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Author: Auer, Sören × DDC: 004 × Has files: Yes ×

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Now showing 1 - 10 of 22
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    EVENTSKG: A 5-Star Dataset of Top-Ranked Events in Eight Computer Science Communities
    (Berlin ; Heidelberg : Springer, 2019) Fathalla, Said; Lange, Christoph; Auer, Sören; Hitzler, Pascal; Fernández, Miriam; Janowicz, Krzysztof; Zaveri, Amrapali; Gray, Alasdair J.G.; Lopez, Vanessa; Haller, Armin; Hammar, Karl
    Metadata of scientific events has become increasingly available on the Web, albeit often as raw data in various formats, disregarding its semantics and interlinking relations. This leads to restricting the usability of this data for, e.g., subsequent analyses and reasoning. Therefore, there is a pressing need to represent this data in a semantic representation, i.e., Linked Data. We present the new release of the EVENTSKG dataset, comprising comprehensive semantic descriptions of scientific events of eight computer science communities. Currently, EVENTSKG is a 5-star dataset containing metadata of 73 top-ranked event series (almost 2,000 events) established over the last five decades. The new release is a Linked Open Dataset adhering to an updated version of the Scientific Events Ontology, a reference ontology for event metadata representation, leading to richer and cleaner data. To facilitate the maintenance of EVENTSKG and to ensure its sustainability, EVENTSKG is coupled with a Java API that enables users to add/update events metadata without going into the details of the representation of the dataset. We shed light on events characteristics by analyzing EVENTSKG data, which provides a flexible means for customization in order to better understand the characteristics of renowned CS events.
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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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    TinyGenius: 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, Akiko
    As 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.
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    Accessibility and Personalization in OpenCourseWare : An Inclusive Development Approach
    (Piscataway, NJ : IEEE, 2020) Elias, Mirette; Ruckhaus, Edna; Draffan, E.A.; James, Abi; Suárez-Figueroa, Mari Carmen; Lohmann, Steffen; Khiat, Abderrahmane; Auer, Sören; Chang, Maiga; Sampson, Demetrios G.; Huang, Ronghuai; Hooshyar, Danial; Chen, Nian-Shing; Kinshuk; Pedaste, Margus
    OpenCourseWare (OCW) has become a desirable source for sharing free educational resources which means there will always be users with differing needs. It is therefore the responsibility of OCW platform developers to consider accessibility as one of their prioritized requirements to ensure ease of use for all, including those with disabilities. However, the main challenge when creating an accessible platform is the ability to address all the different types of barriers that might affect those with a wide range of physical, sensory and cognitive impairments. This article discusses accessibility and personalization strategies and their realisation in the SlideWiki platform, in order to facilitate the development of accessible OCW. Previously, accessibility was seen as a complementary feature that can be tackled in the implementation phase. However, a meaningful integration of accessibility features requires thoughtful consideration during all project phases with active involvement of related stakeholders. The evaluation results and lessons learned from the SlideWiki development process have the potential to assist in the development of other systems that aim for an inclusive approach. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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    Clustering Semantic Predicates in the Open Research Knowledge Graph
    (Heidelberg : Springer, 2022) Arab Oghli, Omar; D’Souza, Jennifer; Auer, Sören
    When semantically describing knowledge graphs (KGs), users have to make a critical choice of a vocabulary (i.e. predicates and resources). The success of KG building is determined by the convergence of shared vocabularies so that meaning can be established. The typical lifecycle for a new KG construction can be defined as follows: nascent phases of graph construction experience terminology divergence, while later phases of graph construction experience terminology convergence and reuse. In this paper, we describe our approach tailoring two AI-based clustering algorithms for recommending predicates (in RDF statements) about resources in the Open Research Knowledge Graph (ORKG) https://orkg.org/. Such a service to recommend existing predicates to semantify new incoming data of scholarly publications is of paramount importance for fostering terminology convergence in the ORKG. Our experiments show very promising results: a high precision with relatively high recall in linear runtime performance. Furthermore, this work offers novel insights into the predicate groups that automatically accrue loosely as generic semantification patterns for semantification of scholarly knowledge spanning 44 research fields.
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    Encoding Knowledge Graph Entity Aliases in Attentive Neural Network for Wikidata Entity Linking
    (Berlin ; Heidelberg : Springer, 2020) Mulang’, Isaiah Onando; Singh, Kuldeep; Vyas, Akhilesh; Shekarpour, Saeedeh; Vidal, Maria-Esther; Lehmann, Jens; Auer, Sören; Huang, Zhisheng; Beek, Wouter; Wang, Hua; Zhou, Rui; Zhang, Yanchun
    The collaborative knowledge graphs such as Wikidata excessively rely on the crowd to author the information. Since the crowd is not bound to a standard protocol for assigning entity titles, the knowledge graph is populated by non-standard, noisy, long or even sometimes awkward titles. The issue of long, implicit, and nonstandard entity representations is a challenge in Entity Linking (EL) approaches for gaining high precision and recall. Underlying KG in general is the source of target entities for EL approaches, however, it often contains other relevant information, such as aliases of entities (e.g., Obama and Barack Hussein Obama are aliases for the entity Barack Obama). EL models usually ignore such readily available entity attributes. In this paper, we examine the role of knowledge graph context on an attentive neural network approach for entity linking on Wikidata. Our approach contributes by exploiting the sufficient context from a KG as a source of background knowledge, which is then fed into the neural network. This approach demonstrates merit to address challenges associated with entity titles (multi-word, long, implicit, case-sensitive). Our experimental study shows ≈8% improvements over the baseline approach, and significantly outperform an end to end approach for Wikidata entity linking.
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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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    SemSur: A Core Ontology for the Semantic Representation of Research Findings
    (Amsterdam [u.a.] : Elsevier, 2018) Fathalla, Said; Vahdati, Sahar; Auer, Sören; Lange, Christoph; Fensel, Anna; de Boer, Victor; Pellegrini, Tassilo; Kiesling, Elmar; Haslhofer, Bernhard; Hollink, Laura; Schindler, Alexander
    The way how research is communicated using text publications has not changed much over the past decades. We have the vision that ultimately researchers will work on a common structured knowledge base comprising comprehensive semantic and machine-comprehensible descriptions of their research, thus making research contributions more transparent and comparable. We present the SemSur ontology for semantically capturing the information commonly found in survey and review articles. SemSur is able to represent scientific results and to publish them in a comprehensive knowledge graph, which provides an efficient overview of a research field, and to compare research findings with related works in a structured way, thus saving researchers a significant amount of time and effort. The new release of SemSur covers more domains, defines better alignment with external ontologies and rules for eliciting implicit knowledge. We discuss possible applications and present an evaluation of our approach with the retrospective, exemplary semantification of a survey. We demonstrate the utility of the SemSur ontology to answer queries about the different research contributions covered by the survey. SemSur is currently used and maintained at OpenResearch.org.
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    An 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, Marco
    The 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).
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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.