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search.filters.applied.f.access: openAccess × Author: Ewerth, Ralph × DDC: 020 × Type: Text × Version: publishedVersion ×

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Now showing 1 - 10 of 10
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    Estimating the information gap between textual and visual representations
    (New York City : Association for Computing Machinery, 2017) Henning, Christian; Ewerth, Ralph
    Photos, drawings, figures, etc. supplement textual information in various kinds of media, for example, in web news or scientific pub- lications. In this respect, the intended effect of an image can be quite different, e.g., providing additional information, focusing on certain details of surrounding text, or simply being a general il- lustration of a topic. As a consequence, the semantic correlation between information of different modalities can vary noticeably, too. Moreover, cross-modal interrelations are often hard to describe in a precise way. The variety of possible interrelations of textual and graphical information and the question, how they can be de- scribed and automatically estimated have not been addressed yet by previous work. In this paper, we present several contributions to close this gap. First, we introduce two measures to describe cross- modal interrelations: cross-modal mutual information (CMI) and semantic correlation (SC). Second, a novel approach relying on deep learning is suggested to estimate CMI and SC of textual and visual information. Third, three diverse datasets are leveraged to learn an appropriate deep neural network model for the demanding task. The system has been evaluated on a challenging test set and the experimental results demonstrate the feasibility of the approach.
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    On the effects of spam filtering and incremental learning for web-supervised visual concept classification
    (New York City : Association for Computing Machinery, 2016) Springstein , Matthias; Ewerth, Ralph
    Deep neural networks have been successfully applied to the task of visual concept classification. However, they require a large number of training examples for learning. Although pre-trained deep neural networks are available for some domains, they usually have to be fine-tuned for an envisaged target domain. Recently, some approaches have been suggested that are aimed at incrementally (or even endlessly) learning visual concepts based on Web data. Since tags of Web images are often noisy, normally some filtering mechanisms are employed in order to remove ``spam'' images that are not appropriate for training. In this paper, we investigate several aspects of a web-supervised system that has to be adapted to another target domain: 1.) the effect of incremental learning, 2.) the effect of spam filtering, and 3.) the behavior of particular concept classes with respect to 1.) and 2.). The experimental results provide some insights under which conditions incremental learning and spam filtering are useful.
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    A Multimodal Approach for Semantic Patent Image Retrieval
    (Aachen, Germany : RWTH Aachen, 2021) Pustu-Iren, Kader; Bruns, Gerrit; Ewerth, Ralph
    Patent images such as technical drawings contain valuable information and are frequently used by experts to compare patents. However, current approaches to patent information retrieval are largely focused on textual information. Consequently, we review previous work on patent retrieval with a focus on illustrations in figures. In this paper, we report on work in progress for a novel approach for patent image retrieval that uses deep multimodal features. Scene text spotting and optical character recognition are employed to extract numerals from an image to subsequently identify references to corresponding sentences in the patent document. Furthermore, we use a neural state-of-the-art CLIP model to extract structural features from illustrations and additionally derive textual features from the related patent text using a sentence transformer model. To fuse our multimodal features for similarity search we apply re-ranking according to averaged or maximum scores. In our experiments, we compare the impact of different modalities on the task of similarity search for patent images. The experimental results suggest that patent image retrieval can be successfully performed using the proposed feature sets, while the best results are achieved when combining the features of both modalities.
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    Semi-supervised identification of rarely appearing persons in video by correcting weak labels
    (New York City : Association for Computing Machinery, 2016) Müller, Eric; Otto, Christian; Ewerth, Ralph
    Some recent approaches for character identification in movies and TV broadcasts are realized in a semi-supervised manner by assigning transcripts and/or subtitles to the speakers. However, the labels obtained in this way achieve only an accuracy of 80% - 90% and the number of training examples for the different actors is unevenly distributed. In this paper, we propose a novel approach for person identification in video by correcting and extending the training data with reliable predictions to reduce the number of annotation errors. Furthermore, the intra-class diversity of rarely speaking characters is enhanced. To address the imbalance of training data per person, we suggest two complementary prediction scores. These scores are also used to recognize whether or not a face track belongs to a (supporting) character whose identity does not appear in the transcript etc. Experimental results demonstrate the feasibility of the proposed approach, outperforming the current state of the art.
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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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    “Are machines better than humans in image tagging?” - A user study adds to the puzzle
    (Heidelberg : Springer, 2017) Ewerth, Ralph; Springstein, Matthias; Phan-Vogtmann, Lo An; Schütze, Juliane
    “Do machines perform better than humans in visual recognition tasks?” Not so long ago, this question would have been considered even somewhat provoking and the answer would have been clear: “No”. In this paper, we present a comparison of human and machine performance with respect to annotation for multimedia retrieval tasks. Going beyond recent crowdsourcing studies in this respect, we also report results of two extensive user studies. In total, 23 participants were asked to annotate more than 1000 images of a benchmark dataset, which is the most comprehensive study in the field so far. Krippendorff’s α is used to measure inter-coder agreement among several coders and the results are compared with the best machine results. The study is preceded by a summary of studies which compared human and machine performance in different visual and auditory recognition tasks. We discuss the results and derive a methodology in order to compare machine performance in multimedia annotation tasks at human level. This allows us to formally answer the question whether a recognition problem can be considered as solved. Finally, we are going to answer the initial question.
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    Multimodal news analytics using measures of cross-modal entity and context consistency
    (London : Springer, 2021) Müller-Budack, Eric; Theiner, Jonas; Diering, Sebastian; Idahl, Maximilian; Hakimov, Sherzod; Ewerth, Ralph
    The World Wide Web has become a popular source to gather information and news. Multimodal information, e.g., supplement text with photographs, is typically used to convey the news more effectively or to attract attention. The photographs can be decorative, depict additional details, but might also contain misleading information. The quantification of the cross-modal consistency of entity representations can assist human assessors’ evaluation of the overall multimodal message. In some cases such measures might give hints to detect fake news, which is an increasingly important topic in today’s society. In this paper, we present a multimodal approach to quantify the entity coherence between image and text in real-world news. Named entity linking is applied to extract persons, locations, and events from news texts. Several measures are suggested to calculate the cross-modal similarity of the entities in text and photograph by exploiting state-of-the-art computer vision approaches. In contrast to previous work, our system automatically acquires example data from the Web and is applicable to real-world news. Moreover, an approach that quantifies contextual image-text relations is introduced. The feasibility is demonstrated on two datasets that cover different languages, topics, and domains.
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    B!SON: A Tool for Open Access Journal Recommendation
    (Heidelberg : Springer, 2022) Entrup, Elias; Eppelin, Anita; Ewerth, Ralph; Hartwig, Josephine; Tullney, Marco; Wohlgemuth, Michael; Hoppe, Anett; Nugent, Ronan
    Finding a suitable open access journal to publish scientific work is a complex task: Researchers have to navigate a constantly growing number of journals, institutional agreements with publishers, funders’ conditions and the risk of Predatory Publishers. To help with these challenges, we introduce a web-based journal recommendation system called B!SON. It is developed based on a systematic requirements analysis, built on open data, gives publisher-independent recommendations and works across domains. It suggests open access journals based on title, abstract and references provided by the user. The recommendation quality has been evaluated using a large test set of 10,000 articles. Development by two German scientific libraries ensures the longevity of the project.
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    Towards an Open Research Knowledge Graph
    (Zenodo, 2018) Auer, Sören; Blümel, Ina; Ewerth, Ralph; Garatzogianni, Alexandra; Heller,, Lambert; Hoppe, Anett; Kasprzik, Anna; Koepler, Oliver; Nejdl, Wolfgang; Plank, Margret; Sens, Irina; Stocker, Markus; Tullney, Marco; Vidal, Maria-Esther; van Wezenbeek, Wilma
    The document-oriented workflows in science have reached (or already exceeded) the limits of adequacy as highlighted for example by recent discussions on the increasing proliferation of scientific literature and the reproducibility crisis. Despite an improved and digital access to scientific publications in the last decades, the exchange of scholarly knowledge continues to be primarily document-based: Researchers produce essays and articles that are made available in online and offline publication media as roughly granular text documents. With current developments in areas such as knowledge representation, semantic search, human-machine interaction, natural language processing, and artificial intelligence, it is possible to completely rethink this dominant paradigm of document-centered knowledge exchange and transform it into knowledge-based information flows by representing and expressing knowledge through semantically rich, interlinked knowledge graphs. The core of the establishment of knowledge-based information flows is the distributed, decentralized, collaborative creation and evolution of information models, vocabularies, ontologies, and knowledge graphs for the establishment of a common understanding of data and information between the various stakeholders as well as the integration of these technologies into the infrastructure and processes of search and knowledge exchange in the research library of the future. By integrating these information models into existing and new research infrastructure services, the information structures that are currently still implicit and deeply hidden in documents can be made explicit and directly usable. This revolutionizes scientific work because information and research results can be seamlessly interlinked with each other and better mapped to complex information needs. As a result, scientific work becomes more effective and efficient, since results become directly comparable and easier to reuse. In order to realize the vision of knowledge-based information flows in scholarly communication, comprehensive long-term technological infrastructure development and accompanying research are required. To secure information sovereignty, it is also of paramount importance to science – and urgency to science policymakers – that scientific infrastructures establish an open counterweight to emerging commercial developments in this area. The aim of this position paper is to facilitate the discussion on requirements, design decisions and a minimum viable product for an Open Research Knowledge Graph infrastructure. TIB aims to start developing this infrastructure in an open collaboration with interested partner organizations and individuals.
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