scholarly journals Introduction to the Principles of Linked Open Data

2017 ◽  
Author(s):  
Jonathan Blaney
Keyword(s):  
Query Language ◽  
Open Data ◽  
Linked Open Data ◽  
Graph Query ◽  
Graph Query Language ◽  
Core Concepts

Introduces core concepts of Linked Open Data, including URIs, ontologies, RDF formats, and a gentle intro to the graph query language SPARQL.

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.

scholarly journals Modern Users of Libraries and the Linked Open Data Environment

2020 ◽  
Vol 69 (3) ◽  
pp. 243-260
Author(s):  
Olga A. Lavrenova ◽  
Andrey A. Vinberg
Keyword(s):  
Classification System ◽  
Query Language ◽  
Open Data ◽  
Basic Research ◽  
Linked Open Data ◽  
Knowledge Organization ◽  
Global Network ◽  
Knowledge Organization System ◽  
Description Framework ◽  
State Library

The goal of any library is to ensure high quality and general availability of information retrieval tools. The paper describes the project implemented by the Russian State Library (RSL) to present Library Bibliographic Classification as a Networked Knowledge Organization System. The project goal is to support content and provide tools for ensuring system’s interoperability with other resources of the same nature (i.e. with Linked Data Vocabularies) in the global network environment. The project was partially supported by the Russian Foundation for Basic Research (RFBR).The RSL General Classified Catalogue (GCC) was selected as the main data source for the Classification system of knowledge organization. The meaning of each classification number is expressed by complete string of wordings (captions), rather than the last level caption alone. Data converted to the Resource Description Framework (RDF) files based on the standard set of properties defined in the Simple Knowledge Organization System (SKOS) model was loaded into the semantic storage for subsequent data processing using the SPARQL query language. In order to enrich user queries for search of resources, the RSL has published its Classification System in the form of Linked Open Data (https://lod.rsl.ru) for searching in the RSL electronic catalogue. Currently, the work is underway to enable its smooth integration with other LOD vocabularies. The SKOS mapping tags are used to differentiate the types of connections between SKOS elements (concepts) existing in different concept schemes, for example, UDC, MeSH, authority data.The conceptual schemes of the leading classifications are fundamentally different from each other. Establishing correspondence between concepts is possible only on the basis of lexical and structural analysis to compute the concept similarity as a combination of attributes.The authors are looking forward to working with libraries in Russia and other countries to create a common space of Linked Open Data vocabularies.

SociaLite: An Efficient Graph Query Language Based on Datalog

2015 ◽  
Vol 27 (7) ◽  
pp. 1824-1837 ◽  
Author(s):  
Jiwon Seo ◽  
Stephen Guo ◽  
Monica S. Lam
Keyword(s):  
Query Language ◽  
Graph Query ◽  
Graph Query Language

scholarly journals Using SPARQL to access Linked Open Data

2015 ◽  
Author(s):  
Matthew Lincoln
Keyword(s):  
Query Language ◽  
Open Data ◽  
Linked Open Data ◽  
Graph Databases ◽  
Cultural Institutions

This lesson explains why many cultural institutions are adopting graph databases, and how researchers can access these data though the query language called SPARQL.

A Graph Query Language for EMF Models

2011 ◽  
pp. 167-182 ◽  
Author(s):  
Gábor Bergmann ◽  
Zoltán Ujhelyi ◽  
István Ráth ◽  
Dániel Varró
Keyword(s):  
Query Language ◽  
Graph Query ◽  
Graph Query Language

An Overview of Graph Indexing and Querying Techniques

2011 ◽  
pp. 71-88 ◽  
Author(s):  
Sherif Sakr ◽  
Ghazi Al-Naymat
Keyword(s):  
Social Networks ◽  
Query Language ◽  
Future Research ◽  
Protein Networks ◽  
Research Directions ◽  
Query Graph ◽  
Graph Indexing ◽  
Graph Query ◽  
Graph Query Language ◽  
Future Research Directions

Recently, there has been a lot of interest in the application of graphs in different domains. Graphs have been widely used for data modeling in different application domains such as: chemical compounds, protein networks, social networks and Semantic Web. Given a query graph, the task of retrieving related graphs as a result of the query from a large graph database is a key issue in any graph-based application. This has raised a crucial need for efficient graph indexing and querying techniques. In this chapter, we provide an overview of different techniques for indexing and querying graph databases. An overview of several proposals of graph query language is also given. Finally, we provide a set of guidelines for future research directions.

scholarly journals Medieval manuscripts and their migrations: Using SPARQL to investigate the research potential of an aggregated Knowledge Graph

2021 ◽  
Author(s):  
Hanno Wijsman ◽  
Toby Burrows ◽  
Laura Cleaver ◽  
Doug Emery ◽  
Eero Hyvönen ◽  
...  
Keyword(s):  
Query Language ◽  
Open Data ◽  
Literacy Skills ◽  
Linked Open Data ◽  
Knowledge Graph ◽  
Manuscript Culture ◽  
The People ◽  
Computer Scientists ◽  
Data Environment ◽  
Manuscript Description

Although the RDF query language SPARQL has a reputation for being opaque and difficult for traditional humanists to learn, it holds great potential for opening up vast amounts of Linked Open Data to researchers willing to take on its challenges. This is especially true in the field of premodern manuscripts studies as more and more datasets relating to the study of manuscript culture are made available online. This paper explores the results of a two-year long process of collaborative learning and knowledge transfer between the computer scientists and humanities researchers from the Mapping Manuscript Migrations (MMM) project to learn and apply SPARQL to the MMM dataset. The process developed into a wider investigation of the use of SPARQL to analyse the data, refine research questions, and assess the research potential of the MMM aggregated dataset and its Knowledge Graph. Through an examination of a series of six SPARQL query case studies, this paper will demonstrate how the process of learning and applying SPARQL to query the MMM dataset returned three important and unexpected results: 1) a better understanding of a complex and imperfect dataset in a Linked Open Data environment, 2) a better understanding of how manuscript description and associated data involving the people and institutions involved in the production, reception, and trade of premodern manuscripts needs to be presented to better facilitate computational research, and 3) an awareness of need to further develop data literacy skills among researchers in order to take full advantage of the wealth of unexplored data now available to them in the Semantic Web.

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