Manipulating RDF data – Linked Data for Professional Education

Tarql: SPARQL for Tables

Fri, 18 Aug 2017 08:22:56 +0000

Tarql is a command-line tool for converting CSV files to RDF using SPARQL 1.1 syntax. In short, a CSV file’s contents are input into a SPARQL query as a table of bindings. This allows manipulation of CSV data using the full power of SPARQL 1.1 syntax, and in particular the generation of RDF using CONSTRUCT queries. Includes design patterns and examples. Discusses how to deal with variations in header rows, delimiters, quotes and character encoding encountered in CSV/TSV files.

URL: http://tarql.github.io/
Keywords: CSV (Comma Separated Values), SPARQL CONSTRUCT, SPARQL OFFSET, Apache Jena, Java (programming language)
Author: Cyganiak, Richard
Date created: 2017-06-22 04:00:00.000
Language: http://id.loc.gov/vocabulary/iso639-2/eng
Time required: P45M

Using the Convert CSV to RDF ingest tool

Fri, 18 Aug 2017 08:22:56 +0000

This guide will walk through the use of the Convert CSV to RDF tool, a semi-automated method of converting comma separated or tab separated text files into RDF that can be displayed in VIVO. These files should include one row of data per record (e.g., a person or publication) and represent the fields or properties associated with each record in separate columns within the row, much as the values appear in a spreadsheet. The most common pattern of loading CSV files involves one CSV file per type of data to be loaded. Note, the current ingest tools involve working through a number of steps from original source data files to the appearance of new data in VIVO. The process requires some understanding of semantic web data modeling and some training.

URL: https://wiki.duraspace.org/display/VIVODOC19x/Using+the+Convert+CSV+to+RDF+ingest+tool
Keywords: CSV (Comma Separated Values), VIVO Ontology for Researcher Discovery, SPARQL
Author: Gross, Benjamin
Publisher: Duraspace
Date created: 2017-03-16 04:00:00.000
Language: http://id.loc.gov/vocabulary/iso639-2/eng
Time required: P30M
Educational use: instruction
Educational audience: teacher-educationSpecialist
Interactivity type: mixed

Synaptica Tech Talks: Data Modeling with Graphs

Wed, 08 Mar 2017 06:53:18 +0000

This tutorial explores data modelling by comparing and contrasting traditional relational databases with property graph databases and RDF Linked Data graph databases. Also discusses the benefits of reusing ontologies and vocabularies from well-known and authoritative sources.

URL: https://www.youtube.com/watch?v=r4IGWNovzes
Keywords: Graph model, Property graph, Name authority file, Relational model
Author: Loh, Yu Gene
Publisher: Synaptica
Date created: 2016-11-01 04:00:00.000
Language: http://id.loc.gov/vocabulary/iso639-2/eng
Time required: P15M
Educational use: instruction
Educational audience: student
Interactivity type: expositive

How Is SPARQL Different From SQL?

Mon, 09 Jan 2017 06:45:30 +0000

This video explains how the SPARQL query language differs from SQL. Highlighted is the ability of SPARQL to handle complex joins that would be too time-consuming using SQL. Also, SPARQL joins do not require a high degree of a priori knowledge of the data scheme.

URL: https://www.youtube.com/watch?v=HtI72FLHSCQ
Keywords: SPARQL, SQL
Author: Vesse, Rob
Publisher: YarcData
Date created: 2012-10-03 04:00:00.000
Language: http://id.loc.gov/vocabulary/iso639-2/eng
Time required: P10M
Educational use: instruction
Educational audience: student
Interactivity type: expositive

Implementing Graph Transformation Languages using RDF Storage and SPARQL Queries

Thu, 24 Nov 2016 05:51:18 +0000

RDF has so far not been widely used in the context of graph rewriting tools. In this paper, the authors take AToMPM, a research-oriented meta-modelling and graph rewriting tool and extend it by adding RDF-based graph transformation functionality. Addressed is the graph matching problem, generating custom SPARQL queries for each rule pattern, and using them to efficiently perform matches on a source RDF graph.

URL: https://www.cs.mcgill.ca/media/tech_reports/12_Implementing_Graph_Transformation_Languages_using_RDF_Storage_and_SPARQL_Queries.pdf
Keywords: CRUD, Graph rewriting, igraph, RDFLib, Graph transformation
Author: Verbrugge, Clark
Date created: 2012-08-31 04:00:00.000
Language: http://id.loc.gov/vocabulary/iso639-2/eng
Time required: P30M
Educational use: instruction
Educational audience: teacher-educationSpecialist
Interactivity type: expositive

RDFProv: A Relational RDF Store for Querying and Managing Scientific Workflow Provenance

Wed, 23 Nov 2016 05:50:23 +0000

Provenance metadata has become increasingly important to support scientific discovery, reproducibility, result interpretation, and problem diagnosis in scientific workflow environments. The provenance management problem concerns the efficiency and effectiveness of the modeling, recording, representation, integration, storage, and querying of provenance metadata. The authors present an approach to provenance management which they claim "seamlessly integrates the interoperability, extensibility, and inference advantages of Semantic Web technologies with the storage and querying power of an RDBMS to meet the emerging requirements of scientific workflow provenance management". In this paper, they elaborate on the design of a relational RDF store, called RDFProv, which is optimized for scientific workflow provenance querying and management.

URL: http://www.sciencedirect.com/science/article/pii/S0169023X10000455
Keywords: Provenance, RDBMS, Web Ontology Language (OWL), Triple store
Author: Fotouhi, Farshad
Publisher: Elsevier
Date created: 2010-08-08 04:00:00.000
Language: http://id.loc.gov/vocabulary/iso639-2/eng
Time required: P1H
Educational use: professionalDevelopment
Educational audience: professional
Interactivity type: expositive

Introduction and Application Scenarios

Sun, 23 Oct 2016 05:39:27 +0000

This module introduces the main principles of Linked Data, the underlying technologies and background standards. It provides basic knowledge for how data can be published over the Web, how it can be queried, and what are the possible use cases and benefits. The module also includes some multiple choice questions in the form of a quiz, screencasts of popular tools, and embedded videos.

URL: http://www.euclid-project.eu/modules/chapter1.html
Keywords: Linked Data, Linked Data Principles, Semantic Web, Web of Data, XML, RDF, HTTP URIs, Triple, Graph, SPARQL, Mashup
Publisher: EUCLID Project
Language: http://id.loc.gov/vocabulary/iso639-2/eng
Time required: P2H
Educational use: instruction
Educational audience: professional
Interactivity type: mixed

Introduction and Application Scenarios Quiz

Sun, 23 Oct 2016 05:39:26 +0000

Ten multiple choice questions that are intended to test one's knowledge after reading and viewing the materials in Module 1: Introduction and Application Scenarios.

URL: http://www.euclid-project.eu/content/chapter-1-quiz.html
Keywords: Mashup, SPARQL, Graph, Triple, HTTP URIs, RDF, XML, Web of Data, Semantic Web, Linked Data Principles, Linked Data
Publisher: EUCLID Project
Language: http://id.loc.gov/vocabulary/iso639-2/eng
Time required: P10M
Educational use: assessment
Educational audience: student
Interactivity type: active

Scaling Up

Sun, 23 Oct 2016 05:39:26 +0000

This module addresses the main issues of Linked Data and scalability. In particular, it provides details on approaches and technologies for clustering, distributing, sharing, and caching data. Furthermore, it addresses the means for publishing data and the relationship between Big Data and Linked Data, exploring how some of the solutions can be transferred in the context of Linked Data.

URL: http://www.euclid-project.eu/modules/chapter6.html
Keywords: MapReduce, Linked Data, Big Data, Scalability, NoSQL, Hadoop
Publisher: EUCLID Project
Language: http://id.loc.gov/vocabulary/iso639-2/eng
Time required: P3H
Educational use: professionalDevelopment
Educational audience: student
Interactivity type: mixed

Scaling Up Quiz

Sun, 23 Oct 2016 05:39:26 +0000

Ten multiple choice questions that are intended to test one's knowledge after reading and viewing the materials in Module 6: Scaling Up (http://www.euclid-project.eu/modules/course6).

URL: http://www.euclid-project.eu/content/chapter-6-quiz.html
Keywords: Hadoop, NoSQL, Scalability, Big Data, MapReduce, Linked Data
Publisher: EUCLID Project
Language: http://id.loc.gov/vocabulary/iso639-2/eng
Time required: P10M
Educational use: instruction
Educational audience: student
Interactivity type: mixed