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 2018 solicits papers in the following three categories:

Long papers: technical papers reporting original research or survey papers
Short papers: 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 of recently published work in a relevant journal or top-tier conference

Accepted long and short papers will be published by Springer in the Lecture Notes in Artificial Intelligence 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 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).

Regular research papers should be at most 14 pages (including references, figures, and tables). Short papers should be between 4 and 7 pages. Extended abstracts should be at most 2 pages and should reference the originally published work.

Submissions must be formatted according to Springer’s guidelines for LNCS authors, which can be found at http://www.springer.de/comp/lncs/authors.html. Each paper is to be submitted electronically as a single PDF file through EasyChair. Papers not respecting the formatting instructions or page limits may be rejected without review.

Except for extended abstracts, submissions must be unpublished and must not be under submission elsewhere. All submitted papers will be reviewed by the Program Committee on the basis of technical quality, relevance, significance, and clarity.
Important dates

Submissions: April 1, 2018
Notification: May 20, 2018
Camera-ready copies: June 10, 2018
Conference: October 3-5, 2018