Now showing items 349-368 of 544

    • Quality-driven resource-adaptive data stream mining? 

      Karnstedt, Marcel (IEEE / ACM, 2011-01)
      Data streams have become ubiquitous in recent years and are handled on a variety of platforms, ranging from dedicated high-end servers to battery-powered mobile sensors. Data stream processing is therefore required to work ...
    • Quantified equilibrium logic and hybrid rules 

      Polleres, Axel (Springer, 2007)
      In the ongoing discussion about combining rules and Ontologies on the Semantic Web a recurring issue is how to combine first-order classical logic with nonmonotonic rule languages. Whereas several modular approaches to de- ...
    • Query-driven video event processing for the internet of multimedia things 

      Yadav, Piyush; Salwala, Dhaval; Arruda Pontes, Felipe; Dhingra, Praneet; Curry, Edward (VLDB Endowment, 2021-08)
      Advances in Deep Neural Network (DNN) techniques have revolutionized video analytics and unlocked the potential for querying and mining video event patterns. This paper details GNOSIS, an event processing platform to ...
    • Querying over Federated SPARQL Endpoints - A State of the Art Survey 

      Rakhmawati, Nur; Umbrich, Jürgen; Karnstedt, Marcel; Hasnain, Ali; Hausenblas, Michael (2013)
      The increasing amount of Linked Data and its inherent distributed nature have attracted significant attention throughout the research community and amongst practitioners to search data, in the past years. Inspired by ...
    • Querying Phenotype-Genotype Associations across Multiple Knowledge Bases using Semantic Web Technologies 

      Iqbal, Aftab (2013)
      Biomedical and genomic data are inherently heterogeneous and their recent proliferation over the Web has demanded innovative querying methods to help domain experts in their clinical and research studies. In this paper we ...
    • Querying web polystores 

      Khan, Ya; Zimmermann, Antoine; Jha, AlokKumar; Rebholz-Schuhmann, Dietrich; Sahay, Ratnesh (IEEE, 2017-12-11)
      The database, semantic web, and linked data communities have proposed solutions that federate queries over multiple data sources using a single data model. Nowadays, the data retrieval requirements originating from versatile ...
    • Random Indexing Explained with High Probability 

      QasemiZadeh, Behrang (2015)
      Random indexing (RI) is an incremental method for constructing a vector space model (VSM) with a reduced dimensionality. Previously, the method has been justified using the mathematical framework of Kanerva's sparse ...
    • Random indexing revisited 

      QasemiZadeh, Behrang (Springer, 2015-05-17)
      Random indexing is a method for constructing vector spaces at a reduced dimensionality. Previously, the method has been proposed using Kanerva's sparse distributed memory model. Although intuitively plausible, this ...
    • Random Manhattan Indexing 

      QasemiZadeh, Behrang; Handschuh, Siegfried (2014)
      Vector space models (VSMs) are mathematically well-defined frameworks that have been widely used in text processing. In these models, high-dimensional, often sparse vectors represent text units. In an application, the ...
    • Random Manhattan Integer Indexing: Incremental L1 Normed Vector Space Construction 

      QasemiZadeh, Behrang; Handschuh, Siegfried (2014)
      Vector space models (VSMs) are mathematically well-defined frameworks that have been widely used in the distributional approaches to semantics. In VSMs, high-dimensional vectors represent linguistic entities. In an ...
    • A random walk model for entity relatedness 

      Torres-Tramón, Pablo; Hayes, Conor (Springer Verlag, 2018-10-31)
      Semantic relatedness is a critical measure for a wide variety of applications nowadays. Numerous models, including path-based, have been proposed for this task with great success in many applications during the last few ...
    • Rapid Competence Development in Serious Games Using Case-Based Reasoning and Threshold Concepts 

      Hulpus, Ioana; Fradinho, Manuel; Hayes, Conor (2010)
      A major challenge in todays fast pace world is the acquisition of competence in a timely and efficient manner, whilst keeping the individual highly motivated. This paper presents a novel based on the use of serious games ...
    • RCE-NN: a five-stage pipeline to execute neural networks (CNNs) on resource-constrained IoT edge devices 

      Sudharsan, Bharath; Breslin, John G.; Ali, Muhammad Intizar (Association for Computing Machinery (ACM), 2020-10-06)
      Microcontroller Units (MCUs) in edge devices are resource constrained due to their limited memory footprint, fewer computation cores, and low clock speeds. These limitations constrain one from deploying and executing machine ...
    • RDFS & OWL Reasoning for Linked Data 

      Hogan, Aidan; Delbru, Renaud; Umbrich, Jurgen (na, 2013)
      Linked Data promises that a large portion of Web Data will be usable as one big interlinked RDF database against which structured queries can be answered. In this lecture we will show how reasoning - using RDF Schema (RDFS) ...
    • Re-coding Black Mirror Chairs' Welcome & Organization 

      Troullinou, Pinelopi; d’Aquin, Mathieu; Tiddi, Ilaria (ACM, 2018-04-23)
      This volume of proceedings presents the papers from the 2nd edition of the interdisciplinary workshop Re-coding Black Mirror, held on April 24, 2018 in Lyon, France and co-located with The WEB Conference (WWW2018). ...
    • ReConRank: A Scalable Ranking Method for Semantic Web Data with Context 

      Hogan, Aidan; Harth, Andreas; Decker, Stefan (2006)
      We present an approach that adapts the well-known PageRank/HITS algorithms to Semantic Web data. Our method combines ranks from the RDF graph with ranks from the context graph, i.e. data sources and their linkage. We present ...
    • Reconstruction of Threaded Conversations in Online Discussion Forums 

      Aumayr, Erik; Jeffrey, Chan; Hayes, Conor (Fifth International AAAI Conference on Weblogs and Social Media, 2011-07-18)
      [no abstract available]
    • Regularizing knowledge graph embeddings via equivalence and inversion axioms 

      Minervini, Pasquale; Costabello, Luca; Muñoz, Emir; Nováček, Vít; Vandenbussche, Pierre-Yves (Springer Verlag, 2017-12-30)
      Learning embeddings of entities and relations using neural architectures is an effective method of performing statistical learning on large-scale relational data, such as knowledge graphs. In this paper, we consider the ...
    • Relaxing the Basic KR&R Principles to Meet the Emergent Semantic Web 

      Nováček, Vít (CEUR-WS, 2008)
      The paper argues for an alternative, empirical (instead of analytical) approach to a Semantic Web-ready KR&R, motivated by the so far largely untackled need for a feasible emergent content processing.