Apologies if you receive multiple copies.
We encourage PC members to consider submitting papers and kindly ask them to
forward this call to interested parties.
**** We want to reassure authors that we are monitoring the coronavirus
situation and exploring contingency plans for a virtual conference should there
a physical conference not be possible. Regardless, the proceedings will go
ahead and will be published this year. ****
===========================================================================================
Call for Papers
===========================================================================================
The 14th International Conference on Scalable Uncertainty Management (SUM 2020)
will be held in Bolzano, Italy from September 23-25, 2020.
See:https://sum2020.inf.unibz.it/
The conference will be taking places as part of the Bolzano Summer of Knowledge event, see:https://summerofknowledge.inf.unibz.it/
===========================================================================================
Description
===========================================================================================
Established in 2007, the SUM conferences are annual events which aim to gather
researchers with a common interest in managing and analyzing imperfect
information from a wide range of fields, such as Artificial Intelligence and
Machine Learning, Databases, Information Retrieval and Data Mining, the
Semantic Web and Risk Analysis, and with the aim of fostering collaboration and
cross-fertilization of ideas from the different communities. An originality of
the SUM conferences is their care for dedicating a large space of their program
to tutorials covering a wide range of topics related to uncertainty management.
Each tutorial provides a survey of one of the research areas in the scope of
the conference.
===========================================================================================
Topics of Interest
===========================================================================================
We solicit papers on the management of large amounts of complex kinds of uncertain, incomplete, or inconsistent information. We are particularly interested in papers that focus on bridging gaps, for instance between different communities, between numerical and symbolic approaches, or between theory and practice. Topics of interest include (but are not limited to):
Imperfect information in databases
- Methods for modeling, indexing, and querying uncertain databases
- Top-k queries, skyline query processing, and ranking
- Approximate, fuzzy query processing
- Uncertainty in data integration and exchange
- Uncertainty and imprecision in geographic information systems
- Probabilistic databases and possibilistic databases?
- Data provenance and trust
- Data summarization
- Very large datasets
Imperfect information in information retrieval and semantic web applications
- Approximate schema and ontology matching
- Uncertainty in description logics and logic programming
- Learning to rank, personalization, and user preferences
- Probabilistic language models
- Combining vector-space models with symbolic representations
- Inductive reasoning for the semantic web
Imperfect information in artificial intelligence
- Statistical relational learning, graphical models, probabilistic inference
Argumentation, defeasible reasoning, belief revision
- Weighted logics for managing uncertainty
- Reasoning with imprecise probability, Dempster-Shafer theory, possibility
theory
- Approximate reasoning, similarity-based reasoning, analogical reasoning
- Planning under uncertainty, reasoning about actions, spatial and temporal
reasoning
- Incomplete preference specifications
- Learning from data
Risk analysis
- Aleatory vs. epistemic uncertainty
- Uncertainty elicitation methods
- Uncertainty propagation methods
- Decision analysis methods
- Tools for synthesizing results
===========================================================================================
Submission Guidelines
===========================================================================================
SUM 2020 solicits original papers in the following three categories:
- Long papers (14 pages): technical papers reporting original research or survey papers
- Short papers (8 pages): papers reporting promising work-in-progress, system
descriptions, position papers on controversial issues, or survey papers
providing a synthesis of some current research trends
- Extended abstracts (2 pages) of recently published work in a relevant journal
or top-tier conference
All SUM submissions must be formatted according to the LNCS/LNAI guidelines:https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines
Papers should be submitted via EasyChair:https://easychair.org/conferences/?conf=sum2020
===========================================================================================
Dates
===========================================================================================
All Deadlines are 23:59 Central European Time.
Submission deadline: May 4th, 2020
Notification: June 17th, 2020
Camera-ready copies due: July 1st, 2020
Conference: Sept. 23rd-25th, 2020
===========================================================================================
Publication
===========================================================================================
Accepted long (14 pages) and short papers (8 pages) will be published by
Springer in the Lecture Notes in Artificial Intelligence (LNAI) series. Authors
of an accepted long or short paper will be expected to sign copyright release
forms, and one author is expected to give a presentation at the conference.
Authors of accepted abstracts (2 pages) will be expected to present their work
during the conference, but the extended abstracts will not be published in the
LNCS/LNAI proceedings (they will be made available in a separate booklet)
===========================================================================================
Organization
===========================================================================================
Jesse Davis (KU Leuven), PC Co Chair
Karim Tabia (Artois University), PC Co Chair
Rafael Penaloza Nyssen (University of Milano-Bicocca), Local Chair
===========================================================================================
PC Members
===========================================================================================
Alessandro Antonucci, IDSIA
Nahla Ben Amor, Institut Supérieur de Gestion de Tunis
Salem Benferhat, Cril CNRS UMR8188, Université d’Artois
Leopoldo Bertossi, Adolfo Ibáñez University (Santiago, Chile)
Fernando Bobillo, University of Zaragoza
Imen Boukhris, LARODEC – Université de Tunis- ISG Tunis
Davide Ciucci, Università di Milano-Bicocca
Thierry Denoeux, Université de Technologie de Compiègne
Sébastien Destercke, CNRS UMR Heudiasyc
Zied Elouedi, Institut Supérieur de Gestion de Tunis
Rainer Gemulla, Universität Mannheim
Lluis Godo, Artificial Intelligence Research Institute, IIIA – CSIC
John Grant, Towson University
Manuel Gómez-Olmedo, University of Granada
Arjen Hommersom, Open University of the Netherlands
Angelika Kimmig, Cardiff University
Eric Lefevre, Université d’Artois
Philippe Leray, LS2N/DUKe – Nantes University
Sebastian Link, The University of Auckland
Thomas Lukasiewicz, University of Oxford
Silviu Maniu, Universite Paris-Sud
Serafin Moral, University of Granada
Francesco Parisi, DIMES – University of Calabria
Nico Potyka, Universitaet Osnabrueck, IKW
Henri Prade, IRIT – CNRS
Andrea Pugliese, University of Calabria
Benjamin Quost, HeuDiaSyC laboratory, University of Technology of Compiègne
Steven Schockaert, Cardiff University
Umberto Straccia, ISTI-CNR
Andrea Tettamanzi, Univ. Nice Sophia Antipolis
Matthias Thimm, Universität Koblenz-Landau
Barbara Vantaggi, Universita’ La Sapienza of Rome
Maurice van Keulen, University of Twente
--
Prof. dr. Jesse Davis
Machine Learning Group & DTAI Sports Analytics Lab
Department of Computer Science
KU Leuven
Belgium
@jessejdavis1
https://people.cs.kuleuven.be/~jesse.davis/
https://dtai.cs.kuleuven.be/sports/
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