Florian Huber

Transforming music data into a PoolParty project

Goal

For the Nolde project it was requested to build a knowledge graph, containing detailed information about the austrian music scene: artists, bands and their music releases. We decided to use PoolParty, since theses entities should be accessible in an editorial workflow. More details about the implementation will be provided in a later blog post.

In the first round I want to share my experiences with the mapping of music data into SKOS. Obviously, LinkedBrainz was the perfect source to collect and transform such data since this is available as RDF/NTriples dumps and even providing a SPARQL endpoint! LinkedBrainz data is modeled using the Music Ontology.

E.g. you can select all mo:MusicArtists with relation to Austria.

SELECT query

I imported LinkedBrainz dump files and imported them into a triple store, together with DBpedia dumps.

With two CONSTRUCT queries, I was able to collect the required data and transform it into SKOS, into a PoolParty compatible format:

Construct Artists

CONSTRUCT Artists#1

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Every matching MusicArtist results in a SKOS concept. The foaf:name is mapped to skos:prefLabel (in German).

As you can see, I used Custom Schema features to provide self-describing metadata on top of pure SKOS features: a MusicBrainz link, a MusicBrainz Id, DBpedia link, homepage…

In addition you can see in the query that also data from DBpedia was collected. In case a owl:sameAs relationship to DBpedia exists, a possible abstract is retrieved. When a DBpedia abstract is available it is mapped to skos:definition.

Construct Releases (mo:SignalGroups) with relations to Artists

Screen Shot 2015-04-10 at 10.59.50

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Similar to the Artists, a matching SignalGroup results in a SKOS Concept. A skos:related relationship is defined between an Artist and his Releases.

Outcome

The SPARQL construct queries provided ttl files that could by imported directly into PoolParty, resulting in a project, containing nearly 1,000 Artists and 10,000 Releases:

PoolParty thesaurus

 

You can reach the knowledge graph by visting the publicly available Linked Data Frontend of PoolParty: http://nolde.poolparty.biz/AustrianMusicGraph

E.g. you can find out details and links about Peter Alexander or Conchita Wurst.

Thomas Thurner

Energy Buildings Performance Scenarios as Linked Open Data

The reduction of green house gas emissions is one of the big global challenges for the next decades. (Linked) Open Data on this multi-domain challenge is key for addressing the issues in policy, construction, energy efficiency, production a like. Today – on the World Environment Day 2014 – a new (linked open) data initiative contributes to this effort: GBPN’s Data Endpoint for Building Energy Performance Scenarios.

gbpn-scenariosGBPN (The Global Buildings Performance Network) provides the full data set on a recently made global scenario analysis for saving energy in the building sector worldwide, projected from 2005 to 2050. The multidimensional dataset includes parameters like housing types, building vintages and energy uses  – for various climate zones and regions and is freely available for full use and re-use as open data under CC-BY 3.0 France license.

To explore this easily, the Semantic Web Company has developed an interactive query / filtering tool which allows to create graphs and tables in slicing this multidimensional data cube. Chosen results can be exported as open data in the open formats: RDF and CSV and also queried via a provided SPARQL endpoint (a semantic web based data API). A built-in query-builder makes the use as well as the learning and understanding of SPARQL easy – for advanced users as well as also for non-experts or beginners.

gbn-filter

The LOD based information- & data system is part of Semantic Web Companies’ recent Poolparty Semantic Drupal developments and is based on OpenLinks Virtuoso 7 QuadStore holding and calculating ~235 million triples as well as it makes use of the RDF ETL Tool: UnifiedViews as well as D2R Server for RDF conversion. The underlying GBPN ontology runs on PoolParty 4.2 and serves also a powerful domain-specific news aggregator realized with SWC’s sOnr webminer.

reegle.info-trusted-linksTogether with other Energy Efficiency related Linked Open Data Initiatives like REEEP, NREL, BPIE and others, GBPNs recent initative is a contribution towards a broader availability of data supporting action agains global warming – as also Dr. Peter Graham, Executive Director of GBPN emphasized “…data and modelling of building energy use has long been difficult or expensive to access – yet it is critical to policy development and investment in low-energy buildings. With the release of the BEPS open data model, GBPN are providing free access to the world’s best aggregated data analyses on building energy performance.”

The Linked Open Data (LOD) is modelled using the RDF Data Cube Vocabulary (that is a W3C recommendation) including 17 dimensions in the cube. In total there are 235 million triples available in RDF including links to DBpedia and Geonames – linking the indicators: years – climate zones – regions and building types as well as user scenarios….

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Andreas Blumauer

SEMANTiCS 2014: Call for Industry Presentations

SEMANTiCS 2014 will take place in Leipzig (Germany) this year from September 4-5. The International Conference on Semantic Systems will be co-located with several workshops and other meetings, e.g. the 2nd DBpedia community meeting.

SEMANTICS-2014-logo-leipzig

SEMANTiCS conference (formerly ‘I-Semantics’) focuses on transfer and industry-related applications of semantic systems and linked data.
Here are some of the options for end-users, vendors and experts to get involved (besides participating as a regular attendee and the option to submit a paper):

  1. Submit an Industry Presentation: http://www.semantics.cc/open-calls/industry-presentations/
  2. Sponsoring / Marketplace / Exhibition: http://www.semantics.cc/sponsoring
  3. Become a reviewer: http://www.semantics.cc/open-calls/call-for-participation/call-for-reviewers/

The organizing committee would be happy to have you on board of the SEMANTiCS 2014 in Leipzig.

Andreas Blumauer

Do you like Google’s Knowledge Graph?

Semantic Enterprise Search enters the second phase.

Finally the Knowledge Graph has arrived in Europe: What has been provided on google.com for the US-Market since May 2012, is now available also for most European countries. Search results are no longer only a list of documents (and advertisements) but also a mashup of facts, points of interest, events etc. referring to the search phrase.

For example, if the user is searching for ‘Wiener Philharmoniker’ (‘Vienna Philharmonic Orchestra’) a factbox including related searches is provided:

Do you like this rather new way of knowledge discovery? We do, except the fact that Google hasn´t properly explained to the audience which technology is behind the Knowledge Graph which is the Web of Linked Data aka the Semantic Web (Do you want to know more about the relationship between the Knowledge Graph and Linked Data? Click here).

But anyway, here are some benefits we can see, if search technologies make use of a ‘knowledge graph’, a ‘knowledge model’, a ‘thesaurus’ or generally spoken: Linked Data.

  • Facts around an object (or an entity) can be found nicely packed up to a dossier
  • Serendipity can be stimulated by ‘related searches’ which means: Users can discover the formely ‘unknown’ in a more comfortable way
  • Data from various sources can be pulled together to a mashup (e.g. ‘upcoming events’ could come from a different database than the basic facts of Vienna Philharmonic Orchestra)
  • Search phrases are well understood by the engine since they are based on concepts and not anymore on literals, e.g. if the user searches for ‘Red Bull Stratos’, also results for ‘Felix Baumgartner’ will be delivered
  • Search can be refined, e.g. if one searches for ‘Vienna’, a list of POIs will be displayed to refine the actual place the user is looking for

Now imagine you would have a search engine in your company’s intranet based on a knowledge graph which is about the enterprise you are working for.

Such an advanced search application would look like this:

  • Data streams and all kind of content from internal sources are nicely mashed with information from the web (e.g. from Twitter, Youtube etc.)
  • Search assistants are provided to help users to refine their information needs to make them more specific
  • Entities and their sub-concepts (e.g. subsidiaries of large companies or regions of countries) are nicely packed together to one dossier

The key question now is: “how to set up a customised knowledge graph for a certain company?”.

Corporate Semantic Web based applications can be realised on top of software platforms like PoolParty. They all have a customised knowledge graph in their core. This is always the basis for concept-based indexing of specialised content from a corporate intranet. The basic standard for this is SKOS which can be used together with advanced query languages like SPARQL. Such graphs can be used for semantic indexing but also to ask for relations like ‘is point-of-interest in’, ‘is event of’, ‘is related search for’ etc. This is the next-generation semantic search which help decision-makers, information professionals and all kind of knowledge workers to improve their work significantly.
One comfortable way to create customised knowledge graphs is to make use of Linked Data sources like Freebase (like Google does) or DBpedia. More details wanted? Take a look at the PoolParty approach for efficient knowledge modeling.