2021, Fraumann, Grischa, Mugnaino, Rogério, Sanz-Casado, Elías, Ball, Rafael

Conferences are deeply connected to research fields, in this case bibliometrics. As such, they are a venue to present and discuss current and innovative research, and play an important role for the scholarly community. In this article, we provide an overview on the history of conferences in bibliometrics. We conduct an analysis to list the most prominent conferences that were announced in the newsletter by ISSI, the International Society for Scientometrics and Informetrics. Furthermore, we describe how conferences are connected to learned societies and journals. Finally, we provide an outlook on how conferences might change in future.

2021, Fraumann, Grischa, D'Souza, Jennifer, Holmberg, Kim

The Eigenfactor™ is a journal metric, which was developed by Bergstrom and his colleagues at the University of Washington. They invented the Eigenfactor as a response to the criticism against the use of simple citation counts. The Eigenfactor makes use of the network structure of citations, i.e. citations between journals, and establishes the importance, influence or impact of a journal based on its location in a network of journals. The importance is defined based on the number of citations between journals. As such, the Eigenfactor algorithm is based on Eigenvector centrality. While journal based metrics have been criticized, the Eigenfactor has also been suggested as an alternative in the widely used San Francisco Declaration on ResearchAssessment (DORA).

2021, Stegmüller, Johannes, Bauer-Marquart, Fabian, Meuschke, Norman, Ruas, Terry, Schubotz, Moritz, Gipp, Bela, Zhang, Chengzhi, Mayr, Philipp, Lu, Wie, Zhang, Yi

Identifying cross-language plagiarism is challenging, especially for distant language pairs and sense-for-sense translations. We introduce the new multilingual retrieval model Cross-Language Ontology-Based Similarity Analysis (CL-OSA) for this task. CL-OSA represents documents as entity vectors obtained from the open knowledge graph Wikidata. Opposed to other methods, CL-OSA does not require computationally expensive machine translation, nor pre-training using comparable or parallel corpora. It reliably disambiguates homonyms and scales to allow its application toWebscale document collections. We show that CL-OSA outperforms state-of-the-art methods for retrieving candidate documents from five large, topically diverse test corpora that include distant language pairs like Japanese-English. For identifying cross-language plagiarism at the character level, CL-OSA primarily improves the detection of sense-for-sense translations. For these challenging cases, CL-OSA’s performance in terms of the well-established PlagDet score exceeds that of the best competitor by more than factor two. The code and data of our study are openly available.

2021, Gottschalk, Simon, Kacupaj, Endri, Abdollahi, Sara, Alves, Diego, Amaral, Gabriel, Koutsiana, Elisavet, Kuculo, Tin, Major, Daniela, Mello, Caio, Cheema, Gullal S., Sittar, Abdul, Swati, Tahmasebzadeh, Golsa, Thakkar, Gaurish

Accessing and understanding contemporary and historical events of global impact such as the US elections and the Olympic Games is a major prerequisite for cross-lingual event analytics that investigate event causes, perception and consequences across country borders. In this paper, we present the Open Event Knowledge Graph (OEKG), a multilingual, event-centric, temporal knowledge graph composed of seven different data sets from multiple application domains, including question answering, entity recommendation and named entity recognition. These data sets are all integrated through an easy-to-use and robust pipeline and by linking to the event-centric knowledge graph EventKG. We describe their common schema and demonstrate the use of the OEKG at the example of three use cases: type-specific image retrieval, hybrid question answering over knowledge graphs and news articles, as well as language-specific event recommendation. The OEKG and its query endpoint are publicly available.

2021, Vafaie, Mahsa, Bruns, Oleksandra, Pilz, Nastasja, Dessì, Danilo, Sack, Harald, Sumikawa, Yasunobu, Ikejiri, Ryohei, Doucet, Antoine, Pfanzelter, Eva, Hasanuzzaman, Mohammed, Dias, Gaël, Milligan, Ian, Jatowt, Adam

An increasing number of archival institutions aim to provide public access to historical documents. Ontologies have been designed, developed and utilised to model the archival description of historical documents and to enable interoperability between different information sources. However, due to the heterogeneous nature of archives and archival systems, current ontologies for the representation of archival content do not always cover all existing structural organisation forms equallywell. After briefly contextualising the heterogeneity in the hierarchical structure of German archives, this paper describes and evaluates differences between two archival ontologies, ArDO and RiC-O, and their approaches to modelling hierarchy levels and archive dynamics.

2021, Burger, Marleen, Cordts, Anette, Habermann, Ted

Im vorliegenden Erfahrungsbericht stellen wir eine Metadatenanalyse vor, welche die Metadatenqualität von 144 Repositorien des TIB-DOI-Service im Hinblick auf die Erfüllung der FAIR Data Principles, Konsistenz und Vollständigkeit untersucht. Im Ergebnis zeigt sich, dass der Fokus der untersuchten Repositorien schwerpunktmäßig auf der Auffindbarkeit der mit Metadaten beschriebenen Ressourcen liegt und im Gesamtdurchschnitt über die Metadaten-Pflichtfelder hinaus nur wenige weitere Metadaten angegeben werden. Insbesondere mit Blick auf eine angestrebte bessere Nachnutzbarkeit sowie eine stärkere Verknüpfung mit anderen in Beziehung stehenden persistenten Identifikatoren wie ORCID, ROR ID oder DOI-zu-DOI-Beziehungen mit zitierten oder zitierenden Ressourcen, bestehen noch ungenutzte Potenziale, die im Sinne einer offenen, zukunftsweisenden Wissenschaft erschlossen werden sollten. Dahingegen zeigt unsere Analyse auch einzelne Repositorien mit umfangreichen Metadaten als Best-Practice-Beispiele auf, an denen sich andere Repositorien orientieren können. Insgesamt ermöglicht die durchgeführte Metadatenanalyse die Ableitung von Handlungsempfehlungen zur passgenauen Beratung von Repositorien, die ihre Metadatenqualität verbessern möchten.

2021, Oelen, Allard, Stocker, Markus, Auer, Sören

The number of scholarly publications grows steadily every year and it becomes harder to find, assess and compare scholarly knowledge effectively. Scholarly knowledge graphs have the potential to address these challenges. However, creating such graphs remains a complex task. We propose a method to crowdsource structured scholarly knowledge from paper authors with a web-based user interface supported by artificial intelligence. The interface enables authors to select key sentences for annotation. It integrates multiple machine learning algorithms to assist authors during the annotation, including class recommendation and key sentence highlighting. We envision that the interface is integrated in paper submission processes for which we define three main task requirements: The task has to be . We evaluated the interface with a user study in which participants were assigned the task to annotate one of their own articles. With the resulting data, we determined whether the participants were successfully able to perform the task. Furthermore, we evaluated the interface’s usability and the participant’s attitude towards the interface with a survey. The results suggest that sentence annotation is a feasible task for researchers and that they do not object to annotate their articles during the submission process.

2021, Tan, Mary Ann, Tietz, Tabea, Bruns, Oleksandra, Oppenlaender, Jonas, Dessì, Danilo, Harald, Sack, Sumikawa, Yasunobu, Ikejiri, Ryohei, Doucet, Antoine, Pfanzelter, Eva, Hasanuzzaman, Mohammed, Dias, Gaël, Milligan, Ian, Jatowt, Adam

Under the German government’s initiative “NEUSTART Kultur”, the German Digital Library or Deutsche Digitale Bibliothek (DDB) is undergoing improvements to enhance user-experience. As an initial step, emphasis is placed on creating a knowledge graph from the bibliographic record collection of the DDB. This paper discusses the challenges facing the DDB in terms of retrieval and the solutions in addressing them. In particular, limitations of the current data model or ontology to represent bibliographic metadata is analyzed through concrete examples. This study presents the complete ontological mapping from DDB-Europeana Data Model (DDB-EDM) to FaBiO, and a prototype of the DDB-KG made available as a SPARQL endpoint. The suitabiliy of the target ontology is demonstrated with SPARQL queries formulated from competency questions.

2021, Dessì, Danilo, Käser, Tanja, Marras, Mirko, Popescu, Elvira, Sack, Harald, Dessì, Danilo, Käser, Tanja, Marras, Mirko, Popescu, Elvira, Sack, Harald

The design and delivering of platforms for online education is fostering increasingly intense research. Scaling up education online brings new emerging needs related with hardly manageable classes, overwhelming content alternatives, and academic dishonesty while interacting remotely, as examples. However, with the impressive progress of the data mining and machine learning fields, combined with the large amounts of learning-related data and high-performance computing, it has been possible to gain a deeper understanding of the nature of learning and teaching online. Methods at the analytical and algorithmic levels are constantly being developed and hybrid approaches are receiving an increasing attention. Recent methods are analyzing not only the online traces left by students a posteriori, but also the extent to which this data can be turned into actionable insights and models, to support the above needs in a computationally efficient, adaptive and timely way. In this paper, we present relevant open challenges lying at the intersection between the machine learning and educational communities, that need to be addressed to further develop the field of intelligent systems for online education. Several areas of research in this field are identified, such as data availability and sharing, time-wise and multi-modal data modelling, generalizability, fairness, explainability, interpretability, privacy, and ethics behind models delivered for supporting education. Practical challenges and recommendations for possible research directions are provided for each of them, paving the way for future advances in this field.

2021, Fraumann, Grischa, Mutz, Rüdiger

The h-index is a mainstream bibliometric indicator, since it is widely used in academia, research management and research policy. While its advantages have been highlighted, such as its simple calculation, it has also received widespread criticism. The criticism is mainly based on the negative effects it may have on scholars, when the index is used to describe the quality of a scholar. The “h” means “highly-cited” and “high achievement”, and should not be confused with the last name of its inventor, Hirsch. Put simply, the h-index combines a measure of quantity and impact in a single indicator. Several initiatives try to provide alternatives to the h-index to counter some of its shortcomings.