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        <article-title>Proceedings of the 15th International Workshop on Neural-Symbolic Learning and Reasoning (NeSy)</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <string-name>Artur d'Avila Garcez</string-name>
          <email>a.garcez@city.ac.uk</email>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Ernesto Jiménez-Ruiz</string-name>
          <email>ernesto.jimenez-ruiz@city.ac.uk</email>
          <xref ref-type="aff" rid="aff0">0</xref>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
        <aff id="aff0">
          <label>0</label>
          <institution>City, University of London</institution>
          ,
          <country country="UK">UK</country>
        </aff>
        <aff id="aff1">
          <label>1</label>
          <institution>SIRIUS, University of Oslo</institution>
          ,
          <country country="NO">Norway</country>
        </aff>
      </contrib-group>
      <abstract>
        <p>The NeSy workshop series celebrates the integration of neural and symbolic thinking, technologies, theories and techniques of Artificial Intelligence and Machine Learning. NeSy is the annual meeting of the Neural-Symbolic Learning and Reasoning Association.1. Neural networks and statistical Machine Learning have obtained industrial relevance in a number of areas from retail to healthcare, achieving state-of-the-art performance at language modelling, speech recognition, graph analytics, image, video and sensor data analysis. Symbolic AI, on the other hand, is challenged by such unstructured data, but is recognised as being in principle transparent, in that reasoned facts from knowledge-bases can be inspected to interpret how decisions follow from input. Neural and symbolic methods also contrast in the problems that they excel at: scene recognition from images appears to be a problem still outside the capabilities of symbolic systems, for example, while neural networks are not yet suficient for industrial-strength complex planning scenarios and deductive reasoning tasks.</p>
      </abstract>
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      <title>-</title>
      <p>CEUR
Workshop
Proceedings
Program Committee Chairs</p>
    </sec>
    <sec id="sec-2">
      <title>Artur d’Avila Garcez</title>
      <p>Natalia Díaz Rodríguez
Ernesto Jiménez Ruiz
Dagmar Groman
Freddy Lecue</p>
      <p>Derek Doran
Local Organization</p>
    </sec>
    <sec id="sec-3">
      <title>Nikos Katzouris</title>
      <p>Program Committee</p>
    </sec>
    <sec id="sec-4">
      <title>Asan Agibetov</title>
      <p>Vito Walter Anelli
Jiaoyan Chen
Bernardo Cuenca Grau
Vincenzo Cutrona
Elvira Domínguez
Ivan Donadello
Monireh Ebrahimi
Vasilis Efthymiou
Eleonora Giunchiglia
Pascal Hitzler
Andreas Holzinger
Stefen Hölldobler
Kristian Kersting
Luis Lamb
Thomas Lukasiewicz
Carlos Maestre
Pasquale Minervini
Summaya Mumtaz
Erik Bryhn
Heiko Paulheim
Catia Pesquita
Alina Petrova
Md Kamruzzaman Sarker
Michael Spranger
Kavitha Srinivas
Andreas Theodorou
Frank Van Harmelen
City, University of London
ENSTA Paris Institut Polytechnique Paris
City, University of London
University of Vienna
CortAIx Thales
Wright State University</p>
    </sec>
    <sec id="sec-5">
      <title>NCSR Demokritos</title>
    </sec>
    <sec id="sec-6">
      <title>Aaron Eberhart Joshua Schwartz Lu Zhou Abdolghani Ebrahimi</title>
      <p>Acknowledgements</p>
      <p>Kansas State University
Kansas State University
Kansas State University</p>
      <p>Northwestern University
We thank all members of the program committee, additional reviewers, authors and local
organizers for their eforts. We would also like to acknowledge that the work of the workshop
organisers was greatly simplified by using the EasyChair conference management system and
the CEUR open-access publication service.</p>
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