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SUM 2019: the 13th international conference on Scalable Uncertainty Management

The thirteenth international conference on Scalable Uncertainty Management (SUM 2019) will be held in Compiègne (France), on December 16-18, 2019.

The SUM conferences are annual events which gather researchers interested in dealing with imperfect information, in a wide range of fields such as artificial intelligence, databases, information retrieval, machine learning, and risk analysis, with the aim of fostering collaboration and cross-fertilization of ideas from different communities.

An originality of the SUM conferences is the care for dedicating a large space to tutorials about a wide range of topics related to uncertainty management. Each tutorial provides a 45-minute survey of one of the research areas in the scope of the conference.

Topics of interest

The scope of the conference covers a wide range of topics related to the management of large amounts of information, in particular information of a complex kind, uncertain, incomplete, or inconsistent. 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 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

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

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

Risk analysis

  • Aleatory vs. epistemic uncertainty
  • Uncertainty elicitation methods
  • Uncertainty propagation methods
  • Decision analysis methods
  • Tools for synthesizing results