Search Results
Handreichung Urheberrecht und Datenschutz
Die 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.
Contextual Language Models for Knowledge Graph Completion
Knowledge Graphs (KGs) have become the backbone of various machine learning based applications over the past decade. However, the KGs are often incomplete and inconsistent. Several representation learning based approaches have been introduced to complete the missing information in KGs. Besides, Neural Language Models (NLMs) have gained huge momentum in NLP applications. However, exploiting the contextual NLMs to tackle the Knowledge Graph Completion (KGC) task is still an open research problem. In this paper, a GPT-2 based KGC model is proposed and is evaluated on two benchmark datasets. The initial results obtained from the _ne-tuning of the GPT-2 model for triple classi_cation strengthens the importance of usage of NLMs for KGC. Also, the impact of contextual language models for KGC has been discussed.
Modelling Archival Hierarchies in Practice: Key Aspects and Lessons Learned
An increasing number of archival institutions aim to provide public access to historical documents. Ontologies have been designed, developed and utilised to model the archival description of historical documents and to enable interoperability between different information sources.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.
Survey: Open Science in Higher Education
Based on a checklist that was developed during a workshop at OER Camp 2016 and presented as a Science 2.0 conference 2016 poster [1], we conducted an online survey among university teachers representing a sufficient variety of subjects. The survey was online from Feb 6th to March 3rd 2017. We got 360 responses, whereof 210 were completes, see raw data [2]. The poster is presented at Open Science Conference, 21.-22.3.2017, Berlin.
Comparative Verification of the Digital Library of Mathematical Functions and Computer Algebra Systems
Digital mathematical libraries assemble the knowledge of years of mathematical research. Numerous disciplines (e.g., physics, engineering, pure and applied mathematics) rely heavily on compendia gathered findings. Likewise, modern research applications rely more and more on computational solutions, which are often calculated and verified by computer algebra systems. Hence, the correctness, accuracy, and reliability of both digital mathematical libraries and computer algebra systems is a crucial attribute for modern research. In this paper, we present a novel approach to verify a digital mathematical library and two computer algebra systems with one another by converting mathematical expressions from one system to the other. We use our previously developed conversion tool (referred to as ) to translate formulae from the NIST Digital Library of Mathematical Functions to the computer algebra systems Maple and Mathematica. The contributions of our presented work are as follows: (1) we present the most comprehensive verification of computer algebra systems and digital mathematical libraries with one another; (2) we significantly enhance the performance of the underlying translator in terms of coverage and accuracy; and (3) we provide open access to translations for Maple and Mathematica of the formulae in the NIST Digital Library of Mathematical Functions.
Biobank Oversight and Sanctions Under the General Data Protection Regulation
This contribution offers an insight into the function and problems of the oversight and sanctions mechanisms outlined in the General Data Protection Regulation as they relate to the biobanking context. These mechanisms might be considered as meta-mechanisms—mechanisms relating to, but not consisting of, substantive legal principles—functioning in tandem to ensure biobank compliance with data protection principles. Each of the mechanisms outlines, on paper at least, comprehensive and impressive compliance architecture—both expanding on their capacity in relation to Directive 95/46. Accordingly, each mechanism looks likely to have a significant and lasting impact on biobanks and biobanking. Despite this comprehensiveness, however, the mechanisms are not immune from critique. Problems appear regarding the standard of protection provided for research subject rights, regarding the disproportionate impact on legitimate interests tied up with the biobanking process—particularly genomic research interests—and regarding their practical implementability in biobanking.
Leveraging Literals for Knowledge Graph Embeddings
Nowadays, Knowledge Graphs (KGs) have become invaluable for various applications such as named entity recognition, entity linking, question answering. However, there is a huge computational and storage cost associated with these KG-based applications. Therefore, there arises the necessity of transforming the high dimensional KGs into low dimensional vector spaces, i.e., learning representations for the KGs. Since a KG represents facts in the form of interrelations between entities and also using attributes of entities, the semantics present in both forms should be preserved while transforming the KG into a vector space. Hence, the main focus of this thesis is to deal with the multimodality and multilinguality of literals when utilizing them for the representation learning of KGs. The other task is to extract benchmark datasets with a high level of difficulty for tasks such as link prediction and triple classification. These datasets could be used for evaluating both kind of KG Embeddings, those using literals and those which do not include literals.
Temporal Role Annotation for Named Entities
Natural language understanding tasks are key to extracting structured and semantic information from text. One of the most challenging problems in natural language is ambiguity and resolving such ambiguity based on context including temporal information. This paper, focuses on the task of extracting temporal roles from text, e.g. CEO of an organization or head of a state. A temporal role has a domain, which may resolve to different entities depending on the context and especially on temporal information, e.g. CEO of Microsoft in 2000. We focus on the temporal role extraction, as a precursor for temporal role disambiguation. We propose a structured prediction approach based on Conditional Random Fields (CRF) to annotate temporal roles in text and rely on a rich feature set, which extracts syntactic and semantic information from text. We perform an extensive evaluation of our approach based on two datasets. In the first dataset, we extract nearly 400k instances from Wikipedia through distant supervision, whereas in the second dataset, a manually curated ground-truth consisting of 200 instances is extracted from a sample of The New York Times (NYT) articles. Last, the proposed approach is compared against baselines where significant improvements are shown for both datasets.
Data Protection Impact Assessments in Practice: Experiences from Case Studies
In the context of the project A Data Protection Impact Assessment (DPIA) Tool for Practical Use in Companies and Public Administration an operationalization for Data Protection Impact Assessments was developed based on the approach of Forum Privatheit. This operationalization was tested and refined during twelve tests with startups, small- and medium sized enterprises, corporations and public bodies. This paper presents the operationalization and summarizes the experience from the tests.
DDB-KG: The German Bibliographic Heritage in a Knowledge Graph
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.