Open Data
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