Undefined 0 (2012) 1–0 1 IOS Press

Hello Cleveland! Linked Data Publication of Live Music Archives

Sean Bechhofer a,∗ David De Roure b Kevin Page b a School of Computer Science, The University of Manchester, United Kingdom, [email protected] b Oxford e-Research Centre, University of Oxford, United Kingdom {kevin.page,david.deroure}@oerc.ox.ac.uk

Abstract. We describe the publication of a linked data set exposing metadata from the Internet Archive Live Music Archive. The collection provides access to recorded performances and is linked to existing musical and geographical resources. The dataset contains over 17,000,000 triples describing 100,000 performances by 4,000 artists.

Keywords: Linked Dataset, Music, Internet Archive

1. Introduction more, in live situations artists will frequently play works by other artists (“covers”), providing source The Internet Archive Live Music Archive[12] (fur- content for cover detection algorithms [11]. ther referred to here as LMA) is an online resource An earlier prototype [15] demonstrated how Linked providing access to a large community-contributed Data can be applied to the MIR research process and collection of live recordings. Covering nearly 4,000 the utility of this approach, particularly when gathering artists, chiefly in rock genres, the archive contains over and managing corpora of source audio; however, this 100,000 live recordings made openly available with system re-used pre-existing Linked Data that described the permission of the artists concerned. Audio files the recordings to populate its collections. As compu- are available in a variety of formats, and each record- ing is accompanied by metadata describing informa- tational analysis increases in scale through projects tion about dates, venues, set lists, the provenance of such as SALAMI [4], so too does the value of re- the audio files and so on. publishing existing large repositories such as the LMA From a musicological perspective, the collection is using Linked Data: as it stands, however, extracting valuable for a number of reasons. First of all, it pro- subcollections from the archive is not a straightforward vides access to the underlying audio files. Thus the task. Metadata is largely published as free text fields, LMA provides a corpus that can be used for Music with heterogeneity in detail and inconsistency in con- Information Retrieval (MIR) [3] tasks such as genre tent. detection, key detection, segmentation and so on as We describe an exercise in republishing LMA meta- exemplified by the MIREX series of workshops [5]. data using a linked data [2] approach. We believe 1 It provides multiple recordings by individual artists that publishing the collection metadata as linked data allowing comparisons across performances. Further- brings benefits in supporting query and integration with existing sources. Note that we are dealing here * Corresponding author purely with the metadata, and leave the audio source 1In the case of the Grateful Dead, an act that for many years en- couraged audience taping of performances, the LMA contains over files untouched (but provide links to the online re- 8,000 recorded performances. sources). Enhancing the collection with additional in-

0000-0000/12/$00.00 c 2012 – IOS Press and the authors. All rights reserved 2 formation gained through analysis of those audio files Music The Music Ontology [16,18] provides terms is left to future work. that describe performances, artists and the rela- 2. Approach tionships between them. Events The Event Ontology [17] provides terms for The collection is published using a layered ap- describing events. proach. The core metadata describing the resources is Similarity The Similarity Ontology [8,7] provides essentially published “as is”, without attempts to align terms for asserting associations between entities. entities. Additional information is then added to the This is used to associate artists in the collection collection asserting mapping relationships to other col- with MusicBrainz ids, and locations with last.fm lections such as MusicBrainz [14], GeoNames [6] or venues and GeoNames entities. last.fm [9]. Although some attempts could have been SKOS SKOS [13] labelling properties are used to la- made to reconcile, for example, artist names as used in bel entities. the collection, this is not achieved through modifica- PROV-O The W3C provenance ontology [10]. tion of the core data. This method allows us to explic- VoID The W3C dataset metadata ontology [1]. itly record provenance information about how the as- etree An ontology3 that defines subclasses of Music sociations were derived, which in turn then allows con- Ontology classes and specific properties used in sumers of the data to make decisions about whether or the etree metadata. not to use or trust the relationships asserted. Such an approach is useful for processes such as artist reconcil- The basic modelling pattern used within the data set iation, where matching between recording artists may is shown in Figure 1. The ontology used to describe the be a somewhat fuzzy process – groups or bands can be volatile organisations in terms of membership (al- collection is relatively inexpressive, essentially provid- though they do not appear in the LMA, The Fall’s “re- ing classes for performances and venues and properties volving door of musicians” is a case in point [19]) and for the assertion of values and relationships. we may not always have confidence that descriptions of an artist from different sources match exactly. Data 4. Record Linkage consumers then have the option of using the encoded raw source data or the additional layer of mapped rela- The collection offers possibilities for linking entities tionships. Furthermore, layering allows for the possi- with external datasets. In particular, music artists and bility of further analysis and re-publication of mapping geographical locations are entities that are described in relationships from the source corpus as new techniques a number of external data sources (many of which are and corrections are developed; this could include pub- also published as Linked Data). lication community driven curation and validation. Artist Alignment MusicBrainz [14] provides an “open 3. Modelling music encyclopedia” and provides identifiers for a large number of music artists. MusicBrainz is a clear The collection contains a number of basic entities. candidate for linking from a collection like LMA. Artist The performer of a concert/gig. There is no at- Queries to MusicBrainz taking exact matches on tempt to link or split up bands, duos, etc. names provides a simple alignment between our artists Performance A particular concert/gig that has been and MusicBrainz, covering 1,168 of the 3,981 artists recorded. Each performance is given a unique in the collection. In keeping with the strategy outlined identifier by LMA. in Section 2, the relationships between the artists and Track Performance of a particular track within a Per- MusicBrainz are asserted using the Similarity Ontol- formance Venue Location where a performance takes place. ogy. Figure 2 shows how these relationships are as- serted. Each Artist, Performance, Track and Venue is minted a URI in the collection namespace2 with an appropri- Track Alignment In the current dataset, tracks are not ate path prefix. A number of ontologies are used for aligned to external data sources. See Section 10 for fur- the description of entities. ther discussion.

2http://etree.linkedmusic.org 3http://etree.linkedmusic.org/vocab 3

Fig. 1. Basic Model

Fig. 2. Similarities with MusicBrainz

Geographical Alignment Performances occur at a The raw location information suffers from incon- particular place4 and can thus potentially be mapped sistencies in presentation (e.g. Chicago, IL; Chicago, to geographical locations in collection such as GeoN- Il; Chicago, Illinois; Chicago etc.). Location informa- ames. Concert performances also tend to take place in tion may in some cases also be ambiguous, with only specific venues (theatres, concert halls etc) which are city or town name being given (e.g. Amsterdam or described in data sources such as last.fm [9]. Informa- Springfield). As discussed in Section 2, our approach tion about venues and general locations is given in the in the collection is to expose the underlying source source metadata, with variable granularity and consis- data and layer additional mappings on top. Thus each tency, using the venue and coverage tags: performance is associated with a unique venue with a name and location. A description that refers to the venue The name of the venue, e.g. concert hall, club, venue Academy in Manchester could refer to one of festival. where the performance was recorded. at least four distinct venues and, since there is insuffi- coverage The larger geographical area for the loca- cient information in the raw LMA data to reliably dis- tion, e.g. city or state. ambiguate, collapsing them is undesirable. Alignments between venues and external sources are thus again as- serted using similarities. 4To the best of our knowledge, the collection does not contain examples of performances recorded by artists collaborating virtually Two external data sources provide additional infor- in geographically distributed locations. mation about venues and geographical locations which 4 is of use here. Both sources provide latitude/longitude This resulted in the core data collection. SPARQL information. queries against this collection were then used to extract field data for processing (e.g venue and location), with GeoNames GeoNames provides identifiers for over the resulting mapping/association triples added back eight million placenames. into the triple store. Conversion to RDF was thereby in last.fm Last.fm provides a comprehensive list of mu- itself a useful step in completing the process. sic venues. 7. Licensing For a performance with a given venue and coverage, candidates for mappings are obtained through queries The dataset is made available under the CC0 1.0 5 Creative Commons public domain waiver to the GeoNames and last.fm APIs. If potential can- Universal licence. Note that this license applies to the published didates are returned from both collections, the geo- metadata, not the source audio files served by the In- graphical locations are cross-compared (both GeoN- ternet Archive servers. ames and last.fm provide latitude/longitude informa- tion). Geographical co-location (up to a certain thresh- 8. Access old) then gives us further confidence in the potential alignment. The dataset can be accessed via a SPARQL endpoint Mapping candidates are associated with venues as detailed in Table 1. Content-negotiated URIs (using again using an explicit Similarity Ontology relation- a pubby6 front end) are also available. ship. Figure 3 shows how such relationships are as- serted. 9. Statistics

5. Provenance Overview statistics are provided in Table 2. The collection currently contains information concerning Alignments with external sources are represented as 3,981 artists, of which 1,168 have mappings to identi- similarities using the vocabulary provided by the Sim- fiers in the MusicBrainz collection. There are 100,413 performances, with 1,631,604 individual tracks. Of the ilarity Ontology [7] (see Figures 2 and 3). This pro- performances, 3,679 have venues mapped to last.fm vides an object that represents the association and thus venue identifiers, and 91,985 have locations identified allows us to attach additional metadata to those objects with GeoNames. There are 416 distinct last.fm venues asserting the provenance of the relationship. In the cur- and 3,578 geolocations linked from the dataset. The rent dataset, this includes a link to a URI describing dataset contains 17,555,791 triples. the method that was used to derive the alignment. We Interlinking within the datasets is primarily via code do not (as yet) provide explicit links to the that artists (performances by the same artist) and through was run in order to produce the alignments, but such an the mapped geolocations and venues. approach may be the topic of further work. Relation- ships from the W3C’s PROV-O ontology [10] are used 10. Discussion to assert additional information about the provenance of these mappings. We believe the dataset as published is a useful resource, providing access to underlying audio files 6. Process through a standardised query mechanism (SPARQL). The publication process has also been useful in high- The pipeline for initial data transformation was as lighting a number of issues in such an exercise, in- follows: cluding the representation and presentation of align- ments. The key value that the current linked dataset 1. Query Internet Archive for performances held in offers is the ability to link recordings of live perfor- etree. mances to artist and geographical information. Thus 2. Crawl and download XML metadata files (using wget ) for performances. 5http://creativecommons.org/publicdomain/ 3. Process XML files using bespoke scripts. zero/1.0/ 4. Load resulting data into triple store. 6http://www4.wiwiss.fu-berlin.de/pubby/ 5

Fig. 3. Location Similarities

Dataset base URI http://etree.linkedmusic.org SPARQL endpoint http://etree.linkedmusic.org/sparql Table 1 Access details

Type Total in Dataset LMA Mapped Entities Distinct External Entities Artists 3,981 MusicBrainz: 1,168 (29%) MusicBrainz: 1,168 Performances 100,413 —- Venues 100,413 last.fm: 3,679 (4%) GeoNames: 91,985 (92%) last.fm: 416 GeoNames: 3,578 Tracks 1,631,604 —- Triples 17,555,791 —- Table 2 Overview Statistics we can potentially compare live performances by in- Individual track matching. Alignment with MusicBrainz dividual artists across different geographical locations. is currently at the level of artists. MusicBrainz (and This could be in terms of metadata – does artist X other sources) also include track level metadata de- play the same setlist every night? Such a query could scribing particular songs or pieces. Providing a map- also potentially be answered by similar resources such ping from the individual (track) performances in etree as setlist.fm7. The etree collection, however, also of- to MusicBrainz would then provide access to a corpus fers the possibility of combining metadata queries with of versions of particular works. Representation of indi- computational analysis of the performance audio data vidual track matching requires the disambiguation be- – does artist X play the same songs at the same tempo tween a musical work, a performance of that work and every night, and does that change with geographical the audio encodings of that work – all of which can be location? represented in the Music Ontology. Such a matching process is likely to be challenging, however, in par- Further Work ticular due to the (i) the lack of standardisation in the description of track names in the etree source meta- This is a first step towards a rich dataset describing data; and (ii) the fact that songs played in live per- the resources in LMA and there are a number of ad- formance may not always be songs that feature in an ditional enhancements that could potentially improve artist’s recorded canon. the dataset and enhance its utility. Additional automated MusicBrainz artist matches. The current dataset uses a simple method to align 7http://www.setlist.fm/ artists to Musicbrainz. This has resulted in 29% of the 6 artists in the dataset being mapped. A non-exhaustive dx.doi.org/10.1016/S0306-4573(01)00033-4 examination of the data by eye suggests that one ex- doi:10.1016/S0306-4573(01)00033-4. planation for this is the number of “non main-stream” [4] D. De Roure, J.S. Downie, and I. Fujinaga. Salami: Struc- tural analysis of large amounts of music information. In UK artists represented in the data set. It may be the case, e-Science All Hands Meeting, 2010. however, that more sophisticated matching can provide [5] J. Stephen Downie. The Music Information Retrieval Eval- further linkage, albeit with reduced confidence. Inclu- uation eXchange (MIREX). D-Lib Magazine, 12(12), De- sion of manually curated mappings may also enhance cember 2006. URL: http://www.dlib.org/dlib/ linkage, albeit at an increased cost. december06/downie/12downie.html. [6] GeoNames [online]. URL: http://www.geonames. Crowdsourced corrections and mapping layers. En- org/ [cited 16th May, 2012]. abling a interface for community contribution to the [7] Kurt Jacobson, Yves Raimond, and Thomas Gangler.¨ Sim- alignment process. For example, allowing users to ilarity Ontology [online]. URL: http://purl.org/ ontology/similarity/ [cited 16th May, 2012]. identify and confirm the track-level mappings dis- [8] Kurt Jacobson, Yves Raimond, and Mark Sandler. An Ecosys- cussed above when they listen to or use the audio data. tem for Transparent Music Similarity in an Open World. In In- ternational Conference on Music Information Retrieval, 2009. Explicit characterisation of alignment processes. As [9] last.fm [online]. URL: http://www.last.fm/ [cited 16th discussed in Section 5, information is provided about May, 2012]. the processes used to align entities with external [10] Timothy Lebo, Satya Sahoo, and Deborah McGuinness. sources. This simply takes the form of a label iden- PROV-O: The PROV Ontology. W3C Working Draft, World tifying a method. Further machine readable informa- Wide Web Consortium, 2012. URL: http://www.w3. org/TR/prov-o/. tion describing the methods (and their execution) could [11] Cynthia C.S. Liem and Alan Hanjalic. Cover Song Retrieval: also be provided. A Comparative Study of System Component Choices. In 10th International Society for Music Information Retrieval Confer- 11. Acknowledgements ence (ISMIR 2009), 2009. [12] Internet Archive Live Music Archive [online]. URL: This work was undertaken during a visit to the Ox- http://archive.org/details/etree [cited 16th ford e-Research Centre (OeRC) by Sean Bechhofer. May, 2012]. [13] Alistair Miles and Sean Bechhofer. SKOS Simple Knowl- He would like to thank the OeRC for hosting him dur- edge Organization System Reference. W3C Recommendation, ing that time and the University of Manchester School World Wide Web Consortium, 2009. URL: http://www. of Computer Science for granting sabbatical leave. The w3.org/TR/skos-reference/. authors would also like to thank the Internet Archive [14] MusicBrainz [online]. URL: http://musicbrainz. for granting permission to use the etree collection. org/ [cited 16th May, 2012]. [15] K.R. Page, B. Fields, B.J. Nagel, G. O’Neill, D.C. De Roure, and T. Crawford. Semantics for music analysis through linked data: How country is my country? In IEEE Sixth International References Conference on e-Science, pages 41–48. IEEE, 2010. [16] Y. Raimond, S. Abdallah, M. Sandler, and F. Giasson. The mu- [1] Keith Alexander, Richard Cyganiak, Michael Hausenblas, and sic ontology. In Proceedings of the International Conference Jun Zhao. Describing Linked Datasets with the VoID Vocabu- on Music Information Retrieval (ISMIR), 2007. lary. W3C Interest Group Note, World Wide Web Consortium, [17] Yves Raimond and Samer Abdallah. The Event Ontology 2012. URL: http://www.w3.org/TR/void/. [online]. URL: http://motools.sourceforge.net/ [2] Christian Bizer, Tom Heath, and Tim Berners-Lee. Linked event/event.html [cited 16th May, 2012]. Data - The Story So Far. International Journal on Seman- [18] Yves Raimond and Fred´ erick´ Giasson. The Music Ontology tic Web and Information Systems, 5(3):1–22, 2009. http: [online]. URL: http://musicontology.com/ [cited //dx.doi.org/10.4018/jswis.2009081901 doi: 16th May, 2012]. 10.4018/jswis.2009081901. [19] Dave Simpson. Mark E Smith makes his entrance [online]. [3] Donald Byrd and Tim Crawford. Problems of music in- 2011. URL: http://www.guardian.co.uk/music/ formation retrieval in the real world. Inf. Process. Man- 2011/jun/14/mark-e-smith-entrance [cited 16th age., 38:249–272, March 2002. URL: http://dl.acm. May, 2012]. org/citation.cfm?id=637503.637509, http://