instruction – Linked Data for Professional Education

0 to 60 on SPARQL Queries in 50 Minutes

Sun, 06 May 2018 02:22:48 +0000

This webinar provides an introduction to SPARQL, a query language for RDF. Users will gain hands on experience crafting queries, starting simply, but evolving in complexity. These queries will focus on coinage data in the SPARQL endpoint hosted by http://nomisma.org with numismatic concepts defined in a SKOS-based thesaurus and physical specimens from three major museum collections (American Numismatic Society, British Museum, and Münzkabinett of the Staatliche Museen zu Berlin) linked to these concepts. Results generated from these queries in the form of CSV may be imported directly into Google Fusion Tables for immediate visualization in the form of charts and maps. Links to all resources necessary to practice the queries covered are available online and referenced from the webinar presentation.

The webinar uses the following SPARQL constructs: SELECT, WHERE, LIMIT, ORDER BY (ASC), FILTER, DISTINCT, regex(), count(), lang(), langMatches().

Access to all SPARQL queries used in the webinar are available at http://numishare.blogspot.com/2015_05_01_archive.

URL: https://www.asist.org/events/webinars/dcmi-webinar-from-0-to-60-on-sparql-queries-in-50-minutes/
Keywords: visualization (map geolocations), Google Fusion Tables, visualization (statistics), geographic queries, UNION, OPTIONAL, sorting, filtering, SPARQL syntax
Author: Ethan Gruber
Publisher: DCMI
Date created: 2014-01-01 07:00:00.000
Language: http://id.loc.gov/vocabulary/iso639-2/eng
Time required: P1H00M
Educational use: instruction
Educational audience: student
Interactivity type: mixed

Validating RDF Data

Fri, 15 Sep 2017 09:45:50 +0000

This book describes two languages for implementing constraints on RDF data, describing the main features of both Shape Expressions (ShEx) and Shapes Constraints Language (SHACL) from a user perspective, and also offering a comparison of the technologies. The authors develop a small number of examples that typify validation requirements and demonstrate how they can be met with ShEx and SHACL. The book is not intended to be a formal specification of the languages, for which the interested reader can consult the corresponding reference documents, but rather, it is meant to serve as an introduction to the technologies with some background about the rationale of their design and some points of comparison.

URL: http://www.morganclaypoolpublishers.com/catalog_Orig/product_info.php?products_id=1091
Keywords: Shape Expressions (ShEx), Shapes Constraints Language (SHACL), Data validation
Author: Kontokostas, Dimitris
Publisher: Morgan & Claypool Publishers
Date created: 2017-08-01 04:00:00.000
Language: http://id.loc.gov/vocabulary/iso639-2/eng
Time required: P8H
Educational use: instruction
Educational audience: teacher-educationSpecialist
Interactivity type: expositive

Open Refine 101

Fri, 25 Aug 2017 08:34:44 +0000

This free online course explains that while data cleaning, preparation and enrichment take up an enormous amount of time, and is nevertheless a crucial stage in the data science methodology. However, data transformation tools haven’t fully caught up with the popularity of data analysis. Learn why domain experts need powerful yet easy-to-use interfaces to explore new data sets, normalize them and process them via innovative services often available via an API only. The instructor demonstrates the strengths of OpenRefine, which are that it offers a self-service agile and iterative interface for data discovery and preparation, as well as an easy-to-learn scripting language.

URL: https://cognitiveclass.ai/courses/introduction-to-openrefine/
Keywords: Google Refine, General Refine Expression Language (GREL), Data enrichment, Data cleansing
Author: Magdinier, Martin
Publisher: Cognitive Class
Language: http://id.loc.gov/vocabulary/iso639-2/eng
Time required: P7H
Educational use: instruction
Educational audience: teacher-educationSpecialist
Interactivity type: mixed

Swagger, the API Economy, REST, Linked Data, and a Semantic Web

Sat, 19 Aug 2017 08:28:00 +0000

In this blog post, the author presents the following situation analysis: The network commonly referred to as the World Wide Web (“Web”) is built upon the powerful duality delivered by a HTTP URI (“Hyperlink”). Hypermedia (Webby Content) is Document Content that includes hyperlinks, which may or may not include explicit controls for Hyperlink lookup (or dereference). Notations for creating RDF document content e.g., (RDF-Turtle or JSON-LD) don’t include explicit Hyperlink-lookup controls. This has led to a false dichotomy has arisen across the communities of so-called “Web Developers” (who prefer explicit Hyperlink-lookup controls) and don’t necessarily see the need for HTTP URI opacity. The author argues that "the notion of a Semantic Web of Linked Data and REST-ful interactions using APIs aren’t in anyway mutually exclusive". He presents an example (with screenshots) showing how a single instance of OpenLink Smart Data Bot (OSDB), combined with the use of several other tools, provides a REST-ful interaction point for engaging a variety of Actions extracted from a variety of APIs documented using Swagger and/or RDF documents.

URL: https://medium.com/openlink-software-blog/swagger-the-api-economy-rest-linked-data-and-a-semantic-web-9d6839dae65a
Keywords: REST (Representational State Transfer), Web APIs, OpenLink Smart Data Bot (OSDB), DBpedia, Virtuoso, CURL
Author: Idehen, Kingsley Uyi
Publisher: OpenLink Software
Date created: 2017-06-24 04:00:00.000
Language: http://id.loc.gov/vocabulary/iso639-2/eng
Time required: P45M
Educational use: instruction
Educational audience: student
Interactivity type: mixed

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

Don’t Use a Hammer to Screw in a Nail: Alternatives to REGEX in SPARQL

Sun, 13 Aug 2017 08:18:11 +0000

This brief blog post explains that regular expressions are expensive to evaluate regardless of what language you are using them in. The author suggests that if you can avoid using a regular expression in favor of a simpler string computation, then you can likely get much better performance out of your SPARQL engine. Alternative strategies include using CONTAINS, LCASE, UCASE, STRSTARTS, and STRENDS. If more complex string operations are required, full-text extensions to the SPARQL engine may be an option.

URL: http://www.cray.com/blog/dont-use-hammer-screw-nail-alternatives-regex-sparql/
Keywords: SPARQL, CONTAINS, String operations, REGEX, Jena Text
Author: Vesse, Rob
Publisher: Cray
Date created: 2014-06-03 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

Equality and Inequality in SPARQL

Sun, 13 Aug 2017 08:18:11 +0000

This brief blog post discusses issues surrounding expression semantics in SPARQL. Practices that people are used to from other languages (like the use of "=" and "!="), can often be confusing to new – and even not so new- SPARQL developers. The author suggests that an awareness of type errors will allow the user to understand what is occurring. Example queries show how type errors are treated when using FILTER. An alternative method involving project expressions or BIND is proposed.

URL: http://www.cray.com/blog/equality-inequality-sparql/
Keywords: SPARQL, BIND, FILTER, Type errors, Project expressions
Author: Vesse, Rob
Publisher: Cray
Date created: 2013-04-23 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

Converting Relational Data Into RDF Format

Tue, 08 Aug 2017 08:07:57 +0000

This tutorial shows two different methods of converting or materializing relational data into RDF graph data format: the D2RQ based method and the SQL based method. The D2RQ based method provides customizable mapping files that allow users to specify the generation of URIs, enabling the reuse of URIs across different columns, tables, schemas, or even databases. URI reuse here means using the same URI to represent the same resource. The SQL-based approach, as provided in this tutorial, does not handle escapes, character encoding, new lines, tabs, and other special characters.

URL: http://www.oracle.com/webfolder/technetwork/tutorials/obe/db/11g/r1/prod/datamgmt/relational_into_rdf/relational_data_into_rdf_format_otn.htm
Keywords: D2RQ Mapping Language, SQL (Structured Query Language), N-Triples, N3
Publisher: Oracle
Language: http://id.loc.gov/vocabulary/iso639-2/eng
Time required: P1H
Educational use: instruction
Educational audience: professional
Interactivity type: active

SPARQL Package for R: Movie Star Graph Visualization Tutorial

Sun, 06 Aug 2017 07:58:04 +0000

The SPARQL Package allows you to directly import results of SPARQL SELECT queries into the statistical environment of R as a data frame, allowing one to directly perform statistical analysis on datasets on the web. This tutorial shows how to do a visual analysis with the Gephi graph visualization tool, and basic social network analysis with R of the data in the DBpedia repository about Hollywood movies. It makes use of the SPARQL Package for R and the igraph library.

URL: http://linkedscience.org/tools/sparql-package-for-r/sparql-package-for-r-gephi-movie-star-graph-visualization-tutorial/
Keywords: R (programming language), Linked Open Data (LOD), Statistical analysis, Data visualization, Gephi
Author: van Hage, Willem Robert
Publisher: COMBINE project
Language: http://id.loc.gov/vocabulary/iso639-2/eng
Time required: P90M
Educational use: instruction
Educational audience: student
Interactivity type: mixed

Using the SPARQL Package in R to Handle Spatial Linked Data: Part II

Sun, 06 Aug 2017 07:58:04 +0000

Continuing where Part I left off, this tutorial shows how to access Spatial and Temporal Data about the complex environmental system of the Brazilian Amazon Rainforest, including deforestation statistics, and a variety of socio-economic variables. It shows how store this data in a meaningful way and how to plot and handle it within R.

URL: http://linkedscience.org/tools/sparql-package-for-r/tutorial-part-ii-calculating-deforestation-per-state/
Keywords: R (programming language), Linked Open Data (LOD), Statistical analysis, Data visualization
Author: Gräler, Benedikt
Publisher: COMBINE project
Language: http://id.loc.gov/vocabulary/iso639-2/eng
Time required: P30M
Educational use: instruction
Educational audience: student
Interactivity type: mixed