My contributions to the LinkedBrainz project

Posted on July 29, 2011 by zazi0815

I worked over the last month on the LinkedBrainz project. I was responsible for continuing the mapping of the MusicBrainz Next Generation Schema (MBZ NGS) into appropriated descriptions that make use of various Semantic Web ontologies (especially the Music Ontology (MO)) that was initialised by kurtjx last year and slightly continued by barryn et al. This mapping is described with the help of the D2RQ mapping language and can be executed on a D2R Server (D2RS) instance.
During that time I created various new projects on GitHub or committed changes to existing ones. That’s why, I would like to give you an overview about the various projects and their main content in the following:

The result of my work so far is that I couldn’t finish the whole mapping of MBZ NGS into appropriated Semantic Web ontology terms. There are still many mappings not available for the link types of the MBZ NGS Advanced Relationships and some where appropriated concepts and/or properties did not exist yet (see above).
Finally, it would be fine to implement a vendor-independent mapping that is based on the R2RML mapping language.

PS: Since the test suite is based on tests that compare the results of SQL queries against the results of SPARQL queries, it can be applied on any other MBZ NGS to MO etc. mapping as well (equivalents to specific D2RQ Translator classes must implement the D2RQ Translator class interface)

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Semantic Federation of Music and Music-Related Information for Establishing a Personal Music Knowledge Base

Posted on July 17, 2011 by zazi0815

Finally, I finished my studies in May 2011. I’m now a degreed something (I would prefer the name Philosphical Engineer or Data Engineer or something like that 😉 ). Since I’m still struggling with formatting my thesis into HTML (LaTeX to HTML transformation doesn’t seem to an easy task when utilising quite customized macros 😦 – anyone woh can help me realizing this task is more than welcome).
So here we go with the PDF version of my thesis and the HTML-based presentation slides* of my defence talk.

Abstract:

Music is perceived and described very subjectively by every individual. Nowadays, people often get lost in their steadily growing, multi-placed, digital music collection. Existing music player and management applications get in trouble when dealing with poor metadata that is predominant in personal music collections. There are several music information services available that assist users by providing tools for precisely organising their music collection, or for presenting them new insights into their own music library and listening habits. However, it is still not the case that music consumers can seamlessly interact with all these auxiliary services directly from the place where they access their music individually.
To profit from the manifold music and music-related knowledge that is or can be available via various information services, this information has to be gathered up, semantically federated, and integrated into a uniform knowledge base that can personalised represent this data in an appropriate visualisation to the users. This personalised semantic aggregation of music metadata from several sources is the gist of this thesis. The outlined solution particularly concentrates on users’ needs regarding music collection management which can strongly alternate between single human beings.
The author’s proposal, the personal music knowledge base (PMKB), consists of a client-server architecture with uniform communication endpoints and an ontological knowledge representation model format that is able to represent the versatile information of its use cases. The PMKB concept is appropriate to cover the complete information flow life cycle, including the processes of user account initialisation, information service choice, individual information extraction, and proactive update notification.
The PMKB implementation makes use of SemanticWeb technologies. Particularly the knowledge representation part of the PMKB vision is explained in this work. Several new Semantic Web ontologies are defined or existing ones are massively modified to meet the requirements of a personalised semantic federation of music and music-related data for managing personal music collections. The outcome is, amongst others,

  • a new vocabulary for describing the play back domain,
  • another one for representing information service categorisations and quality ratings, and
  • one that unites the beneficial parts of the existing advanced user modelling ontologies.

The introduced vocabularies can be perfectly utilised in conjunction with the existing Music Ontology framework. Some RDFizers that also make use of the outlined ontologies in their mapping definitions, illustrate the fitness in practise of these specifications.
A social evaluation method is applied to carry out an examination dealing with the reutilisation, application and feedback of the vocabularies that are explained in this work. This analysis shows that it is a good practise to properly publish Semantic Web ontologies with the help of some Linked Data principles and further basic SEO techniques to easily reach the searching audience, to avoid duplicates of such KR specifications, and, last but not least, to directly establish a “shared understanding”. Due to their project-independence, the proposed vocabularies can be deployed in every knowledge representation model that needs their knowledge representation capacities.
This thesis added its value to make the vision of a personal music knowledge base come true.

Have fun!

See also: http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-72434

*) best view with Firefox 4.x+, full-screen mode on, and Univers font family installed

PS: the HTML-based presentation slides are based on an extended version of the Slidy template

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Musical Characteristics Analysis

Posted on March 11, 2011 by zazi0815

This is a follow-up post of the recently published one that introduced a categorisation of musical characteristics.

Audio Signal Feature Extraction and Derivation

Even the process of feature extraction and derivation from audio signals is a rather complex one. The graphic above illustrates an abstraction of the main tasks that are necessary to gain from given audio signals compact descriptions that are known as musical fingerprints. They should ideally act as a robust and correct song identification mechanism and be applicable for a very fast song recognition. That is why, they have to be efficient computable and compact as possible (see [1]). Fortunately, the whole audio signal analysis process can be outsourced and various music information services provide different functionalities to support this task, e.g., Echo Nest or Canoris.

Musical Characteristics Analysis

A full-fledged work flow of musical characteristics analysis includes a music context data extraction process, besides an audio signal analysis task to retrieve music content data. Thereby, a metadata enhancement task makes use of further information services, instead of only relying on information that can be gained from a music document (see the graphic above). It is important to note that the processes of classification, categorisation and similarity calculations should be driven by user profiles to be adaptable [2]. Finally, fuzzy and abstract feature descriptions are necessary to enable an intuitive handling with the music knowledge base.
Please have a look at my bachelor-like thesis [2] to get a deeper insight of the background of music content and context data, and the not trivial extraction and derivation process of musical characteristics. Even this whole analysis process can be outsourced (as needed) to external music information services, e.g., Echo Nest.
A PDF version of the audio signal feature extraction and derivation figure can be found here and one of the musical characteristics analysis figure there. These graphics are freely usable and sharable under the Creative Common Attribution 3.0 Unported license.
 

[1] Cano, Pedro; “Content-based Audio Search: From Fingerprinting to Semantic Audio
Retrieval”; PhD thesis; Technology Department of Pompeu Fabra University,
Barcelona; 2007
[2] Ferris, Bob; “Musical and Music Related Metadata and Features for Administration of Private Music Collections”; smiy.org; 2010

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Categories of Musical Characteristics

Posted on February 26, 2011 by zazi0815

Categories of Musical Features

The phenomenon of music in its full range can be described by manifold concepts and subjective perceived features. In a nutshell these are musical characteristics. Their origins are varied from sciences i.e., physics, psychoacoustics, music theory, and musicology, or they are established by a community or culture.
Generally, musical characteristics are highly versatile and can be arranged in different levels and sub categories. The classification above is taking its cue from the conceptual framework Musical Audio Mining (MAMI; see [1]). It is a translation of the categorisation that was introduced and fully explained in my bachelor-like thesis. It has three main types. These are basic audio signal features, derived audio signal features and taxonomies. Classifications of the latter ones are derived from features of the former ones. Besides, entities of taxonomies, e.g., music genre taxonomies, can be evolved from music context data or a combination of both main types of music description.
A PDF version of that figure can be found here. This graphic is freely usable and sharable under the Creative Common Attribution 3.0 Unported license.

[1] Lesaffre, Micheline; “Music Information Retrieval – Conceptual Framework, Annotation and User Behaviour”; PhD thesis; Faculty of Arts and Philosophy, Department of Art, Music and Theatre Sciences Ghent University; 2006

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A generalisation of the Linked Data publishing guideline

Posted on February 17, 2011 by zazi0815

Initially (in summer 2006), Tim Berners Lee composed the Linked Data publishing guideline to address shortcomings of predominant data providing practices in the Web and to promote the benefits of utilizing Semantic Web knowledge representation models for that issue. Later, people started realizing the independency of these principles to concrete applied technologies, i.e., the Linked Data publishing guideline is generally independent from Semantic Web technologies. This movement is especially propagated by Kingsley Idehen (see [1]).
The Linked Data publishing guideline addresses the following five main principles. Whereby, the fifth one is a recently made, optional addition that was suggested by Matthew Rowe (see [2]).

  1. Use a name reference mechanism for resource identification, e.g., URIs (cf. the first interface constraint of the REST architectural style).
  2. Use an information resource delivering mechanism on top of that name reference mechanism, e.g., a URI scheme, for example, HTTP URIs, to enable the transfer of a document that contains at least the information resource to a requested resource identifier (cf. the second interface constraint of the REST architectural style*).
  3. Use a resource description mechanism to facilitate the expression of knowledge (assertions) regarding the denoted resource, e.g., RDF (cf. the forth interface constraint of the REST architectural style).
  4. Provide a resource connection mechanism to permit the embedding of links. They refer to other resources to establish meaningful (unambiguously as possible) interpretations of a received information resource. This enforces knowledge exploration, e.g., via the HTTP Link header field, if, for instance, the specification of the media type does not make link embedding in the representation possible (cf. “follow your nose” and the fifth interface constraint of the REST architectural style).
  5. Provide an information resource modification mechanism by allowing (optionally) the transfer of (a) modification(s) of received content by using the resource identifier of the request, e.g., changes are entered in a (HTML) form and sent to the server by applying the HTTP PATCH method (cf. the third interface constraint of the REST architectural style).

Thereby, especially the last principle directs into a read and write enabled Linked Data information space scenario (see [3]). A good overview of relevant specifications for Linked Data in the context of the Semantic Web can be found at linkeddata-specs.info.
 

*) Note, this constraint was later explicitly phrased, see the referenced presentation.
 

[1] Idehen, Kingsley; “What is Linked Data, really?”; openlinksw.com; 2011
[2] Rowe, Matthew; “A Proposed 5th Rule/Principle for Linked Data: Push”; matthew-rowe.com; 2010
[3] Berners-Lee, Tim; “Read-Write Linked Data”; w3.org; 2010
 

Further related resources:

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The common, layered Semantic Web technology stack

Posted on January 10, 2011 by zazi0815

A visualization of the common, layered Semantic Web technology stack
Here is a visualization of the common, layered Semantic Web technology stack. This graphic is a modified version of the Semantic Web technology stack visualization created by Benjamin Nowack. This figure is freely usable and sharable under the Creative Common Attribution 3.0 Unported license.
Here are some references to get into some parts of the different layers:

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Ontologies and Vocabularies in the PMKB* space

Posted on December 20, 2010 by zazi0815

Ontologies and Vocabularies in the PMKB* space

It’s time to present an overview of applied, related, modified and created ontologies and vocabularies I’m currently working with. The graphic above shows an overview of applied ontologies and vocabularies in the knowledge representation model of the personal music knowledge base (PMKB). One can see the reutilisation of existing Semantic Web ontologies in new ones and furthermore, also their application in other new Semantic Web ontologies. A link from one vocabulary to another one was created, when at least one sub class, sub property, domain or range relation to another vocabulary exists. Some existing Semantic Web ontologies were also modified during the development process for required universals for the knowledge representation model of the personal music knowledge base.
In the following are reference to the specifications of the mentioned ontologies, vocabularies and schemata.
 
existing vocabularies:

modified vocabularies:

new vocabularies:

PS: sorry for the overlaying arcs, unfortunately I did not get a better design managed with Inkscape (any helpful hints are very welcome)

*) PMKB is the acronym for personal music knowledge base the project I’m currently working on 😉

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The Recommendation Ontology

Posted on August 7, 2010 by zazi0815

Hello everybody,

today I like to present the Recommendation Ontology, which is an ontology for modeling high level recommendations on/ for the Semantic Web. Thereby, the ontology hook up parts of the Similarity Ontology, DCMI Metadata Terms, the Association Ontology and the Ordered List Ontology.

The Recommendation Ontology - Recommendation concept

The graphic above illustrates the core of the Recommendation Ontology, the rec:Recommendation concept (see also here, for an extended view, and here, for an extended graph with relations, of this concept). A rec:Recommendation instance can consist of

Since rec:Recommendation is a sub class of ao:LikeableAssociation, it is also possible to like (ao:likeminded), rate (rev:rating) or give a feedback (rev:Feedback) to a recommendation. Furthermore, one can also relate detailed association (sim:Association based) or similarity statements (sim:Similarity based) to a recommendation by using the property ao:included_association.

The Recommendation Ontology - Ranked recommendation concept as graph with relations

As an extension of rec:Recommendation, we built the rec:RankedRecommendation concept to enable also ordered (ranked) recommendations at a high level. The graphic above illustrates this concept as graph with relations (see also here, for an extended graph view). rec:RankedRecommendation is not only a sub class of rec:Recommendation, furthermore, it is also a sub class of olo:OrderedList, which enables all features of an ordered list also to this concept. Following this design, a rec:ranked_recommendation_object is not only a sub property of rec:recommendation_object, but also a sub property of olo:slot. That means the recommendation objects are now related by using the property olo:item.
Below are two examples of use cases modelled with help of the Recommendation Ontology. The first one describes an extended music track recommendation and the second one a simple music track recommendation example.

@prefix rec: <http://purl.org/ontology/rec/core#> .
@prefix ex: <http://example.org/> .
@prefix olo: <http://purl.org/ontology/olo/core#> .
@prefix isi: <http://purl.org/ontology/is/inst/> .
@prefix ao: <http://purl.org/ontology/ao/core#> .
@prefix sim: <http://purl.org/ontology/similarity/> .
@prefix is: <http://purl.org/ontology/is/core#> .
@prefix dbtune: <http://dbtune.org/musicbrainz/resource/track/> .
 
dbtune:008e72df-6469-4557-8b5b-c54c3285fbd3
   rec:recommendation ex:AMusicRecommendation .
 
ex:AMusicRecommendation a rec:RankedRecommendation ;
   rec:recommender isi:lastfm ;
   sim:subject dbtune:008e72df-6469-4557-8b5b-c54c3285fbd3 ;
   rec:ranked_recommendation_object [
      a olo:Slot ;
      olo:item dbtune:097c362d-72b7-4a53-96e2-d9ff02f8be1f ;
      olo:index 1
      ] ;
   rec:ranked_recommendation_object [
      a olo:Slot ;
      olo:item dbtune:161d35d9-3e31-41e9-8392-cf5d2c03b31d ;
      olo:index 2
      ] ;
   rec:ranked_recommendation_object [
      a olo:Slot ;
      olo:item dbtune:093c1742-ebda-45cd-a50c-734e5d92e7f2 ;
      olo:index 3
      ] ;
   rec:ranked_recommendation_object [
      a olo:Slot ;
      olo:item dbtune:0a208327-525a-429b-8e79-51669bfb81f7 ;
      olo:index 4
      ] ;
   rec:ranked_recommendation_object [
      a olo:Slot ;
      olo:item dbtune:0161855d-0b98-4f2d-b2ab-446dbd8d6759 ;
      olo:index 5
      ] ;
   rec:ranked_recommendation_object [
      a olo:Slot ;
      olo:item dbtune:014f2510-e653-43e9-862a-880ac6388bb1 ;
      olo:index 6
      ] ;
   rec:ranked_recommendation_object [
      a olo:Slot ;
      olo:item dbtune:0ab210b0-18c6-4c52-aee7-78f6012b9192 ;
      olo:index 7
      ] ;
   rec:ranked_recommendation_object [
      a olo:Slot ;
      olo:item dbtune:027326ef-1455-48c9-8543-ac908eb71925 ;
      olo:index 8
      ] ;
   rec:ranked_recommendation_object [
      a olo:Slot ;
      olo:item dbtune:04dc3d0b-dff4-41a0-8bf3-12b4b34460a6 ;
      olo:index 9
      ] ;
   rec:ranked_recommendation_object [
      a olo:Slot ;
      olo:item dbtune:0348eea1-8178-4dc1-8a37-d09b5897ace2 ;
      olo:index 10
      ] ;
   ao:included_association ex:SimAssociation01 ;
   ao:included_association ex:SimAssociation02 ;
   ao:included_association ex:SimAssociation03 ;
   ao:included_association ex:SimAssociation04 ;
   ao:included_association ex:SimAssociation05 ;
   ao:included_association ex:SimAssociation06 ;
   ao:included_association ex:SimAssociation07 ;
   ao:included_association ex:SimAssociation08 ;
   ao:included_association ex:SimAssociation09 ;
   ao:included_association ex:SimAssociation10 .
 
ex:lfmTrackSimilarity a sim:AssociationMethod ;
   is:info_service isi:lastfm .
 
ex:Association01 a sim:Similarity ;
   sim:subject dbtune:008e72df-6469-4557-8b5b-c54c3285fbd3 ;
   sim:object dbtune:097c362d-72b7-4a53-96e2-d9ff02f8be1f ;
   sim:weight 1.0 ;
   sim:method ex:lfmTrackSimilarity .
 
ex:Association02 a sim:Similarity ;
   sim:subject dbtune:008e72df-6469-4557-8b5b-c54c3285fbd3 ;
   sim:object dbtune:161d35d9-3e31-41e9-8392-cf5d2c03b31d ;
   sim:weight 0.968842 ;
   sim:method ex:lfmTrackSimilarity .
 
ex:Association03 a sim:Similarity ;
   sim:subject dbtune:008e72df-6469-4557-8b5b-c54c3285fbd3 ;
   sim:object dbtune:093c1742-ebda-45cd-a50c-734e5d92e7f2 ;
   sim:weight 0.547951 ;
   sim:method ex:lfmTrackSimilarity .
 
ex:Association04 a sim:Similarity ;
   sim:subject dbtune:008e72df-6469-4557-8b5b-c54c3285fbd3 ;
   sim:object dbtune:0a208327-525a-429b-8e79-51669bfb81f7 ;
   sim:weight 0.449539 ;
   sim:method ex:lfmTrackSimilarity .
 
ex:Association05 a sim:Similarity ;
   sim:subject dbtune:008e72df-6469-4557-8b5b-c54c3285fbd3 ;
   sim:object dbtune:0161855d-0b98-4f2d-b2ab-446dbd8d6759 ;
   sim:weight 0.401746 ;
   sim:method ex:lfmTrackSimilarity .
 
ex:Association06 a sim:Similarity ;
   sim:subject dbtune:008e72df-6469-4557-8b5b-c54c3285fbd3 ;
   sim:object dbtune:014f2510-e653-43e9-862a-880ac6388bb1 ;
   sim:weight 0.36673 ;
   sim:method ex:lfmTrackSimilarity .
 
ex:Association07 a sim:Similarity ;
   sim:subject dbtune:008e72df-6469-4557-8b5b-c54c3285fbd3 ;
   sim:object dbtune:0ab210b0-18c6-4c52-aee7-78f6012b9192 ;
   sim:weight 0.36142 ;
   sim:method ex:lfmTrackSimilarity .
 
ex:Association08 a sim:Similarity ;
   sim:subject dbtune:008e72df-6469-4557-8b5b-c54c3285fbd3 ;
   sim:object dbtune:027326ef-1455-48c9-8543-ac908eb71925 ;
   sim:weight 0.351277 ;
   sim:method ex:lfmTrackSimilarity .
 
ex:Association09 a sim:Similarity ;
   sim:subject dbtune:008e72df-6469-4557-8b5b-c54c3285fbd3 ;
   sim:object dbtune:04dc3d0b-dff4-41a0-8bf3-12b4b34460a6 ;
   sim:weight 0.350151 ;
   sim:method ex:lfmTrackSimilarity .
 
ex:Association10 a sim:Similarity ;
   sim:subject dbtune:008e72df-6469-4557-8b5b-c54c3285fbd3 ;
   sim:object dbtune:0348eea1-8178-4dc1-8a37-d09b5897ace2 ;
   sim:weight 0.336997 ;
   sim:method ex:lfmTrackSimilarity .

This RDF/Turtle representation shows an extended music track recommendation modeled as rec:RankedRecommendation instance. It is based on the music track recommendations of James Brown’s “Get Up (I Feel Like Being a) Sex Machine” from Last.fm. Hence, the subject of the rec:recommendation relation is a resolvable URI to a description of James Brown’s “Get Up (I Feel Like Being a) Sex Machine” at MusicBrainz, modeled and represented as Semantic Graph and provided by DBTune.
Since a rec:RankedRecommendation is a sub class of rec:Recommendation and olo:OrderedList, it can also make use of ordered list features. That means, this
enables the opportunity to also provide an ordered (/ranked) list of recommendation objects (related by rec:ranked_recommendation_object), by hiding the details of their ranking features on this level. These details are provided by further association statements (here sim:Similarity) related by using the ao:included_association property.
Each slot item (related by olo:item) is also a music track description at MusicBrainz, modeled and represented as Semantic Graph and provided by DBTune. Furthermore, each similarity statement (sim:Similarity) includes

  • the seed music track (related by sim:subject), on which this similarity calculation is based on,
  • the other music track (related by sim:object),
  • the weight of the similarity (related by sim:weight) and
  • the association (here similarity) method (related by sim:method) of the similarity calculation.

This association method (sim:AssociationMethod) can not be further specified here, because it is based on a proprietary algorithm from Last.fm. Therefore, the complete recommendation (related by rec:recommender) and also the association method itself
(related by is:info_service) is associated to the Information Service Last.fm.

@prefix rec: <http://purl.org/ontology/rec/core#> .
@prefix ex: <http://example.org/> .
@prefix isi: <http://purl.org/ontology/is/inst/> .
@prefix sim: <http://purl.org/ontology/similarity/> .
@prefix is: <http://purl.org/ontology/is/core#> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix dbtune: <http://dbtune.org/musicbrainz/resource/track/> .
 
ex:AMusicRecommendation a rec:Recommendation ;
   rec:for dbtune:008e72df-6469-4557-8b5b-c54c3285fbd3 ;
   rec:recommender isi:lastfm ;
   rec:recommendation_audience ex:FunkyPeople ;
   sim:subject dbtune:008e72df-6469-4557-8b5b-c54c3285fbd3 ;
   rec:recommendation_object dbtune:097c362d-72b7-4a53-96e2-d9ff02f8be1f ;
   rec:recommendation_object dbtune:161d35d9-3e31-41e9-8392-cf5d2c03b31d ;
   rec:recommendation_object dbtune:093c1742-ebda-45cd-a50c-734e5d92e7f2 ;
   rec:recommendation_object dbtune:0a208327-525a-429b-8e79-51669bfb81f7 ;
   rec:recommendation_object dbtune:0161855d-0b98-4f2d-b2ab-446dbd8d6759 ;
   rec:recommendation_object dbtune:014f2510-e653-43e9-862a-880ac6388bb1 ;
   rec:recommendation_object dbtune:0ab210b0-18c6-4c52-aee7-78f6012b9192 ;
   rec:recommendation_object dbtune:027326ef-1455-48c9-8543-ac908eb71925 ;
   rec:recommendation_object dbtune:04dc3d0b-dff4-41a0-8bf3-12b4b34460a6 ;
   rec:recommendation_object dbtune:0348eea1-8178-4dc1-8a37-d09b5897ace2 ;
   sim:method ex:lfmTrackSimilarity .
 
ex:lfmTrackSimilarity a sim:AssociationMethod ;
   is:info_service isi:lastfm .
 
ex:FunkyPeople a foaf:Group .

This RDF/Turle representation is a simplified example from the extended music track recommendation example above. It is simply stripped down to its high level recommendation. The target, where the recommendation is for, is now related by the inverse property of rec:recommendation, rec:for. Furthermore, it includes a fictional audience group (ex:FunkyPeople), which is related by the property rec:recommendation_audience. Finally, the recommendation objects are now simply related by the property rec:recommendation_object. Of course, this doesn’t include the ranking, it is now an unordered list. However, it still includes the relation to its similarity method (ex:lfmTrackSimilarity).

The Recommendation Ontology and the illustrated examples above demonstrates the reutilization, specialization and application of concepts of existing ontologies. I like to invite you to create further, more specialized concepts and properties, which are based on the introduced ontologies, or further examples, which make use of the Recommendation Ontology, e.g. suggested items from Amazon, which are maybe also modeled with help of the GoodRelations vocabulary.

I would like to thank very much the whole Music Ontology Specification mailing list group to help to establish this multiple purpose recommendation ontology and especially Kurt Jacobson for providing everytime new input and ideas. Please feel free to reuse and extend the Recommendation Ontology also for your own projects (let me know your use cases 😉 ). Comments, suggestions and critics are also very welcome.

Cheers,

Bob

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The Play Back Ontology

Posted on July 27, 2010 by zazi0815

Hello everybody,

today I like to present the Play Back Ontology, whose use cases are trying to combine and extend the previous announced ontologies (Ordered List Ontology, Counter Ontology and Association Ontology) and of course further ontologies (e.g. Music Ontology, Similarity Ontology, DCMI Metadata Terms, e.g. the Bibliographic Ontology or the Functional Requirements for Bibliographic Records Vocabulary), which are needed to model them and for ontology alignment. Therefore, the Play Back Ontology provides basic concepts and properties for modelling knowledge representations that are related to the play back domain, e.g. a playlist, play back and skip counter, on/ for the Semantic Web.

The play back domain is a subset of the media domain. It deals with playing back some media, e.g. videos, music tracks or slideshows, and how used media can be structured or organized.

On the basis of this definition the objects that are included into this domain are media objects. That means all concepts of the Play Back Ontology are somehow related to at least one media object. The range of these media objects is currently restricted to the concepts bibo:Document and frbr:Endeavour, incl. all their sub classes, e.g. mo:Track.

The Play Back Ontology - Playlist concept

The Play Back Ontology - Playlist concept as graph with relations

The graphics above illustrate the pbo:Playlist concept, which is a sub class of bibo:Document and olo:OrderedList. Hence, a specialized ordered list of the media domain. Therefore, a pbo:Playlist instance consists of pbo:PlaylistSlot instances (related by pbo:playlist_slot), which are related to at least one media object by using the property pbo:playlist_item. pbo:Playlist has a further sub class, pbo:FixedPlaylist, to described playlists or sections of playlists, which have a strict order, e.g. a section of music tracks that should be played always one after another, or that are somehow related to each other. Furthermore, pbo:Playlist instances can be related to something by using the property pbo:playlist. To complete the description of the playlist concept of the Play Back Ontology in its current state, it is also necessary to mention the semantic relation pbo:transition, which can be used to associate a description of a transition between two neighbouring playlist slots, e.g. two successice music tracks in a dj mix.

The Play Back Ontology – Play back and skip counter concepts as graph with relations

The Play Back Ontology consists further more of a couple of media action counters, which are represented by pbo:MediaActionCounter. In the graphic above you can see this concept and its sub classes pbo:PlayBackCounter and pbo:SkipCounter. Due to the property pbo:media_object, which is a sub property of co:object, it is possible to only associate media objects to pbo:MediaActionCounter instances. The same behaviour is realized for media object relations to co:ScrobbleEvent instances by using the property pbo:media_scrobble_object. Finally, one can represent the skip time of a media object for a skip event by utilizing the refined scrobble event pbo:SkipEvent, which is in the domain of the property pbo:skip_time.
Below are examples of use cases of the Play Back Ontology. The first one describes a music playlist and the second one the usage of play back and skip counter.

The Play Back Ontology - Playlist example

@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@prefix dc: <http://purl.org/dc/elements/1.1/> .
@prefix olo: <http://purl.org/ontology/olo/core#> .
@prefix pbo: <http://purl.org/ontology/pbo/core#> .
@prefix ao: <http://purl.org/ontology/ao/core#> .
@prefix mo: <http://purl.org/ontology/mo/> .
@prefix ex: <http://example.org/> .
@prefix sim: <http://purl.org/ontology/similarity/> .
 
ex:FunkyPlaylist a pbo:Playlist ;
   dc:title "Funky Playlist"^^xsd:string ;
   dc:description "A playlist full of funky legends"^^xsd:string ;
   dc:creator <http://foaf.me/zazi#me> ;
   olo:length 2 ;
   sim:association ex:ZazisAssociation ;
   sim:association ex:BobsAssociationInUse ;
   pbo:playlist_slot [
      a pbo:PlaylistSlot ;
      olo:index 1 ;
      pbo:playlist_item ex:SexMachine
   ] ;
   pbo:playlist_slot [
      a pbo:PlaylistSlot ;
      olo:index 2 ;
      pbo:playlist_item ex:GoodFoot
   ] .
 
ex:SexMachine a mo:Track ;
   dc:title "Sex Machine"^^xsd:string ;
   dc:creator <http://dbpedia.org/resource/James_Brown> .
 
ex:GoodFoot a mo:Track ;
   dc:title "Good Foot"^^xsd:string .
 
ex:ZazisAssociation a sim:Association ;
   dc:creator <http://foaf.me/zazi#me> ;
   ao:genre "Funk"^^xsd:string ;
   ao:mood "party"^^xsd:string ;
   ao:occasion "my birthday party 2008"^^xsd:string .
 
ex:BobsAssociation a sim:Association ;
   dc:creator <http://foaf.me/zazi#me> ;
   ao:genre ex:Funk ;
   ao:mood "happy"^^xsd:string ;
   ao:occasion "good feeling music"^^xsd:string .
 
ex:BobsAssociationInUse a ao:LikeableAssociation ;
   ao:included_association ex:BobsAssociation ;
   ao:likeminded <http://moustaki.org/foaf.rdf#moustaki> .
 
ex:Funk a mo:Genre .

The graphic (see here, for another graph view, and here, for an extended graph
view, of this example) above illustrates the RDF/Turtle representation below the graphic, which is a music playlist example created with the Play Back Ontology (see also RDF and N3 for downloadable representations of this example). This music playlist is modeled as pbo:Playlist instance and is annotated with further editoral metadata, e.g. a title, a description and a creator, and has a fixed length (olo:length) that represents the number of slots this specific ordered list instance consist of. Each slot is modeled as pbo:PlaylistSlot instance to ensure that only some media objects are in the playlist. This is especially realized by the range restriction of pbo:playlist_item. In the example these items are mo:Track instances.
Furthermore, a playlist can be annotated with association statements (sim:Association based) related by sim:association. Please have a look at the previous blog post about the Association Ontology for an explanation of these statements.

The Play Back Ontology - Play back and skip counter example as graph with relations

@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@prefix dc: <http://purl.org/dc/elements/1.1/> .
@prefix co: <http://purl.org/ontology/co/core#> .
@prefix pbo: <http://purl.org/ontology/pbo/core#> .
@prefix mo: <http://purl.org/ontology/mo/> .
@prefix ex: <http://example.org/> .
@prefix event: <http://purl.org/NET/c4dm/event.owl#> .
@prefix time: <http://www.w3.org/2006/time#> .
@prefix am: <http://vocab.deri.ie/am#> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix ao: <http://purl.org/ontology/ao/core#> .
 
ex:PlayBackCounter a pbo:PlayBackCounter ;
   dc:title "Play Back Counter"^^xsd:string ;
   dc:creator <http://foaf.me/zazi#me> ;
   dc:description "A play back counter of a specific music track"^^xsd:string ;
   co:count 2 ;
   pbo:media_object ex:SexMachine .
 
ex:SkipCounter a pbo:SkipCounter ;
   dc:title "Skip Counter"^^xsd:string ;
   dc:creator <http://foaf.me/zazi#me> ;
   dc:description "A skip counter of a specific music track"^^xsd:string ;
   co:count 1 ;
   pbo:media_object ex:SexMachine .
 
ex:SexMachine a mo:Track ;
   dc:title "Sex Machine"^^xsd:string ;
   dc:creator <http://dbpedia.org/resource/James_Brown> .
 
ex:SexMachineSE1 a co:ScrobbleEvent ;
   event:time [
      a time:Instant ;
      time:inXSDDateTime "2010-07-15T11:21:52+01:00"^^xsd:dateTime
      ] ;
   event:agent <http://foaf.me/zazi#me> ;
   ao:used_application ex:iTunes ;
   ao:used_device ex:MyPC ;
   co:event_counter ex:PlayBackCounter .
 
ex:SexMachineSE2 a co:ScrobbleEvent ;
   event:time [
      a time:Instant ;
      time:inXSDDateTime "2010-07-15T11:27:52+01:00"^^xsd:dateTime
      ] ;
   event:agent <http://foaf.me/zazi#me> ;
   ao:used_application ex:iTunes ;
   ao:used_device ex:MyPC ;
   co:event_counter ex:PlayBackCounter .
 
ex:SexMachineSE3 a co:ScrobbleEvent ;
   event:time [
      a time:Instant ;
      time:inXSDDateTime "2010-07-15T11:35:52+01:00"^^xsd:dateTime
      ] ;
   event:agent <http://foaf.me/zazi#me> ;
   ao:used_application ex:iTunes ;
   ao:used_device ex:MyPC ;
   co:event_counter ex:SkipCounter .
 
ex:iTunes a am:Application .
 
ex:MyPC a owl:Thing .

The graphic (see here, for an extended graph view) above illustrates the RDF/Turtle representation below the graphic, which describes a play back and skip counter created with the Play Back Ontology (see also RDF and N3 for downloadable representations of this example). It shows, how co:Counter can be specialized as a play back counter (pbo:PlayBackCounter) and as a skip counter (pbo:SkipCounter) for a specific music track (here of the type mo:Track), related by the property pbo:media_object.
Each time, when this specific music track was played, a co:ScrobbleEvent instance was created and linked to its play back counter by the property co:event_counter. This happened also, when this specific music track was skipped. The only difference is that such an event is related to its skip counter pbo:SkipCounter.
To describe scrobble events more in detail (besides event:time and event:agent), one can add properties such as ao:used_application (to describe the application, which is related to the scrobbling activity, e.g. ex:iTunes) or ao:used_device (to describe the device, which is related to the scrobbling activity, e.g. ex:MyPC).

The Play Back Ontology and the illustrated examples above demonstrates the reutilization, specialization and application of concepts of existing ontologies, as mention in the introduction of this blog post. I like to invite you to create further, more specialized concepts and properties, which are based on the introduced ontologies.

I would like to thank very much the whole Music Ontology Specification mailing list group to help to establish this multiple purpose play back ontology and especially Kurt Jacobson for providing everytime new input and ideas. Please feel free to reuse and extend the Play Back Ontology also for your own projects (let me know your use cases 😉 ). Comments, suggestions and critics are also very welcome.

Cheers,

Bob

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