Filters

2014 - 201923

More filters

202223
Reset filters

Settings

End date: 2022 × Start date: 2022 × End date: 2019 × Start date: 2010 × DDC: 004 × Type: Text ×

Search Results

Now showing 1 - 10 of 23
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    Temporal Role Annotation for Named Entities
    (Amsterdam [u.a.] : Elsevier, 2018) Koutraki, Maria; Bakhshandegan-Moghaddam, Farshad; Sack, Harald; Fensel, Anna; de Boer, Victor; Pellegrini, Tassilo; Kiesling, Elmar; Haslhofer, Bernhard; Hollink, Laura; Schindler, Alexander
    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.
  • Thumbnail Image
    Item
    The Research Core Dataset (KDSF) in the Linked Data context
    (Amsterdam [u.a.] : Elsevier, 2019) Walther, Tatiana; Hauschke, Christian; Kasprzik, Anna; Sicilia, Miguel-Angel; Simons, Ed; Clements, Anna; de Castro, Pablo; Bergström, Johan
    This paper describes our efforts to implement the Research Core Dataset (“Kerndatensatz Forschung”; KDSF) as an ontology in VIVO. KDSF is used in VIVO to record the required metadata on incoming data and to produce reports as an output. While both processes need an elaborate adaptation of the KDSF specification, this paper focusses on the adaptation of the KDSF basic data model for recording data in VIVO. In this context, the VIVO and KDSF ontologies were compared with respect to domain, syntax, structure, and granularity in order to identify correspondences and mismatches. To produce an alignment, different matching approaches have been applied. Furthermore, we made necessary modifications and extensions on KDSF classes and properties.
  • Thumbnail Image
    Item
    Semantic segmentation of non-linear multimodal images for disease grading of inflammatory bowel disease: A segnet-based application
    ([Sétubal] : SCITEPRESS - Science and Technology Publications Lda., 2019) Pradhan, Pranita; Meyer, Tobias; Vieth, Michael; Stallmach, Andreas; Waldner, Maximilian; Schmitt, Michael; Popp, Juergen; Bocklitz, Thomas; De Marsico, Maria; Sanniti di Baja, Gabriella; Fred, Ana
    Non-linear multimodal imaging, the combination of coherent anti-stokes Raman scattering (CARS), two-photon excited fluorescence (TPEF) and second harmonic generation (SHG), has shown its potential to assist the diagnosis of different inflammatory bowel diseases (IBDs). This label-free imaging technique can support the ‘gold-standard’ techniques such as colonoscopy and histopathology to ensure an IBD diagnosis in clinical environment. Moreover, non-linear multimodal imaging can measure biomolecular changes in different tissue regions such as crypt and mucosa region, which serve as a predictive marker for IBD severity. To achieve a real-time assessment of IBD severity, an automatic segmentation of the crypt and mucosa regions is needed. In this paper, we semantically segment the crypt and mucosa region using a deep neural network. We utilized the SegNet architecture (Badrinarayanan et al., 2015) and compared its results with a classical machine learning approach. Our trained SegNet mod el achieved an overall F1 score of 0.75. This model outperformed the classical machine learning approach for the segmentation of the crypt and mucosa region in our study.
  • Thumbnail Image
    Item
    Interaction Network Analysis Using Semantic Similarity Based on Translation Embeddings
    (Berlin ; Heidelberg : Springer, 2019) Manzoor Bajwa, Awais; Collarana, Diego; Vidal, Maria-Esther; Acosta, Maribel; Cudré-Mauroux, Philippe; Maleshkova, Maria; Pellegrini, Tassilo; Sack, Harald; Sure-Vetter, York
    Biomedical knowledge graphs such as STITCH, SIDER, and Drugbank provide the basis for the discovery of associations between biomedical entities, e.g., interactions between drugs and targets. Link prediction is a paramount task and represents a building block for supporting knowledge discovery. Although several approaches have been proposed for effectively predicting links, the role of semantics has not been studied in depth. In this work, we tackle the problem of discovering interactions between drugs and targets, and propose SimTransE, a machine learning-based approach that solves this problem effectively. SimTransE relies on translating embeddings to model drug-target interactions and values of similarity across them. Grounded on the vectorial representation of drug-target interactions, SimTransE is able to discover novel drug-target interactions. We empirically study SimTransE using state-of-the-art benchmarks and approaches. Experimental results suggest that SimTransE is competitive with the state of the art, representing, thus, an effective alternative for knowledge discovery in the biomedical domain.
  • Thumbnail Image
    Item
    Hi Doppelgänger: Towards Detecting Manipulation in News Comments
    (New York City : Association for Computing Machinery, 2019) Pennekamp, Jan; Henze, Martin; Hohlfeld, Oliver; Panchenko, Andriy
    Public opinion manipulation is a serious threat to society, potentially influencing elections and the political situation even in established democracies. The prevalence of online media and the opportunity for users to express opinions in comments magnifies the problem. Governments, organizations, and companies can exploit this situation for biasing opinions. Typically, they deploy a large number of pseudonyms to create an impression of a crowd that supports specific opinions. Side channel information (such as IP addresses or identities of browsers) often allows a reliable detection of pseudonyms managed by a single person. However, while spoofing and anonymizing data that links these accounts is simple, a linking without is very challenging. In this paper, we evaluate whether stylometric features allow a detection of such doppelgängers within comment sections on news articles. To this end, we adapt a state-of-the-art doppelgänger detector to work on small texts (such as comments) and apply it on three popular news sites in two languages. Our results reveal that detecting potential doppelgängers based on linguistics is a promising approach even when no reliable side channel information is available. Preliminary results following an application in the wild shows indications for doppelgängers in real world data sets.
  • Thumbnail Image
    Item
    A Case for Integrated Data Processing in Large-Scale Cyber-Physical Systems
    (Maui, Hawaii : HICSS, 2019) Glebke, René; Henze, Martin; Wehrle, Klaus; Niemietz, Philipp; Trauth, Daniel; Mattfeld, Patrick; Bergs, Thomas; Bui, Tung X.
    Large-scale cyber-physical systems such as manufacturing lines generate vast amounts of data to guarantee precise control of their machinery. Visions such as the Industrial Internet of Things aim at making this data available also to computation systems outside the lines to increase productivity and product quality. However, rising amounts and complexities of data and control decisions push existing infrastructure for data transmission, storage, and processing to its limits. In this paper, we exemplarily study a fine blanking line which can produce up to 6.2 Gbit/s worth of data to showcase the extreme requirements found in modern manufacturing. We consequently propose integrated data processing which keeps inherently local and small-scale tasks close to the processes while at the same time centralizing tasks relying on more complex decision procedures and remote data sources. Our approach thus allows for both maintaining control of field-level processes and leveraging the benefits of “big data” applications.
  • Thumbnail Image
    Item
    Preface
    (Aachen, Germany : RWTH Aachen, 2019) Kaffee, Lucie-Aimee; Endris, Kemele M.; Vidal, Maria-Esther; Comerio, Marco; Sadeghi, Mersedeh; Chaves-Fraga; David, Colpaert Pieter; Kaffee, Lucie Aimée; Endris, Kemele M.; Vidal, María-Esther; Comerio, Marco; Sadeghi, Mersedeh; Chaves-Fraga, David; Colpaert, Pieter
    This volumne presents the proceedings of the 1st International Workshop on Approaches for Making Data Interoperable (AMAR 2019) and 1st International Workshop on Semantics for Transport (Sem4Tra) held in Karlsruhe, Germany, September 9, 2019, co-located with SEMANTiCS 2019. Interoperability of data is an important factor to make transportation data accessible, therefore we present the topics alongside each other in this proceedings.
  • Thumbnail Image
    Item
    Electron beam induced dehydrogenation of MgH2 studied by VEELS
    (Cham : Springer International Publishing AG, 2016) Surrey, Alexander; Schultz, Ludwig; Rellinghaus, Bernd
    Nanosized or nanoconfined hydrides are promising materials for solid-state hydrogen storage. Most of these hydrides, however, degrade fast during the structural characterization utilizing transmission electron microscopy (TEM) upon the irradiation with the imaging electron beam due to radiolysis. We use ball-milled MgH2 as a reference material for in-situ TEM experiments under low-dose conditions to study and quantitatively understand the electron beam-induced dehydrogenation. For this, valence electron energy loss spectroscopy (VEELS) measurements are conducted in a monochromated FEI Titan3 80–300 microscope. From observing the plasmonic absorptions it is found that MgH2 successively converts into Mg upon electron irradiation. The temporal evolution of the spectra is analyzed quantitatively to determine the thickness-dependent, characteristic electron doses for electron energies of both 80 and 300 keV. The measured electron doses can be quantitatively explained by the inelastic scattering of the incident high-energy electrons by the MgH2 plasmon. The obtained insights are also relevant for the TEM characterization of other hydrides.
  • Thumbnail Image
    Item
    Precise Navigation of Small Agricultural Robots in Sensitive Areas with a Smart Plant Camera
    (Basel : MDPI, 2015) Dworak, Volker; Huebner, Michael; Selbeck, Joern
    Most of the relevant technology related to precision agriculture is currently controlled by Global Positioning Systems (GPS) and uploaded map data; however, in sensitive areas with young or expensive plants, small robots are becoming more widely used in exclusive work. These robots must follow the plant lines with centimeter precision to protect plant growth. For cases in which GPS fails, a camera-based solution is often used for navigation because of the system cost and simplicity. The low-cost plant camera presented here generates images in which plants are contrasted against the soil, thus enabling the use of simple cross-correlation functions to establish high-resolution navigation control in the centimeter range. Based on the foresight provided by images from in front of the vehicle, robust vehicle control can be established without any dead time; as a result, off-loading the main robot control and overshooting can be avoided.