Tag Archive for 'linkeddata'

New UMBEL 1.50 Ships With 20 Linked Ontologies

I am proud to announce the immediate release of UMBEL version 1.50. This is a major effort that took a year to release.

What is UMBEL?

Let’s start by explaining what is UMBEL for the ones that never encountered this project before. UMBEL stands for “Upper Mapping and Binding Exchange Layer“. It is a conceptual structure that is designed to help content interoperate between systems.

UMBEL is a coherent general structure of 34 000 reference concepts which provides a scaffolding to link and interoperate other datasets and domain vocabularies. The conceptual structure is organized in a structure of 31 mostly disjoint SuperType.

UMBEL is written in OWL 2 and SKOS.

What are UMBEL’s Objectives?

UMBEL’s main goals are:

  • To create a scaffolding for defining knowledge graphs
  • To create a rich semantic to identify and help disambiguating entities
  • To help expend queries to semantic search engines
  • To help inter-linking ontologies to create a coherent ontological environment
  • To help structure and federate information silos

What is new in UMBEL version 1.50?

Many things changed in UMBEL 1.50: additional of new concepts, multiple structural fixes and improvements, etc. However there are 3 major changes that occurred in this release:

  1. Complete update and addition of linkages between UMBEL reference concepts and related classes existing in external ontologies
  2. Removal of all the named individuals from UMBEL. UMBEL is now only composed of classes reference concepts
  3. Reshaping of the SuperType upper structure by adding new ones and removing some of them

For the complete list of UMBEL changes, I would strongly suggest you to read Mike’s blog post about this UMBEL release.

UMBEL Mapping to External Ontologies

One interesting aspect of the UMBEL structure is to use the coherent structure to federate information silos. We can do that by linking ontologies and vocabularies, used to describe entities indexed in these silos, directly into UMBEL.

But what does that mean? Let’s take a look at a portion of the UMBEL structure related to actors, authors and their relations to humans:

actors-authors-humans

Now let’s assumes that we have two data sources:

  1. DBpedia from which we want to use its Journalist entities, and
  2. Musicbrainz from which we want to use its solo musical artist entities

The journalist entities of the DBpedia data source belong to the dbpedia:Journalist class of the DBpedia ontology. The Musicbrainz solo musical artists belong to the mo:SoloMusicArtist class of the Music Ontology. If you check each of these ontology, you won’t find any connections between these two classes. They appears to be living in two different [conceptual] worlds.

However, what happens if these two classes get connected to some UMBEL reference concepts? Let’s take a look:

dbpedia-mo-connections

What we did here is to connect the two classes to the UMBEL reference structure using the equivalent to property. What we are stating with these assertions is that these two classes are equivalent to these other classes in UMBEL. This seems harmless, but when we start thinking about that, something special is happening.

The special thing that is happening is that we can now query the different datasets (Musicbrains and DBpedia) on new ground. We can now query them such that if I request to get the list of all humans, then I can and I will get all soloist and all journalist. If the data store to get all authors, then I would get all DBpedia journalists and maybe authors of other datasets that may be linked to the UMBEL reference structure.

This is an illustration of how UMBEL can be used to federate information silos.

The good news is that the UMBEL reference structure is already linked to 20 ontologies used by different organizations to define their data sources:

  1. DBPedia Ontology – Links between the DBpedia Ontology classes and the UMBEL Reference Concepts. Half of them comes from the linkage between Proton and UMBEL, and half the others come from hand mapping
  2. Geonames – Geonames
  3. Opencyc – OpenCyc Ontology
  4. Schema.org – Schema.org ontology defines entities known by Google and other search engines
  5. Wikipedia – Links between the Wikipedia pages and the UMBEL Reference Concepts
  6. DOAP – DOAP(Description of a Project) is a vocabulary for project description.
  7. ORG – The ORG (Core Organization) Ontology is a vocabulary for describing organizational structures for a broad variety of types of organization
  8. OO – OO(Open Organizations) is a vocabulary providing supplementary terms for organizations that wish to publish open data about themselves
  9. TRANSIT – TRANSIT(Transit) is a vocabulary for describing transit systems and routes
  10. TIME – The TIME(Time Ontology) defines temporal entities
  11. BIBO – BIBO (Bibliographic Ontology)
  12. CC – CC (CreativeCommons Ontology)
  13. Event – Event Ontology
  14. FOAF – FOAF (Friend Of A Friend Ontology) used to describe people and organizations
  15. GEO – WSG84 Geographic Ontology
  16. MO – MO (Music Ontology)
  17. PO – PO (Programmes Ontology)
  18. RSS – RSS (Really Simple Syndication Ontology)
  19. SIOC – SIOC (Semantically-Interlinked Online Communities Ontology)
  20. FRBR – FRBR (Functional Requirements for Bibliographic Records)

According to Linked Open Vocabularies (LOV) service, the UMBEL reference structure, along with these 20 ontologies linkage would enable you to reach 504 datasets tracked by LOV.

Configuring and Using the OSF Search API (Screencast)

In this screencast, I will show you how you can leverage the semantic power of the OSF Search endpoint into Drupal using OSF for Drupal. You will see how you can configure the OSF SearchAPI module, how you can turn any property into a filtering facet and how you can display the facets into blocks.

Then I will briefly show you how you can create new search results templates and how the template selection works using type inference.

Finally I will show you how you can enable and disable inference in the search feature, and how you can leverage the semantic structure of your data to change the relevancy of the search results returned. You have all the leisure to boost different characteristics of your data to return more relevant results to your users.

 


tut_14_blog_400

Specifying Field Widgets for OSF Entities Fields (Screencast)

In this screencast, I will show you how you can use ontologies to specify the field types to use for the classes and properties we map into Drupal using OSF Entities mapping process. Once the field types will be configured for each Datatype property, I will run the mapping process to generate new fields that will use the configured field types. Once done, I will show you the impact of this configuration into the fields and fields instances that are being created into Drupal.

The second part of this screencast focus on the configuration of the field widgets that are being used by each field. Then I will update a few entities using the new forms. I will tell you how you can modify the form by re-ordering the fields, by changing their titles or other configuration options such as their cardinality.

OSF Entities supports the following 18 field types and 34 field widgets.

 


tut_13_blog_400




This blog is a regularly updated collection of my thoughts, tips, tricks and ideas about data mining, data integration, data publishing, the semantic Web, my researches and other related software development.


RSS Twitter LinkedIN


Follow

Get every new post on this blog delivered to your Inbox.

Join 93 other followers:

Or subscribe to the RSS feed by clicking on the counter:




RSS Twitter LinkedIN