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Author: Auer, Sören × End date: 2024 × Start date: 2020 × End date: 2024 × Start date: 2020 × Version: publishedVersion × Subject: Konferenzschrift ×

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Now showing 1 - 7 of 7
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    NLPContributions: An Annotation Scheme for Machine Reading of Scholarly Contributions in Natural Language Processing Literature
    (Aachen : RWTH, 2020) D'Souza, Jennifer; Auer, Sören
    We describe an annotation initiative to capture the scholarly contributions in natural language processing (NLP) articles, particularly, for the articles that discuss machine learning (ML) approaches for various information extraction tasks. We develop the annotation task based on a pilot annotation exercise on 50 NLP-ML scholarly articles presenting contributions to five information extraction tasks 1. machine translation, 2. named entity recognition, 3. Question answering, 4. relation classification, and 5. text classification. In this article, we describe the outcomes of this pilot annotation phase. Through the exercise we have obtained an annotation methodology; and found ten core information units that reflect the contribution of the NLP-ML scholarly investigations. The resulting annotation scheme we developed based on these information units is called NLPContributions. The overarching goal of our endeavor is four-fold: 1) to find a systematic set of patterns of subject-predicate-object statements for the semantic structuring of scholarly contributions that are more or less generically applicable for NLP-ML research articles; 2) to apply the discovered patterns in the creation of a larger annotated dataset for training machine readers [18] of research contributions; 3) to ingest the dataset into the Open Research Knowledge Graph (ORKG) infrastructure as a showcase for creating user-friendly state-of-the-art overviews; 4) to integrate the machine readers into the ORKG to assist users in the manual curation of their respective article contributions. We envision that the NLPContributions methodology engenders a wider discussion on the topic toward its further refinement and development. Our pilot annotated dataset of 50 NLP-ML scholarly articles according to the NLPContributions scheme is openly available to the research community at https://doi.org/10.25835/0019761.
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    24th International Conference on Business Information Systems : Preface
    (Hannover : TIB Open Publishing, 2021) Abramowicz, Witold; Auer, Sören; Abramowicz, Witold; Auer, Sören; Lewańska, Elżbieta
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    Crowdsourcing Scholarly Discourse Annotations
    (New York, NY : ACM, 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.
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    The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources
    (Paris : European Language Resources Association, 2020) D'Souza, Jennifer; Hoppe, Anett; Brack, Arthur; Jaradeh, Mohamad Yaser; Auer, Sören; Ewerth, Ralph
    We introduce the STEM (Science, Technology, Engineering, and Medicine) Dataset for Scientific Entity Extraction, Classification, and Resolution, version 1.0 (STEM-ECR v1.0). The STEM-ECR v1.0 dataset has been developed to provide a benchmark for the evaluation of scientific entity extraction, classification, and resolution tasks in a domain-independent fashion. It comprises abstracts in 10 STEM disciplines that were found to be the most prolific ones on a major publishing platform. We describe the creation of such a multidisciplinary corpus and highlight the obtained findings in terms of the following features: 1) a generic conceptual formalism for scientific entities in a multidisciplinary scientific context; 2) the feasibility of the domain-independent human annotation of scientific entities under such a generic formalism; 3) a performance benchmark obtainable for automatic extraction of multidisciplinary scientific entities using BERT-based neural models; 4) a delineated 3-step entity resolution procedure for human annotation of the scientific entities via encyclopedic entity linking and lexicographic word sense disambiguation; and 5) human evaluations of Babelfy returned encyclopedic links and lexicographic senses for our entities. Our findings cumulatively indicate that human annotation and automatic learning of multidisciplinary scientific concepts as well as their semantic disambiguation in a wide-ranging setting as STEM is reasonable.
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    Semantic Representation of Physics Research Data
    (Setúbal, Portugal : Science and Technology Publications, Lda, 2020) Say, Aysegul; Fathalla, Said; Vahdati, Sahar; Lehmann, Jens; Auer, Sören; Aveiro, David; Dietz, Jan; Filipe, Joaquim
    Improvements in web technologies and artificial intelligence enable novel, more data-driven research practices for scientists. However, scientific knowledge generated from data-intensive research practices is disseminated with unstructured formats, thus hindering the scholarly communication in various respects. The traditional document-based representation of scholarly information hampers the reusability of research contributions. To address this concern, we developed the Physics Ontology (PhySci) to represent physics-related scholarly data in a machine-interpretable format. PhySci facilitates knowledge exploration, comparison, and organization of such data by representing it as knowledge graphs. It establishes a unique conceptualization to increase the visibility and accessibility to the digital content of physics publications. We present the iterative design principles by outlining a methodology for its development and applying three different evaluation approaches: data-driven and criteria-based evaluation, as well as ontology testing.
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    Creation of a Knowledge Space by Semantically Linking Data Repository and Knowledge Management System - a Use Case from Production Engineering
    (Laxenburg : IFAC, 2022) Sheveleva, Tatyana; Wawer, Max Leo; Oladazimi, Pooya; Koepler, Oliver; Nürnberger, Florian; Lachmayer, Roland; Auer, Sören; Mozgova, Iryna
    The seamless documentation of research data flows from generation, processing, analysis, publication, and reuse is of utmost importance when dealing with large amounts of data. Semantic linking of process documentation and gathered data creates a knowledge space enabling the discovery of relations between steps of process chains. This paper shows the design of two systems for data deposit and for process documentation using semantic annotations and linking on a use case of a process chain step of the Tailored Forming Technology.
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    Metadata analysis of open educational resources
    (New York,NY,United States : Association for Computing Machinery, 2021) Tavakoli, Mohammadreza; Elias, Mirette; Kismihók, Gábor; Auer, Sören; Scheffel, Maren
    Open Educational Resources (OERs) are openly licensed educational materials that are widely used for learning. Nowadays, many online learning repositories provide millions of OERs. Therefore, it is exceedingly difficult for learners to find the most appropriate OER among these resources. Subsequently, the precise OER metadata is critical for providing high-quality services such as search and recommendation. Moreover, metadata facilitates the process of automatic OER quality control as the continuously increasing number of OERs makes manual quality control extremely difficult. This work uses the metadata of 8,887 OERs to perform an exploratory data analysis on OER metadata. Accordingly, this work proposes metadata-based scoring and prediction models to anticipate the quality of OERs. Based on the results, our analysis demonstrated that OER metadata and OER content qualities are closely related, as we could detect high-quality OERs with an accuracy of 94.6%. Our model was also evaluated on 884 educational videos from Youtube to show its applicability on other educational repositories.