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- ItemHandreichung Urheberrecht und Datenschutz(Genève : CERN, 2023) Blumtritt, Ute; Euler, Ellen; Fadeevy, Yuliya; Pohle, Jörg; Rack, Fabian; Wrzesinski, MarcelDie vorliegende Handreichung adressiert wissenschaftsgeleitete Zeitschriften sowie herausgebende Einrichtungen. Sie sollen in die Lage versetzt werden, erste urheberrechtliche wie datenschutzrechtliche Fragen zu beantworten und dabei Qualitätsstandards einzuhalten. Dieser Text ersetzt keine Rechtsberatung, sondern bietet grundsätzliche Informationen, gibt Empfehlungen zum Weiterlesen für klassische Fragestellungen und verweist auf gelungene Beispiele im weiteren Feld des wissenschaftsgeleiteten Publizierens.
- ItemHandreichung Technik und Infastrukturen(Genève : CERN, 2023) Eichler, Frederik; Eppelin, Anita; Kampkaspar, Dario; Schrader, Antonia C.; Söllner, Konstanze; Vierkant, Paul; Withanage, Dulip; Wrzesinski, MarcelIn der vorliegenden Handreichung stellen wir unterschiedliche technische Ressourcen vor, die redaktionelle Arbeiten unterstützen können. Dabei empfiehlt es sich, Software und Systeme zu nutzen, die den Wandel hin zu einer offenen, niederschwelligen und nachhaltigen Wissenschaftskultur fördern. Hierzu zählt in erster Linie die Verwendung von Open-Source-Software. Unsere Empfehlungen haben dabei eine begrenzte Reichweite: Serviceanbieter, Software und Projekte sind zu einem späteren Zeitpunkt ggf. nicht mehr verfügbar. Auch sind gerade die Infrastruktureinrichtungen in das föderale Wissenschaftssystem integriert, was sie bestimmten Unwägbarkeiten aussetzt.
- ItemTinyGenius: Intertwining natural language processing with microtask crowdsourcing for scholarly knowledge graph creation(New York,NY,United States : Association for Computing Machinery, 2022) Oelen, Allard; Stocker, Markus; Auer, Sören; Aizawa, AkikoAs the number of published scholarly articles grows steadily each year, new methods are needed to organize scholarly knowledge so that it can be more efficiently discovered and used. Natural Language Processing (NLP) techniques are able to autonomously process scholarly articles at scale and to create machine readable representations of the article content. However, autonomous NLP methods are by far not sufficiently accurate to create a high-quality knowledge graph. Yet quality is crucial for the graph to be useful in practice. We present TinyGenius, a methodology to validate NLP-extracted scholarly knowledge statements using microtasks performed with crowdsourcing. The scholarly context in which the crowd workers operate has multiple challenges. The explainability of the employed NLP methods is crucial to provide context in order to support the decision process of crowd workers. We employed TinyGenius to populate a paper-centric knowledge graph, using five distinct NLP methods. In the end, the resulting knowledge graph serves as a digital library for scholarly articles.
- ItemA Data Model for Linked Stage Graph and the Historical Performing Arts Domain(Aachen, Germany : RWTH Aachen, 2023) Tietz, Tabea; Bruns, Oleksandra; Sack, Harald; Bikakis, Antonis; Ferrario, Roberta; Jean, Stéphane; Markhoff, Béatrice; Mosca, Alessandro; Nicolosi Asmundo, MariannaThe performing arts are complex, dynamic and embedded into societal and political systems. Providing means to research historical performing arts data is therefore crucial for understanding our history and culture. However, currently no commonly accepted ontology for historical performing arts data exists. On the example of the Linked Stage Graph, this position paper presents the ongoing process of creating an application-driven and efficient data model by leveraging and building upon existing standards and ontologies like CIDOC-CRM, FRBR, and FRBRoo.
- ItemAn Approach to Evaluate User Interfaces in a Scholarly Knowledge Communication Domain(Cham : Springer, 2023) Obrezkov, Denis; Oelen, Allard; Auer, Sören; Abdelnour-Nocera, José L.; Marta Lárusdóttir; Petrie, Helen; Piccinno, Antonio; Winckler, MarcoThe amount of research articles produced every day is overwhelming: scholarly knowledge is getting harder to communicate and easier to get lost. A possible solution is to represent the information in knowledge graphs: structures representing knowledge in networks of entities, their semantic types, and relationships between them. But this solution has its own drawback: given its very specific task, it requires new methods for designing and evaluating user interfaces. In this paper, we propose an approach for user interface evaluation in the knowledge communication domain. We base our methodology on the well-established Cognitive Walkthough approach but employ a different set of questions, tailoring the method towards domain-specific needs. We demonstrate our approach on a scholarly knowledge graph implementation called Open Research Knowledge Graph (ORKG).
- ItemExploring the Impact of Negative Sampling on Patent Citation Recommendation(Paris : CNRS, 2023) Dessi, Rima; Aras, Hidir; Alam, MehwishDue to the increasing number of patents being published every day, patent citation recommendations have become one of the challenging tasks. Since patent citations may lead to legal and economic consequences, patent recommendations are even more challenging as compared to scientific article citations. One of the crucial components of the patent citation algorithm is negative sampling which is also a part of many other tasks such as text classification, knowledge graph completion, etc. This paper, particularly focuses on proposing a transformer-based ranking model for patent recommendations. It further experimentally compares the performance of patent recommendations based on various state-of-the-art negative sampling approaches to measure and compare the effectiveness of these approaches to aid future developments. These experiments are performed on a newly collected dataset of US patents from Google patents.
- ItemDiving into Knowledge Graphs for Patents: Open Challenges and Benefits(Aachen, Germany : RWTH Aachen, 2023) Dessi, Danilo; Dessi, Rima; Alam, Mehwish; Trojahn, Cassia; Hertling, Sven; Pesquita, Catia; Aebeloe, Christian; Aras, Hidir; Azzam, Amr; Cano, Juan; Domingue, John; Gottschalk, Simon; Hartig, Olaf; Hose, Katja; Kirrane, Sabrina; Lisena, Pasquale; Osborne, Francesco; Rohde, Philipp; Steels, Luc; Taelman, Ruben; Third, Aisling; Tiddi, Ilaria; Türker, RimaTextual documents are the means of sharing information and preserving knowledge for a large variety of domains. The patent domain is also using such a paradigm which is becoming difficult to maintain and is limiting the potentialities of using advanced AI systems for domain analysis. To overcome this issue, it is more and more frequent to find approaches to transform textual representations into Knowledge Graphs (KGs). In this position paper, we discuss KGs within the patent domain, present its challenges, and envision the benefits of such technologies for this domain. In addition, this paper provides insights of such KGs by reproducing an existing pipeline to create KGs and applying it to patents in the computer science domain.
- ItemTRANSRAZ Data Model: Towards a Geosocial Representation of Historical Cities(Berlin : AKA, 2023) Bruns, Oleksandra; Tietz, Tabea; Göller, Sandra; Sack, Harald; Acosta, M.; Peroni, S.; Vahdati, S.; Gentile, A.-L.; Pellegrini, T.; Kalo, J.-C.Preserving historical city architectures and making them (publicly) available has emerged as an important field of the cultural heritage and digital humanities research domain. In this context, the TRANSRAZ project is creating an interactive 3D environment of the historical city of Nuremberg which spans over different periods of time. Next to the exploration of the city’s historical architecture, TRANSRAZ is also integrating information about its inhabitants, organizations, and important events, which are extracted from historical documents semi-automatically. Knowledge Graphs have proven useful and valuable to integrate and enrich these heterogeneous data. However, this task also comes with versatile data modeling challenges. This paper contributes the TRANSRAZ data model, which integrates agents, architectural objects, events, and historical documents into the 3D research environment by means of ontologies. Goal is to explore Nuremberg’s multifaceted past in different time layers in the context of its architectural, social, economical, and cultural developments.
- ItemImproving Language Model Predictions via Prompts Enriched with Knowledge Graphs(Aachen, Germany : RWTH Aachen, 2023) Brate, Ryan; Minh-Dang, Hoang; Hoppe, Fabian; He, Yuan; Meroño-Peñuela, Albert; Sadashivaiah, Vijay; Alam, Mehwish; Buscaldi, Davide; Cochez, Michael; Osborne, Francesco; Reforgiato Recupero, DiegoDespite advances in deep learning and knowledge graphs (KGs), using language models for natural language understanding and question answering remains a challenging task. Pre-trained language models (PLMs) have shown to be able to leverage contextual information, to complete cloze prompts, next sentence completion and question answering tasks in various domains. Unlike structured data querying in e.g. KGs, mapping an input question to data that may or may not be stored by the language model is not a simple task. Recent studies have highlighted the improvements that can be made to the quality of information retrieved from PLMs by adding auxiliary data to otherwise naive prompts. In this paper, we explore the effects of enriching prompts with additional contextual information leveraged from the Wikidata KG on language model performance. Specifically, we compare the performance of naive vs. KG-engineered cloze prompts for entity genre classification in the movie domain. Selecting a broad range of commonly available Wikidata properties, we show that enrichment of cloze-style prompts with Wikidata information can result in a significantly higher recall for the investigated BERT and RoBERTa large PLMs. However, it is also apparent that the optimum level of data enrichment differs between models.
- ItemVerwertungsgesellschaften und Open Content – Schnittmengen und Friktionen(Marburg : Büchner, 2023) Rack, Fabian; Fischer, Georg; Klingner, Stephan; Zill, MalteInhalte gegen Geld lizenzieren und an VG-Ausschüttungen teilhaben einerseits, Inhalte kostenlos der Allgemeinheit als »Open Content« zur Verfügung stellen andererseits – nur scheinbar ein Widerspruch. Der Beitrag zeigt an den Beispielen Musik und Text, wo sich beide Ansätze treffen und inwieweit sie miteinander vereinbar sind. Er plädiert dafür, die Vereinbarkeit der Ansätze zu stärken und Unsicherheiten in der praktischen Anwendung abzubauen.