Towards linked open data enabled ontology learning from text

2016 ◽  
Author(s):  
Meisam Booshehri ◽  
Peter Luksch
Keyword(s):  
Open Data ◽  
Linked Open Data ◽  
Ontology Learning ◽  
Learning From Text

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

scholarly journals The read–write Linked Data Web

2013 ◽  
Vol 371 (1987) ◽  
pp. 20120513 ◽  
Author(s):  
Tim Berners-Lee ◽  
Kieron O’Hara
Keyword(s):  
Future Development ◽  
Linked Data ◽  
Open Data ◽  
Linked Open Data ◽  
The Future ◽  
The Web

This paper discusses issues that will affect the future development of the Web, either increasing its power and utility, or alternatively suppressing its development. It argues for the importance of the continued development of the Linked Data Web, and describes the use of linked open data as an important component of that. Second, the paper defends the Web as a read–write medium, and goes on to consider how the read–write Linked Data Web could be achieved.

Лингвистические связанные открытые данные: состояние и перспективы

2021 ◽  
pp. 28-36
Author(s):  
А.Б. Антопольский ◽  
А.B. Antopolsky
Keyword(s):  
Open Data ◽  
Linked Open Data

Представлен проект лингвистических связанных открытых данных (Linguistic Linked Open Data - LLOD), реализуемый международной рабочей группой на платформе Семантической сети. Излагаются принципы проекта, перечисляются принятые в нем стандарты и методики, рассматриваются главные ресурсы, загруженные в облако проекта в настоящее время. Обсуждаются основные понятия и приводится классификация языковых ресурсов, представленных в проекте. История проекта описывается через изложение материалов семинара участников LLOD в течение 2012-2020 гг. Приводятся проекты по развитию LLOD и преобразованию в него различных языковых ресурсов, которые выполняют исследовательские коллективы по всему миру. Делается вывод, что этот проект является наиболее перспективным направлением по интеграции и коллаборации в области языковых технологий и ресурсов.

Sign in / Sign up

Export Citation Format

Share Document