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International Journal on Digital Libraries
Journal Prestige (SJR): 0.441
Citation Impact (citeScore): 2
Number of Followers: 736  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1432-1300 - ISSN (Online) 1432-5012
Published by Springer-Verlag Homepage  [2352 journals]
  • Expressiveness and machine processability of Knowledge Organization
           Systems (KOS): an analysis of concepts and relations
    • Abstract: This study considers the expressiveness (that is, the expressive power or expressivity) of different types of Knowledge Organization Systems (KOS) and discusses its potential to be machine-processable in the context of the semantic web. For this purpose, the theoretical foundations of KOS are reviewed based on conceptualizations introduced by the Functional Requirements for Subject Authority Data (FRSAD) and the Simple Knowledge Organization System (SKOS); natural language processing techniques are also implemented. Applying a comparative analysis, the dataset comprises a thesaurus (Eurovoc), a subject headings system (LCSH) and a classification scheme (DDC). These are compared with an ontology (CIDOC-CRM) by focusing on how they define and handle concepts and relations. It was observed that LCSH and DDC focus on the formalism of character strings (nomens) rather than on the modelling of semantics; their definition of what constitutes a concept is quite fuzzy, and they comprise a large number of complex concepts. By contrast, thesauri have a coherent definition of what constitutes a concept, and apply a systematic approach to the modelling of relations. Ontologies explicitly define diverse types of relations, and are by their nature machine-processable. The paper concludes that the potential of both the expressiveness and machine processability of each KOS is extensively regulated by its structural rules. It is harder to represent subject headings and classification schemes as semantic networks with nodes and arcs, while thesauri are more suitable for such a representation. In addition, a paradigm shift is revealed which focuses on the modelling of relations between concepts, rather than the concepts themselves.
      PubDate: 2019-04-12
  • Encoding music performance data in Humdrum and MEI
    • Abstract: This paper proposes extensions to two existing music encoding formats, Humdrum and Music Encoding Initiative (MEI), in order to facilitate linking music performance data with corresponding score information. We began by surveying music scholars about their needs for encoding timing, loudness, pitch, and timbral performance data. We used the results of this survey to design and implement new spines in Humdrum syntax to encode summary descriptors at note, beat, and measure levels and new attributes in the MEI format to encode both note-wise summaries and continuous data. These extensions allow for multiple performances of the same piece to be directly compared with one another, facilitating both humanistic and computational study of recorded musical performances.
      PubDate: 2019-03-01
  • Crowdsourcing Linked Data on listening experiences through reuse and
           enhancement of library data
    • Abstract: Research has approached the practice of musical reception in a multitude of ways, such as the analysis of professional critique, sales figures and psychological processes activated by the act of listening. Studies in the Humanities, on the other hand, have been hindered by the lack of structured evidence of actual experiences of listening as reported by the listeners themselves, a concern that was voiced since the early Web era. It was however assumed that such evidence existed, albeit in pure textual form, but could not be leveraged until it was digitised and aggregated. The Listening Experience Database (LED) responds to this research need by providing a centralised hub for evidence of listening in the literature. Not only does LED support search and reuse across nearly 10,000 records, but it also provides machine-readable structured data of the knowledge around the contexts of listening. To take advantage of the mass of formal knowledge that already exists on the Web concerning these contexts, the entire framework adopts Linked Data principles and technologies. This also allows LED to directly reuse open data from the British Library for the source documentation that is already published. Reused data are re-published as open data with enhancements obtained by expanding over the model of the original data, such as the partitioning of published books and collections into individual stand-alone documents. The database was populated through crowdsourcing and seamlessly incorporates data reuse from the very early data entry phases. As the sources of the evidence often contain vague, fragmentary of uncertain information, facilities were put in place to generate structured data out of such fuzziness. Alongside elaborating on these functionalities, this article provides insights into the most recent features of the latest instalment of the dataset and portal, such as the interlinking with the MusicBrainz database, the relaxation of geographical input constraints through text mining, and the plotting of key locations in an interactive geographical browser.
      PubDate: 2019-03-01
  • Applications of RISM data in digital libraries and digital musicology
    • Abstract: Information about manuscripts and printed music indexed in RISM (Répertoire International des Sources Musicales), a large, international project that records and describes musical sources, was for decades available solely through book publications, CD-ROMs, or subscription services. Recent initiatives to make the data available on a wider scale have resulted in, most significantly, a freely accessible online database and the availability of its data as open data and linked open data. Previously, the task of increasing the amount of data was primarily carried out by RISM national groups and the Zentralredaktion (Central Office). The current opportunities available by linking to other freely accessible databases and importing data from other resources open new perspectives and prospects. This paper describes the RISM data and their applications for digital libraries and digital musicological projects. We discuss the possibilities and challenges in making available a large and growing quantity of data and how the data have been utilized in external library and musicological projects. Interactive functions in the RISM OPAC are planned for the future, as is closer collaboration with the projects that use RISM data. Ultimately, RISM would like to arrange a “take and give” system in which the RISM data are used in external projects, enhanced by the project participants, and then delivered back to the RISM Zentralredaktion.
      PubDate: 2019-03-01
  • Documenting a song culture: the Dutch Song Database as a resource for
           musicological research
    • Abstract: The Dutch Song Database is a digital repository documenting Dutch song culture in past and present. It contains more than 173 thousand references to song occurrences in the Dutch and Flemish language, from the Middle Ages up to the present, as well as over 18 thousand descriptions of song sources, such as song books, manuscripts and field recordings, all adhering to high quality standards. In this paper, we present the history and functionality of the database, and we demonstrate how the Dutch Song Database facilitates and enables musicological research by presenting its contents and search functionalities in a number of exemplary cases. We discuss difficulties and impediments that were encountered during the development of the database, and we sketch a future prospect for further development in the European context.
      PubDate: 2019-03-01
  • On providing semantic alignment and unified access to music library
    • Abstract: A variety of digital data sources—including institutional and formal digital libraries, crowd-sourced community resources, and data feeds provided by media organisations such as the BBC—expose information of musicological interest, describing works, composers, performers, and wider historical and cultural contexts. Aggregated access across such datasets is desirable as these sources provide complementary information on shared real-world entities. Where datasets do not share identifiers, an alignment process is required, but this process is fraught with ambiguity and difficult to automate, whereas manual alignment may be time-consuming and error-prone. We address this problem through the application of a Linked Data model and framework to assist domain experts in this process. Candidate alignment suggestions are generated automatically based on textual and on contextual similarity. The latter is determined according to user-configurable weighted graph traversals. Match decisions confirming or disputing the candidate suggestions are obtained in conjunction with user insight and expertise. These decisions are integrated into the knowledge base, enabling further iterative alignment, and simplifying the creation of unified viewing interfaces. Provenance of the musicologist’s judgement is captured and published, supporting scholarly discourse and counter-proposals. We present our implementation and evaluation of this framework, conducting a user study with eight musicologists. We further demonstrate the value of our approach through a case study providing aligned access to catalogue metadata and digitised score images from the British Library and other sources, and broadcast data from the BBC Radio 3 Early Music Show.
      PubDate: 2019-03-01
  • Heuristic and supervised approaches to handwritten annotation extraction
           for musical score images
    • Abstract: Performers’ copies of musical scores are typically rich in handwritten annotations, which capture historical and institutional performance practices. The development of interactive interfaces to explore digital archives of these scores and the systematic investigation of their meaning and function will be facilitated by the automatic extraction of handwritten score annotations. We present several approaches to the extraction of handwritten annotations of arbitrary content from digitized images of musical scores. First, we show promising results in certain contexts when using simple unsupervised clustering techniques to identify handwritten annotations in conductors’ scores. Next, we compare annotated scores to unannotated copies and use a printed sheet music comparison tool, Aruspix, to recover handwritten annotations as additions to the clean copy. Using both of these techniques in a combined annotation pipeline qualitatively improves the recovery of handwritten annotations. Recent work has shown the effectiveness of reframing classical optical musical recognition tasks as supervised machine learning classification tasks. In the same spirit, we pose the problem of handwritten annotation extraction as a supervised pixel classification task, where the feature space for the learning task is derived from the intensities of neighboring pixels. After an initial investment of time required to develop dependable training data, this approach can reliably extract annotations for entire volumes of score images without further supervision. These techniques are demonstrated using a sample of orchestral scores annotated by professional conductors of the New York Philharmonic Orchestra. Handwritten annotation extraction in musical scores has applications to the systematic investigation of score annotation practices by performers, annotator attribution, and to the interactive presentation of annotated scores, which we briefly discuss.
      PubDate: 2019-03-01
  • Guest editors’ introduction to the special issue on digital
           libraries for musicology
    • Authors: Kevin R. Page; J. Stephen Downie
      PubDate: 2019-02-23
      DOI: 10.1007/s00799-019-00268-1
  • Curating and annotating a collection of traditional Irish flute recordings
           to facilitate stylistic analysis
    • Authors: Münevver Köküer; Islah Ali-MacLachlan; Daithí Kearney; Peter Jančovič
      Abstract: This paper presents the curation and annotation of a collection of traditional Irish flute recordings to facilitate the analysis of stylistic characteristics. We introduce the structure of Irish tunes, types of tunes and the ornamentation, which is a decisive stylistic determinant in Irish traditional music. We identify seminal recordings of prominent flute players and provide information related to players and their style and geographical context. We describe the process of manual annotation of the audio data. The annotations consist of the onsets of notes, note frequency and identity of notes and ornaments. We also present initial stylistic analysis of individual players in terms of ornamentation and phrasing and provide a variety of statistics for the data. The ability to accurately represent and analyse stylistic features such as ornaments allow for the development of discourse related to several key ethnomusicological questions surrounding music making, musical heritage and cultural change.
      PubDate: 2019-02-23
      DOI: 10.1007/s00799-019-00267-2
  • Introduction to the focused issue on the 20th International Conference on
           Theory and Practice of Digital Libraries (TPDL 2016)
    • Authors: Norbert Fuhr; László Kovács; Thomas Risse; Wolfgang Nejdl
      PubDate: 2019-02-01
      DOI: 10.1007/s00799-019-00265-4
  • A Wikidata-based tool for building and visualising narratives
    • Authors: Daniele Metilli; Valentina Bartalesi; Carlo Meghini
      Abstract: In this paper we present a semi-automatic tool for constructing and visualising narratives, intended as networks of events related to each other by semantic relations. The tool obeys an ontology for narratives that we developed. It retrieves and assigns internationalised resource identifiers to the instances of the classes of the ontology using Wikidata as an external knowledge base and also facilitates the construction and contextualisation of events, and their linking to form the narratives. The knowledge collected by the tool is automatically saved as an Web ontology language graph. The tool also allows the visualisation of the knowledge included in the graph in simple formats like tables, network graphs and timelines. We have carried out an initial qualitative evaluation of the tool. As case study, an historian from the University of Pisa has used the tool to build the narrative of Dante Alighieri’s life. The evaluation has regarded the effectiveness of the tool and the satisfaction of the users’ requirements.
      PubDate: 2019-01-30
      DOI: 10.1007/s00799-019-00266-3
  • An MEI-based standard encoding for hierarchical music analyses
    • Authors: David Rizo; Alan Marsden
      Abstract: We propose a standard representation for hierarchical musical analyses as an extension to the Music Encoding Initiative (MEI) representation for music. Analyses of music need to be represented in digital form for the same reasons as music: preservation, sharing of data, data linking, and digital processing. Systems exist for representing sequential information, but many music analyses are hierarchical, whether represented explicitly in trees or graphs or not. Features of MEI allow the representation of an analysis to be directly associated with the elements of the music analyzed. MEI’s basis in TEI (Text Encoding Initiative), allows us to design a scheme which reuses some of the elements of TEI for the representation of trees and graphs. In order to capture both the information specific to a type of music analysis and the underlying form of an analysis as a tree or graph, we propose related “semantic” encodings, which capture the detailed information, and generic “non-semantic” encodings which expose the tree or graph structure. We illustrate this with examples of representations of a range of different kinds of analysis.
      PubDate: 2018-12-08
      DOI: 10.1007/s00799-018-0262-x
  • A framework for modelling and visualizing the US Constitutional Convention
           of 1787
    • Authors: Nicholas Cole; Alfie Abdul-Rahman; Grace Mallon
      Abstract: This paper describes a new approach to the presentation of records relating to formal negotiations and the texts that they create. It describes the architecture of a model, platform, and web interface ( that can be used by domain experts to convert the records typical of formal negotiations into a model of decision-making (with minimal training). This model has implications for both research and teaching, by allowing for better qualitative and quantitative analysis of negotiations. The platform emphasizes the reconstruction as closely as possible of the context within which proposals and decisions are made. The usability and benefits of a generic platform are illustrated by a presentation of the records relating to the 1787 Constitutional Convention that wrote the Constitution of the USA.
      PubDate: 2018-11-26
      DOI: 10.1007/s00799-018-0263-9
  • Recent applications of Knowledge Organization Systems: introduction to a
           special issue
    • Authors: Koraljka Golub; Rudi Schmiede; Douglas Tudhope
      PubDate: 2018-11-21
      DOI: 10.1007/s00799-018-0264-8
  • An empirically validated, onomasiologically structured, and linguistically
           motivated online terminology
    • Authors: Karolina Suchowolec; Christian Lang; Roman Schneider
      Abstract: Terminological resources play a central role in the organization and retrieval of scientific texts. Both simple keyword lists and advanced modelings of relationships between terminological concepts can make a most valuable contribution to the analysis, classification, and finding of appropriate digital documents, either on the web or within local repositories. This seems especially true for long-established scientific fields with elusive theoretical and historical branches, where the use of terminology within documents from different origins is often far from being consistent. In this paper, we report on the progress of a linguistically motivated project on the onomasiological re-modeling of the terminological resources for the grammatical information system grammis. We present the design principles and the results of their application. In particular, we focus on new features for the authoring backend and discuss how these innovations help to evaluate existing, loosely structured terminological content, as well as to efficiently deal with automatic term extraction. Furthermore, we introduce a transformation to a future SKOS representation. We conclude with a positioning of our resources with regard to the Knowledge Organization discourse and discuss how a highly complex information environment like grammis benefits from the re-designed terminological KOS.
      PubDate: 2018-11-17
      DOI: 10.1007/s00799-018-0254-x
  • A pragmatic approach to hierarchical categorization of research expertise
           in the presence of scarce information
    • Authors: Gustavo Oliveira de Siqueira; Sérgio Canuto; Marcos André Gonçalves; Alberto H. F. Laender
      Abstract: Throughout the history of science, different knowledge areas have collaborated to overcome major research challenges. The task of associating a researcher with such areas makes a series of tasks feasible such as the organization of digital repositories, expertise recommendation and the formation of research groups for complex problems. In this article, we propose a simple yet effective automatic classification model that is capable of categorizing research expertise according to a knowledge area classification scheme. Our proposal relies on discriminatory evidence provided by the title of academic works, which is the minimum information capable of relating a researcher to its knowledge area. Our experiments show that using supervised machine learning methods trained with manually labeled information, it is possible to produce effective classification models.
      PubDate: 2018-11-16
      DOI: 10.1007/s00799-018-0260-z
  • Automated identification of media bias in news articles: an
           interdisciplinary literature review
    • Authors: Felix Hamborg; Karsten Donnay; Bela Gipp
      Abstract: Media bias, i.e., slanted news coverage, can strongly impact the public perception of the reported topics. In the social sciences, research over the past decades has developed comprehensive models to describe media bias and effective, yet often manual and thus cumbersome, methods for analysis. In contrast, in computer science fast, automated, and scalable methods are available, but few approaches systematically analyze media bias. The models used to analyze media bias in computer science tend to be simpler compared to models established in the social sciences, and do not necessarily address the most pressing substantial questions, despite technically superior approaches. Computer science research on media bias thus stands to profit from a closer integration of models for the study of media bias developed in the social sciences with automated methods from computer science. This article first establishes a shared conceptual understanding by mapping the state of the art from the social sciences to a framework, which can be targeted by approaches from computer science. Next, we investigate different forms of media bias and review how each form is analyzed in the social sciences. For each form, we then discuss methods from computer science suitable to (semi-)automate the corresponding analysis. Our review suggests that suitable, automated methods from computer science, primarily in the realm of natural language processing, are already available for each of the discussed forms of media bias, opening multiple directions for promising further research in computer science in this area.
      PubDate: 2018-11-16
      DOI: 10.1007/s00799-018-0261-y
  • Anatomy of scholarly information behavior patterns in the wake of academic
           social media platforms
    • Authors: Hamed Alhoori; Mohammed Samaka; Richard Furuta; Edward A. Fox
      Abstract: As more scholarly content is born digital or converted to a digital format, digital libraries are becoming increasingly vital to researchers seeking to leverage scholarly big data for scientific discovery. Although scholarly products are available in abundance—especially in environments created by the advent of social networking services—little is known about international scholarly information needs, information-seeking behavior, or information use. The purpose of this paper is to address these gaps via an in-depth analysis of the information needs and information-seeking behavior of researchers, both students and faculty, at two universities, one in the USA and the other in Qatar. Based on this analysis, the study identifies and describes new behavior patterns on the part of researchers as they engage in the information-seeking process. The analysis reveals that the use of academic social networks has notable effects on various scholarly activities. Further, this study identifies differences between students and faculty members in regard to their use of academic social networks, and it identifies differences between researchers according to discipline. Although the researchers who participated in the present study represent a range of disciplinary and cultural backgrounds, the study reports a number of similarities in terms of the researchers’ scholarly activities.
      PubDate: 2018-11-03
      DOI: 10.1007/s00799-018-0255-9
  • Image libraries and their scholarly use in the field of art and
           architectural history
    • Authors: Sander Münster; Christina Kamposiori; Kristina Friedrichs; Cindy Kröber
      Abstract: The use of image libraries in the field of art and architectural history has been the subject of numerous research studies over the years. However, since previous investigations have focused, primarily, either on user behavior or reviewed repositories, our aim is to bring together both approaches. Against this background, this paper identifies the main characteristics of research and information behavior of art and architectural history scholars and students in the UK and Germany and presents a structured overview of currently available scholarly image libraries. Finally, the implications for a user-centered design of information resources and, in particular, image libraries are provided.
      PubDate: 2018-07-07
      DOI: 10.1007/s00799-018-0250-1
  • Open information extraction as an intermediate semantic structure for
           Persian text summarization
    • Authors: Mahmoud Rahat; Alireza Talebpour
      Abstract: Semantic applications typically exploit structures such as dependency parse trees, phrase-chunking, semantic role labeling or open information extraction. In this paper, we introduce a novel application of Open IE as an intermediate layer for text summarization. Text summarization is an important method for providing relevant information in large digital libraries. Open IE is referred to the process of extracting machine-understandable structural propositions from text. We use these propositions as a building block to shorten the sentence and generate a summary of the text. The proposed system offers a new form of summarization that is able to break the structure of the sentence and extract the most significant sub-sentential elements. Other advantages include the ability to identify and eliminate less important sections of the sentence (such as adverbs, adjectives, appositions or dependent clauses), or duplicate pieces of sentences which in turn opens up the space for entering more sentences in the summary to enhance the coverage and coherency of it. The proposed system is localized for Persian language; however, it can be adopted to other languages. Experiments performed on a standard data set “Pasokh” with a standard comparison tool showed promising results for the proposed approach. We used summaries produced by the system in a real-world application in the virtual library of Shahid Beheshti University and received good feedbacks from users.
      PubDate: 2018-06-28
      DOI: 10.1007/s00799-018-0244-z
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