dataesr/scientific-paragraphs-categorization huggingface dataset:
Scores are sometimes used, but in a much looser way than in Western classical music, often in the form of lead sheets which only indicate the basic melody of the tune and its harmonic structure. For a comprehensive modelling of the jazz domain, several subdomains have to be considered presenting a variety of data modalities and formats. Jazz as a musical sound is the subject of analysis by jazz musicians and musicologists: they study such aspects as harmonic and melodic content, improvisation, musical interaction, and stylistic characteristics of movements and epochs in jazz  [9]. Jazz as a societal phenomenon has been studied by ethnomusicologists and jazz researchers, looking at jazz creators and consumers, its history and its role in society  [10, 11]. Given the similarities between jazz and other Western musical genres, existing ontologies of music, such as the Music Ontology  [12, 13]  and the MusicBrainz ontology, 1 are an obvious starting point for modelling the jazz domain.
Yet the centrality of improvisation, prevalence of multi-instrumentalists and changing lineups are important differences that require modelling by means of a dedicated ontology. These phenomena, while crucial for jazz, can be found in other genres and traditions: Western popular music knows multi-instrumentalists (Freddy Mercury who played guitar, piano and sang); pop bands often tour with guest musicians; improvised solos can be an important part of the musical product (Jimi Hendrix was most famous for his solos). Therefore a jazz ontology would not only borrow concepts from other genres but also contribute to other musical domains, where less salient musical practices may have escaped ontologists' attention. Few corpora exist for systematic study of jazz on a large scale. The Weimar Jazz Database  [14]  comprises 456 manually transcribed and richly annotated jazz solos: it contains solo transcriptions in a score-like machine-readable format.
LinkedJazz is a Linked Open Data project concerned with careers, lives, and relationships of jazz artists. It links all major open resources on jazz musicians and contains a curation tool to verify and reconcile the records  [15]. Additionally, it provides a public interface to assist with semi-automatic analyses of musicians' interviews and to add new relationships  [16]. Our recent project, Dig That Lick: Analysing Large-Scale Data for Melodic Patterns in Jazz Performances 2 (DTL) was a two-year endeavour within the fourth Trans-Atlantic Program Digging into Data Challenge. It addressed jazz on a larger scale, combining various aspects of jazz and data modalities in an interdisciplinary approach. The work was concerned with automatic analysis of melodic patterns (''licks'') in jazz improvisations, aiming to trace musical influence based on borrowing of licks  [17].
The analysis workflow included automatic melody extraction from 50K audio recordings  [18, 19] ; segmenting tracks into solo and other parts; and advanced pattern search on symbolic representations of solos  [20]. This paper describes modelling, collecting, integrating, correcting, enriching metadata and linking it to audio. In the next section, we outline previous work in semantic modelling and dataset development relevant to music (FRBR, Music Ontology) and in particular to jazz (Weimar Jazz Database, LinkedJazz). We lay out a methodological framework that guided us during planning, modelling and populating the Jazz Ontology in Section 3. Section 4 presents the semantic model of the Jazz Ontology: the basic model which represents and relates the discographic and the sessionographic information about jazz, as well as further additions modelling jazz-specific phenomena.
Section 5 describes the process of ontology population for three jazz datasets, including dataset merging and enrichment, while 1 https://wiki. musicbrainz. org/LinkedBrainz. 2 http://dig-that-lick. eecs. qmul. ac. uk. Section 6 discusses the assessment of the ontology in terms of formal requirements and in-use validation. We wrap up with a discussion and future work suggestions.
Related work and datasets One of the main standards for semantic modelling in cultural heritage is FRBR (Functional Requirements for Bibliographic Records). It is a conceptual model for describing entities and relationships in libraries, museums, and archives  [21]. FRBR was developed by the International Federation of Library Associations and Institutions (IFLA) and is widely used by cultural institutions around the world, in particular for electronic cataloguing of physical and digital objects. As a conceptual model, it is separate from the language, cataloguing standards, or the actual implementation system and provides the basis for interoperability between holdings, collections, and datasets  [22]. FRBR Group 1 defines four main entities to represent the products of intellectual or artistic endeavour: ''Work (a distinct intellectual or artistic creation) and expression (the intellectual or artistic realisation of a work) reflect intellectual or artistic content.
Manifestation (the physical embodiment of an expression of a work) and item (a single exemplar of a manifestation) reflect physical form''. Group 2 includes persons and corporate bodies responsible for the custodianship of Group 1 intellectual or artistic endeavours (e. g. , creators, consumers). Group 3 includes events and places  [23]  (Fig. 1 ). While too general in its pure form, FRBR is relevant for music  [24]  and in particular for jazz, and has been widely implemented or referred to in existing semantic models of music. It has been represented as an OWL ontology. 3 he Music Ontology (MO)  [12, 13]  is among the most comprehensive ontologies for the music domain, providing a vocabulary for publishing and linking a wide range of music-related data on the Web. 4  It extends the FRBR model and provides an eventbased conceptualisation of music creation, performance, production and consumption, including aspects such as discographic information, production processes as well as music-related events.
Moreover, it was found to generalise well to a number of non-Western musical traditions  [45, 46]. Our model builds strongly on the Music Ontology as its main reference, yet some aspects like frequently changing band lineups and the prevalence of multi-instrumentalists are specific for the genre and need more detailed modelling. Turning our attention to data sources relevant in the jazz domain, MusicBrainz is the largest crowd-sourced collection of music metadata online, mainly focused on discographic information about published CDs. It is widely used by applications and music consumers to automatically add metadata to their downloaded digital tracks 12,13,14   [48, 49]. The data is ingested according to detailed guidelines, and curated by volunteers, therefore it is quite consistent, though errors cannot be excluded.
An API is provided for data retrieval and sharing. The MusicBrainz data model has been expressed in OWL and the data is available in RDF format. MusicBrainz is an important Linked Open Data resource for us even though its focus on releases and CD metadata does not meet the core needs of our ontology and its applications. For interoperability, we kept our data model consistent with MusicBrainz where possible. Another important aspect of MusicBrainz is its fingerprinting algorithm,  15  which we use to identify track duplicates in our collection. The Weimar Jazz Database is a collection of manually transcribed and richly annotated jazz solos. It was produced as part of the Jazzomat Project at the University of Music ''Franz Liszt'' Weimar in Germany between 2012 and 2017 and has become an important corpus for systematic jazz research  [14].
The database comprises 456 instrumental jazz solos from 343 different recordings, and provides musical content annotations for meter, structural segmentation, measures, beats, chord labels, style, solo instrument and more. Discographic metadata is also included with the tracks. The Jazzomat data model has been developed with a detailed manual analysis of musical content as the main goal. Data is disseminated as an SQLite database; it has been represented in OWL as part of the JazzCats project  [50]. The data released as part of Dig That Lick (see Section 1) and used to evaluate the Jazz Ontology builds on the findings and experience from the Jazzomat project, taking its approach into the realm of big data (tens of thousands of tracks) by automating manual tasks such as solo transcription. LinkedJazz  16  is a research project at the Pratt Semantic Lab concerned with Linked Open Data, and in particular documents and data related to the personal and professional lives of jazz artists  [15, 16, 51].
The researchers crawled all the main Linked Open Data resources such as DBpedia,  17  Library of Congress authority files,  18  MusicBrainz,  19  Virtual International Authority File,  20  to collect and link all entries related to jazz artists. The outcomes of automatic discovery (around 9,000 artists) were curated by researchers and volunteers by means of a dedicated application. LinkedJazz researchers created a data model specifically describing jazz artists and relationships between them, implementing FOAF and the Music Ontology, and extending these further. We build upon the findings and the data model of this pioneering project, which enables us to easily link our ontology to existing Linked Open Data on jazz musicians. Only a small proportion of musicians in our data are currently referenced online -we hope to change that with our datasets.
The JazzCats 21   [53]  project successfully interlinked disparate jazz-related datasets by means of Semantic Technologies. The Fig. 3. LinkedJazz properties describing relationships between jazz artists  [52]. datasets in question were the Weimar Jazz Database, the Linked-Jazz repository and the Body&Soul dataset 22 which provides a discography for over 200 performances of ''Body and Soul'' recorded between 1930 and 2004  [54]. A follow-up project further added connections to two other musicological datasets describing concert life in London in the 18th and 19th centuries  [50, 55]. The goal was to connect existing datasets, not to model the domain of jazz with its specific characteristics which differentiate it from other musical domains. Whereas Body&Soul implemented the Music Ontology for its discographic relationships, LinkedJazz extended the Music Ontology properties with further relevant relationships among musicians (Fig.
3 ). In contrast, the Weimar Jazz Database did not implement existing ontologies or schemas and many classes were project specific. In this case, RDF was produced from an SQLite3 database via an automated process. Given the very rich solo annotations produced in the Jazzomat project, most of them would not normally be available for other data sources, or different types of content annotations might be used. In contrast to previous works with a stronger emphasis on interlinking data, we took a top-down approach, aiming to model the domain of jazz, first concentrating on elements which most jazz-related projects and datasets would have in common. We also focus on jazz-specific characteristics which have not been covered by more general models, such as fluidity of band line-ups and prevalence of multi-instrumentalists, as well as the centrality of live performance, soloing and improvisation.
Methodology and scope In this section, we describe the Jazz Ontology in accordance with the MIRO Guidelines for Ontology Reporting  [61]. We address the compulsory sections of the Guidelines keeping their numbering (such as A. 1: Ontology owner or E. 5: Entity naming convention), and, where relevant, optional descriptions (e. g. , D. 1: Knowledge acquisition method). Please refer to the MIRO Guidelines for the list of required and optional descriptions. 23  The Jazz Ontology (A. 1), owned and developed by the Dig That Lick consortium 24  allow anyone to adapt and build upon it as long as they credit its creators (A. 3). The ontology can be accessed at the permanent URL https://w3id. org/JazzOntology/ (A. 4), and it is represented in OWL (E. 1). The ontology is versioned and issues are tracked on GitHub: https://github.
com/ppquadrat/JazzOntology (A. 5, C. 3). All documentation, semantic model diagrams and RDF datasets based on the Jazz Ontology can be found in the Open Science Framework repository associated with Dig That Lick. 25 he semantic model and the ontologies presented in this paper were developed to enable systematic study of jazz, in particular queries across large, heterogeneous metadata repositories; to support metadata correction and disambiguation; to track workflows and provenance in manual and automatic content metadata creation; to support linking audio and other media to clean metadata (B. 1). The main aim of the Jazz Ontology is to provide interoperability between ontologies and datasets. It was not conceptualised as an explicit knowledge representation model. Related work is presented in Section 2 (B.
At more advanced stages of the research, focus groups with jazz experts were used to confirm the broader concepts (Section S2 of the Supplement), to discuss, test and improve the models illustrated by the entity-relationship diagrams presented in this paper (Section 4) to ensure the correct modelling of the datasets metadata (Sections 5. 1 and 5. 2), and to validate data inference, merging (Section 5. 4) and enrichment (Section 5. 6).
Evaluation (A. 6) Our approach to evaluating the Jazz Ontology is based on testing the ontology in practice through populating (Section 5) and merging (Section 5. 4) heterogeneous datasets based on the ontology (G. 3), and then releasing online tools that query the datasets for active use by the jazz community  [20]  (G. 5, see Fig. 1  in the Supplement). Additionally, SPARQL queries were implemented (Section S3 of the Supplement) based on the competency questions defined in Section S1 of the Supplement -these can be run when the ontology is modified to ensure consistency (G. 1). See Section 6 for more details.
Often the band is named after the leader -jazz databases often exploit this notion in their models too (see Section 5. 2). In other cases, bands do have names which are distinct from the leader's name -we therefore decided to model bands explicitly as mo:MusicGroup connected to its leader (a MusicArtist) using the has_leader property. In some circumstances however, particularly in jam sessions, a band may have no leader or may have more than one leader. This is permitted by the ontology as no strong commitments are made at the logical level (e. g. no cardinality constraints or complex class descriptions). Another important notion in jazz is the relationship between the Band and its lineup. The lineup changes frequently from session to session. This may be observed even between tracks; in free text annotations it is often represented through notes like: ''musician A on tracks 2 and 5'' or ''for this session musician B departed and musicians C and D joined''.
Medleys Since jazz is based strongly on improvisation, modifying and recombining existing tunes is a widespread practice, and as a result, medleys are frequent in our datasets. Even though performance of medleys can be represented implicitly using the event decomposition model, they are not directly modelled in the Music Ontology. A Medley is typically a set of tunes strung into one performance, without breaks between them. Often various musical devices are used to justify the transitions from one tune to the next. In the overwhelming majority of cases, the combination of the tunes is linear, e. g. , they follow one after another. We therefore turned to the Ordered List Ontology to model the order of the played tunes. 30 The complete medley is modelled as an OrderedList and the tunes within it are the objects in its slots.
The Performance is then related to both the list (the medley tune) and to each of the tunes (Fig. 6 ). Alongside medleys, there are many other ways how existing tunes can be recombined, transformed, or built upon in jazz. We therefore introduced further properties relating Performance and Tune, such as has_intro, changes_of, theme_of, vari-ations_on, citation_of, all of which we encountered in our datasets. 30 http://smiy. sourceforge. net/olo/spec/orderedlistontology. html.
Its three main classes are Entity, Activity, and Agent. In the case of metadata generation, a workflow is an Activity which generates entities such as a Transcription for a Sound or a Match for a pair of Licks. In the case of manual transcription, the transcriber is the Agent associated with the TranscriptionWorkflow; for automatically generated transcriptions, the workflow is associated with the algorithm, code, or application which was executed. Our datasets in the context of automatic metadata creation can be understood as a layered digital library  [43]  that comprises an audio collections layer, a metadata layer, and a computational 31 https://www. w3. org/TR/prov-o/. analysis layer which is described by workflows. The outcomes produced by workflows constitute the feature metadata layerthe input for the exploratory analysis, which can be carried out by other workflows or by human researchers.
Ontology population: RDF dataset content, integration and enrichment In this section we describe how we collected audio datasets (Jazz Encyclopedia, Illinois, Porkpie); how they were associated with sessionographic information; how they were recombined for the purposes of coverage and representation (100 Years of Jazz, In order to test the ontology and its support for semantic integration and interoperability, we assembled a large corpus of jazz recordings. The data comprises over 50K tracks, aiming to ensure that the collection was broadly representative, without obvious gaps or omissions. The collections that contributed to our audio corpus were: • the Jazz Encyclopedia -a collection of 500 CDs documenting jazz from its beginnings to the 1950s. • the Illinois collection -jazz CDs from the library of the University of Illinois Urbana-Champaign.
• the Porkpie collection -a set of audio CDs used in the JDISC project (but not directly related to the metadata collected in the JDISC project). The Illinois and Porkpie collections contained only basic discographic information attached to the CDs: album title, (album) artist name, track titles, track numbers, and (for Illinois) label and release number. For sessionography we turned to the most authoritative jazz metadata database used by jazz researchersthe Jazz Discography (see Section 5. 2). The metadata we collected and created can be found on the 'Dig That Lick' 32 'Metadata' subproject 33 on the Open Science framework.
The Jazz Encyclopedia ''The Encyclopedia of Jazz: The World's Greatest Jazz Collection'', 34 accompanied by extensive annotations, was released by Membran, a German music label group that specialises in releasing large CD sets. The collection is probably among the largest ever commercially released compilations. It consists of five parts: classic jazz, swing time, big bands, bebop and modern jazz, each comprising 100 compact discs. Altogether the dataset consists of 9,065 tracks, recordings of 6,255 distinct tunes performed by 898 bands between 1917 and 1958. A CD usually presents one band, or in some rare cases several bands from the same time, area or style. Some bands have more than one CD dedicated to them. The collection is accompanied by a metadata file in CSV format. It lists CDs with their order number and title, and for each 32 https://osf.
We provide a mapping to standard instrument names where possible -they are also included as an attribute to the instruments in the RDF repository alongside the original description. There are many exotic instruments in this dataset, from washboard to piccolo flute. Additionally descriptions of other sound events are included (e. g. , 'not audible' or 'occasional shouting') which makes it difficult to parse the instrument data. Performers or their instrument metadata may include a question mark. To capture this type of uncertainty, we implemented a confidence attribute for the performer class. We stopped short of devising a thesaurus of musical instruments, given the time constraints of the project. See  [37, 79]  for knowledge representation issues in musical instrument ontology design and automatic instrument classification methods.
Of all metadata, dates required the most effort to clean and process (see Section 4. 3 for examples of provided date strings). We developed a parser for approximate datespans capable of processing the dates in our datasets. Altogether we have been  able to process about 20,000 approximate dates with our parser. The code is available on GitHub. 36 Approximate dates are a frequent feature in a number of domains outside music, e. g. , history, archaeology and cultural heritage more generally. Also, in many cases, user generated metadata results in approximate or ambiguous dates. We anticipate that our tool will be useful in those domains. Overall, our Jazz Encyclopedia RDF repository 37 contains high quality, detailed data and is one of the very few machine-readable jazz metadata repositories of this size and quality.
The Jazz Discography The Jazz Discography 38 (JD) is the largest resource of information on jazz sessionography. It is available as a printed book and a CD-Rom, yet the most recent version is accessible as an online subscription. This resource is considered by jazz researchers and libraries to be the most complete and reliable source of sessionographic data. It covers all styles and epochs of jazz. As of October 2019 the online database included over 49K leaders, over 242K recording sessions, over 1. 3M musician entries, over 1. 5M tune entries and over 420K record releases. 39  The Jazz Discography is organised by Leaders and the concept of Band is otherwise absent (Fig. 12 ). Where a band name does not contain the leader's name, it is stored in the leader's 'first name' field. Otherwise band names are omitted.
We introduced both concepts, bands (as a class) and leaders (as a property). Information about sessions for each leader is presented as human-readable semi-structured text, For example, a list of musicians is given for the first session, and for the following sessions remarks indicate which musicians departed and which 36 https://github. com/ppquadrat/DigThatLick/blob/master/dateParser. py. 37 https://osf. io/8su3a/. 38 https://www. lordisco. com. 39 https://www. lordisco. com/tjdonline. html, last accessed 6/6/2020. new musicians joined. There are further remarks for single tune performances within sessions, indicating, e. g. , that a given musician only plays on track one. The necessity of such remarks clearly shows that the model adapted by the Jazz Discography is not ideal and needs refinement.
The 100 years of Jazz dataset The Jazz Encyclopedia (Section 5. 1) offered excellent and reliable metadata and good coverage of jazz styles and epochs from the beginnings of jazz up to the 1950s. To complement this audio dataset to cover the whole history of jazz, we chose audio tracks from our Illinois audio collection which we could match with sessionographic metadata from the Jazz Discography (Section 5. 2). The coverage of matching metadata between the Jazz Discography metadata and our audio CD corpus was surprisingly low (matching via label names resulted in 34% coverage). 3,368 tracks from the Illinois audio were chosen covering later decades of jazz starting from 1960. These tracks comprised the ILL dataset. The union of the two collections, the JE and the ILL datasets, consists of 12,433 tracks with audio recordings and corresponding bibliographic and sessionographic metadata, representing the 100 years of jazz history -we called this corpus the ''100 Years of Jazz'' dataset.