The Web of Linked Data and its information.

Alberto Nogales Moyano alberto.nogales@uah.esPhD researcher, Alcalá University (Spain)

1. Introduction.2. Linked Data.3. Linked Data Cloud.4. Linked Open Vocabularies.5. Use case: information in LOD.6. Conclusions.

Agenda

1. Big Web data sources available.2. Different formats.3. Different mechanisms of access.

Introduction

Principles to publish and interlink structured dataTim Berners Lee (2007)

1. Use URIs as names for things.2. Use HTTTP URIs so that people can look up those names.3. When someone looks up a URI, provide useful RDF information.4. Include RDF statements that link to other URIs so that they can discover related things.

How to publish data

Web of Linked Data

RDF triples

Alberto Nogales Alcalá Universityworks in

Researcher

is

RDF links

Evolution

May 2007 August 2014

LOD components

• Datasets: Set of open and structured data in a particular domain.

• Vocabularies: Define the concepts and relationships used to describe and represent an area.

Linked Open Vocabularies

• Compiles the vocabularies used in the Web of Linked Data.

• Can be accessed easily. User can download them.

• Gives metrics and information about how they are interlinked (VOAF vocabulary).

VOAF vocabulary

• voaf:reliesOn• voaf:usedBy• voaf:metadataVoc• voaf:extends• voaf:specializes• voaf:generalizes• voaf:hasEquivalencesWith• voaf:hasDisjunctionsWith

Use case

• Mappings between schema.org and LOV.

• Obtain stats from LOD using mappings.

• Retrieve information from Dbpedia.

• Complete ontologies.

Schema.org• Created in 2011 by Yahoo!, Bing and Google.

• An ontology addressing multiple areas, not domain specific.

• Webmasters can mark up Websites.

• Users can obtain more precise results when searching for contents.

Workflow

Mappings classes

Example

Results

Mappings properties

Example

Results

Comparison

LogMap an ontology mapping tool

Instances in LOD

• LODStats a project aimed to give stats from LOD.

• Contains information from 9690 datasets.

• Gives statistics about classes, properties and vocabularies

• Information can be accessed through a SPARQL endpoint.

Results with LOD

Instances with classes

Instances with properties

Retrieving Dbpedia data

1. Starting from a particular webpage with metadata from schema.org (Web Data Commons).

2. We have a class and property from schema.org and a value.

3. Making a query and running it against Dbpedia, we can obtain new information.

Real example

1. Website mamangua.com with instance http://schema.org/Hotel/addressRegion with value “Rio de Janeiro”

2. If we query DBpedia with the previous value.

3. We obtain new information. For example monuments like Cristo Redentor.

4. This information could be added to the Web.

Extending an ontology

1. We find a mapping between schema.org and an ontology.

2. Mappings could refer to the same terms.

3. New properties from schema.org could extend the ontology.

Real example

1. Schema.org and Semantic Web Portal Ontology have a mapping with “City”

2. The term is referring the same thing in both.

3. Class “City” from schema.org has new properties not included in SWPO.

4. We can extend the ontology with this properties.

Conclusions

1. The Web of Linked Data is a good approach to share information.

2. Linked Open Vocabularies lets us know how the information in the datasets are stored.

3. There is an importance between terms represented in vocabularies and its value in the Web of Linked Data.

Useful links

• http://linkeddata.org/• http://lod-cloud.net/• http://lov.okfn.org/• http://schema.org/• http://stats.lod2.eu/• http://webdatacommons.org/• http://wiki.dbpedia.org/

Related papers

Exploring the Potential for Mapping Schema.org Microdata and the Web of Linked Data.

Authors: A. Nogales, M.A. Sicilia, E. Garcia-Barriocanal and S. Sanchez-Alonso.

Presented in MTSR 2013 Thessaloniki (Greece).

Thank you!!!