"Seed+Expand": A validated methodology for creating high quality publication oeuvres of individual researchers

The study of science at the individual micro-level frequently requires the disambiguation of author names. The creation of author's publication oeuvres involves matching the list of unique author names to names used in publication databases. Despite recent progress in the development of unique author identifiers, e.g., ORCID, VIVO, or DAI, author disambiguation remains a key problem when it comes to large-scale bibliometric analysis using data from multiple databases. This study introduces and validates a new methodology called seed+expand for semi-automatic bibliographic data collection for a given set of individual authors. Specifically, we identify the oeuvre of a set of Dutch full professors during the period 1980-2011. In particular, we combine author records from the National Research Information System (NARCIS) with publication records from the Web of Science. Starting with an initial list of 8,378 names, we identify "seed publications" for each author using five different approaches. Subsequently, we "expand" the set of publication in three different approaches. The different approaches are compared and resulting oeuvres are evaluated on precision and recall using a "gold standard" dataset of authors for which verified publications in the period 2001-2010 are available.Comment: Paper accepted for the ISSI 2013, small changes in the text due to referee comments, one figure added (Fig 3

BiblioDAP'21: The 1st Workshop on Bibliographic Data Analysis and Processing

Automatic processing of bibliographic data becomes very important in digital libraries, data science and machine learning due to its importance in keeping pace with the significant increase of published papers every year from one side and to the inherent challenges from the other side. This processing has several aspects including but not limited to I) Automatic extraction of references from PDF documents, II) Building an accurate citation graph, III) Author name disambiguation, etc. Bibliographic data is heterogeneous by nature and occurs in both structured (e.g. citation graph) and unstructured (e.g. publications) formats. Therefore, it requires data science and machine learning techniques to be processed and analysed. Here we introduce BiblioDAP'21: The 1st Workshop on Bibliographic Data Analysis and Processing

Distributed human computation framework for linked data co-reference resolution

Distributed Human Computation (DHC) is a technique used to solve computational problems by incorporating the collaborative effort of a large number of humans. It is also a solution to AI-complete problems such as natural language processing. The Semantic Web with its root in AI is envisioned to be a decentralised world-wide information space for sharing machine-readable data with minimal integration costs. There are many research problems in the Semantic Web that are considered as AI-complete problems. An example is co-reference resolution, which involves determining whether different URIs refer to the same entity. This is considered to be a significant hurdle to overcome in the realisation of large-scale Semantic Web applications. In this paper, we propose a framework for building a DHC system on top of the Linked Data Cloud to solve various computational problems. To demonstrate the concept, we are focusing on handling the co-reference resolution in the Semantic Web when integrating distributed datasets. The traditional way to solve this problem is to design machine-learning algorithms. However, they are often computationally expensive, error-prone and do not scale. We designed a DHC system named iamResearcher, which solves the scientific publication author identity co-reference problem when integrating distributed bibliographic datasets. In our system, we aggregated 6 million bibliographic data from various publication repositories. Users can sign up to the system to audit and align their own publications, thus solving the co-reference problem in a distributed manner. The aggregated results are published to the Linked Data Cloud

I Cannot Tell What the Dickens His Name Is : Name Disambiguation in Institutional Repositories

INTRODUCTION Authors who publish under more than one form of their name, multiple authors with the same name, and incomplete author information can all create challenges for repository staff when entering metadata. Unless properly addressed, these variations and duplications can result in search and retrieval errors for users. Name disambiguation, the process of identifying, merging, and making names accessible in one standard form, is a vital process repository staff should incorporate into their workflow to address these issues. DESCRIPTION OF PROGRAM Staff working with ScholarWorks, Boise State’s institutional repository, are exploring the use of disambiguation tools to solve the issue of name duplication. Systems explored include ORCID, ResearcherID, Scopus, Google Scholar Citations, Names Project, and the Digital Commons’ Author Merge Tool. NEXT STEPS Based on this initial assessment, ScholarWorks staff will continue to use the Author Merge Tool on a regular basis and explore ways to document and retain information discovered during the analysis phase. Additionally, they will continue to experiment with emerging name authority tools, such as ORCID. Finally, metadata specialists are encouraged to advocate for international standards that will provide prescribed rules for how metadata is entered into a repository system

Bayesian Non-Exhaustive Classification A Case Study: Online Name Disambiguation using Temporal Record Streams

The name entity disambiguation task aims to partition the records of multiple real-life persons so that each partition contains records pertaining to a unique person. Most of the existing solutions for this task operate in a batch mode, where all records to be disambiguated are initially available to the algorithm. However, more realistic settings require that the name disambiguation task be performed in an online fashion, in addition to, being able to identify records of new ambiguous entities having no preexisting records. In this work, we propose a Bayesian non-exhaustive classification framework for solving online name disambiguation task. Our proposed method uses a Dirichlet process prior with a Normal * Normal * Inverse Wishart data model which enables identification of new ambiguous entities who have no records in the training data. For online classification, we use one sweep Gibbs sampler which is very efficient and effective. As a case study we consider bibliographic data in a temporal stream format and disambiguate authors by partitioning their papers into homogeneous groups. Our experimental results demonstrate that the proposed method is better than existing methods for performing online name disambiguation task.Comment: to appear in CIKM 201

Content Enrichment of Digital Libraries: Methods, Technologies and Implementations

Parallel to the establishment of the concept of a "digital library", there have been rapid developments in the fields of semantic technologies, information retrieval and artificial intelligence. The idea is to use make use of these three fields to crosslink bibliographic data, i.e., library content, and to enrich it "intelligently" with additional, especially non-library, information. By linking the contents of a library, it is possible to offer users access to semantically similar contents of different digital libraries. For instance, a list of semantically similar publications from completely different subject areas and from different digital libraries can be made accessible. In addition, the user is able to see a wider profile about authors, enriched with information such as biographical details, name alternatives, images, job titles, institute affiliations, etc. This information comes from a wide variety of sources, most of which are not library sources. In order to make such scenarios a reality, this dissertation follows two approaches. The first approach is about crosslinking digital library content in order to offer semantically similar publications based on additional information for a publication. Hence, this approach uses publication-related metadata as a basis. The aligned terms between linked open data repositories/thesauri are considered as an important starting point by considering narrower, broader and related concepts through semantic data models such as SKOS. Information retrieval methods are applied to identify publications with high semantic similarity. For this purpose, approaches of vector space models and "word embedding" are applied and analyzed comparatively. The analyses are performed in digital libraries with different thematic focuses (e.g. economy and agriculture). Using machine learning techniques, metadata is enriched, e.g. with synonyms for content keywords, in order to further improve similarity calculations. To ensure quality, the proposed approaches will be analyzed comparatively with different metadata sets, which will be assessed by experts. Through the combination of different information retrieval methods, the quality of the results can be further improved. This is especially true when user interactions offer possibilities for adjusting the search properties. In the second approach, which this dissertation pursues, author-related data are harvested in order to generate a comprehensive author profile for a digital library. For this purpose, non-library sources, such as linked data repositories (e.g. WIKIDATA) and library sources, such as authority data, are used. If such different sources are used, the disambiguation of author names via the use of already existing persistent identifiers becomes necessary. To this end, we offer an algorithmic approach to disambiguate authors, which makes use of authority data such as the Virtual International Authority File (VIAF). Referring to computer sciences, the methodological value of this dissertation lies in the combination of semantic technologies with methods of information retrieval and artificial intelligence to increase the interoperability between digital libraries and between libraries with non-library sources. By positioning this dissertation as an application-oriented contribution to improve the interoperability, two major contributions are made in the context of digital libraries: (1) The retrieval of information from different Digital Libraries can be made possible via a single access. (2) Existing information about authors is collected from different sources and aggregated into one author profile.Parallel zur Etablierung des Konzepts einer „Digitalen Bibliothek“ gab es rasante Weiterentwicklungen in den Bereichen semantischer Technologien, Information Retrieval und künstliche Intelligenz. Die Idee ist es, mit ihrer Hilfe bibliographische Daten, also Inhalte von Bibliotheken, miteinander zu vernetzen und „intelligent“ mit zusätzlichen, insbesondere nicht-bibliothekarischen Informationen anzureichern. Durch die Verknüpfung von Inhalten einer Bibliothek wird es möglich, einen Zugang für Benutzer*innen anzubieten, über den semantisch ähnliche Inhalte unterschiedlicher Digitaler Bibliotheken zugänglich werden. Beispielsweise können hierüber ausgehend von einer bestimmten Publikation eine Liste semantisch ähnlicher Publikationen ggf. aus völlig unterschiedlichen Themenfeldern und aus verschiedenen digitalen Bibliotheken zugänglich gemacht werden. Darüber hinaus können sich Nutzer*innen ein breiteres Autoren-Profil anzeigen lassen, das mit Informationen wie biographischen Angaben, Namensalternativen, Bildern, Berufsbezeichnung, Instituts-Zugehörigkeiten usw. angereichert ist. Diese Informationen kommen aus unterschiedlichsten und in der Regel nicht-bibliothekarischen Quellen. Um derartige Szenarien Realität werden zu lassen, verfolgt diese Dissertation zwei Ansätze. Der erste Ansatz befasst sich mit der Vernetzung von Inhalten Digitaler Bibliotheken, um auf Basis zusätzlicher Informationen für eine Publikation semantisch ähnliche Publikationen anzubieten. Dieser Ansatz verwendet publikationsbezogene Metadaten als Grundlage. Die verknüpften Begriffe zwischen verlinkten offenen Datenrepositorien/Thesauri werden als wichtiger Angelpunkt betrachtet, indem Unterbegriffe, Oberbegriffe und verwandten Konzepte über semantische Datenmodelle, wie SKOS, berücksichtigt werden. Methoden des Information Retrieval werden angewandt, um v.a. Publikationen mit hoher semantischer Verwandtschaft zu identifizieren. Zu diesem Zweck werden Ansätze des Vektorraummodells und des „Word Embedding“ eingesetzt und vergleichend analysiert. Die Analysen werden in Digitalen Bibliotheken mit unterschiedlichen thematischen Schwerpunkten (z.B. Wirtschaft und Landwirtschaft) durchgeführt. Durch Techniken des maschinellen Lernens werden hierfür Metadaten angereichert, z.B. mit Synonymen für inhaltliche Schlagwörter, um so Ähnlichkeitsberechnungen weiter zu verbessern. Zur Sicherstellung der Qualität werden die beiden Ansätze mit verschiedenen Metadatensätzen vergleichend analysiert wobei die Beurteilung durch Expert*innen erfolgt. Durch die Verknüpfung verschiedener Methoden des Information Retrieval kann die Qualität der Ergebnisse weiter verbessert werden. Dies trifft insbesondere auch dann zu wenn Benutzerinteraktion Möglichkeiten zur Anpassung der Sucheigenschaften bieten. Im zweiten Ansatz, den diese Dissertation verfolgt, werden autorenbezogene Daten gesammelt, verbunden mit dem Ziel, ein umfassendes Autorenprofil für eine Digitale Bibliothek zu generieren. Für diesen Zweck kommen sowohl nicht-bibliothekarische Quellen, wie Linked Data-Repositorien (z.B. WIKIDATA) und als auch bibliothekarische Quellen, wie Normdatensysteme, zum Einsatz. Wenn solch unterschiedliche Quellen genutzt werden, wird die Disambiguierung von Autorennamen über die Nutzung bereits vorhandener persistenter Identifikatoren erforderlich. Hierfür bietet sich ein algorithmischer Ansatz für die Disambiguierung von Autoren an, der Normdaten, wie die des Virtual International Authority File (VIAF) nachnutzt. Mit Bezug zur Informatik liegt der methodische Wert dieser Dissertation in der Kombination von semantischen Technologien mit Verfahren des Information Retrievals und der künstlichen Intelligenz zur Erhöhung von Interoperabilität zwischen Digitalen Bibliotheken und zwischen Bibliotheken und nicht-bibliothekarischen Quellen. Mit der Positionierung dieser Dissertation als anwendungsorientierter Beitrag zur Verbesserung von Interoperabilität werden zwei wesentliche Beiträge im Kontext Digitaler Bibliotheken geleistet: (1) Die Recherche nach Informationen aus unterschiedlichen Digitalen Bibliotheken kann über einen Zugang ermöglicht werden. (2) Vorhandene Informationen über Autor*innen werden aus unterschiedlichsten Quellen eingesammelt und zu einem Autorenprofil aggregiert

Expanding the Metadata Librarian Horizon: Reflections on the Metadata Practices in the Web and Digital Repositories

The growing amount of digital resources on the web and in libraries have been rapidly changing the ways data is organized, shared and discovered, and the Metadata Librarians’ roles and practices have been constantly reshaped under this larger environment. In light of related literature and the author’s experiences in web archiving and working with several digital repositories including CONTENTdm, Islandora, Digital Commons, DSpace and Omeka, this presentation will discuss the ongoing changes in metadata practices in various areas, such as identity management, authority control, repository design and capability, metadata, its presentation and discovery, and linked data. It will also reflect on the metadata practices in the library community and the web practices in classifying and discovering data, and explore the web’s impact on library cataloging and the metadata profession. The changing environment and practices call for the Metadata Librarian’s flexibility in working with different digital library systems and personnel from other departments, an open mindset, and new understandings and skill sets related to vocabulary management, document encoding, data processing, and innovative ways of or semi-auto cataloging and classification

The lost academic home: institutional affiliation links in Google Scholar Citations

This article is (c) Emerald Group Publishing and permission has been granted for this version to appear here (please insert the web address here). Emerald does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Emerald Group Publishing Limited[EN] Purpose - Google Scholar Citations (GSC) provides an institutional affiliation link which groups together authors who belong to the same institution. The purpose of this paper is to ascertain whether this feature is able to identify and normalize all the institutions entered by the authors, and whether it is able to assign all researchers to their own institution correctly. Design/methodology/approach - Systematic queries to GSC's internal search box were performed under two different forms (institution name and institutional e-mail web domain) in September 2015. The whole Spanish academic system (82 institutions) was used as a test. Additionally, specific searches to companies (Google) and world-class universities were performed to identify and classify potential errors in the functioning of the feature. Findings - Although the affiliation tool works well for most institutions, it is unable to detect all existing institutions in the database, and it is not always able to create a unique standardized entry for each institution. Additionally, it also fails to group all the authors who belong to the same institution. A wide variety of errors have been identified and classified. Research limitations/implications - Even though the analyzed sample is good enough to empirically answer the research questions initially proposed, a more comprehensive study should be performed to calibrate the real volume of the errors. Practical implications - The discovered affiliation link errors prevent institutions from being able to access the profiles of all their respective authors using the institutions lists offered by GSC. Additionally, it introduces a shortcoming in the navigation features of Google Scholar which may impair web user experience. Social implications - Some institutions (mainly universities) are under-represented in the affiliation feature provided by GSC. This fact might jeopardize the visibility of institutions as well as the use of this feature in bibliometric or webometric analyses. Originality/value - This work proves inconsistencies in the affiliation feature provided by GSC. A whole national university system is systematically analyzed and several queries have been used to reveal errors in its functioning. The completeness of the errors identified and the empirical data examined are the most exhaustive to date regarding this topic. Finally, some recommendations about how to correctly fill in the affiliation data (both for authors and institutions) and how to improve this feature are provided as well.Orduña Malea, E.; Ayllón, JM.; Martín-Martín, A.; Delgado-López-Cózar, E. (2017). The lost academic home: institutional affiliation links in Google Scholar Citations. Online Information Review. 41(6):762-781. doi:10.1108/OIR-10-2016-0302S76278141