Quebec City, Canada, September 16-18, 2015

(c) 2013, Jonathan Hourez, UMons

(c) 2015, Christoph Beierle, U. Hagen

Photo: Luc Antoine Couturier

(c) 2015, Christoph Beierle, U. Hagen

Photo: Luc Antoine Couturier

Invited Speakers |

We are glad to announce the following confirmed invited speakers:

- Jean-Marie De Koninck, Université Laval, Québec City, Canada
- Lise Getoor, University of California Santa Cruz, USA
- Ronald R. Yager, Machine Intelligence Institute - Iona College, USA

Abstracts of Invited Talks |

In many fields of mathematics, the set of known results is very thin compared with the set of conjectures and hypothesis which have not yet been proved. Particularly remarkable is the hollow universe that sometimes seems to separate the world of the known from what we believe to be reality. Through various examples from number theory, we will attempt here to explore that hollow universe separating these two worlds.

One of the challenges in big data analytics is to efficiently learn and reason collectively about extremely large, heterogeneous, incomplete, noisy interlinked data. Collective reasoning requires the ability to exploit both the logical and relational structure in the data and the probabilistic dependencies. In this talk I will overview our recent work on probabilistic soft logic (PSL), a framework for collective, probabilistic reasoning in relational domains. PSL is able to reason holistically about both entity attributes and relationships among the entities. The underlying mathematical framework, which we refer to as a hinge-loss Markov random field, supports extremely efficient, exact inference. This family of graphical models captures logic-like dependencies with convex hinge-loss potentials. I will survey applications of PSL to diverse problems ranging from information extraction to computational social science. Our recent results show that by building on state-of-the-art optimization methods in a distributed implementation, we can solve large-scale problems with millions of random variables orders of magnitude faster than existing approaches.

The Internet has provided for a rapid growth of computer mediated social networks and other social interactions. One focus here is to discuss how to enrich the domain of social network modeling by introducing ideas from fuzzy sets and related intelligent technologies. We approach this extension in a number of ways. One is with the introduction of fuzzy graphs representing the networks. This allows a generalization of the types of connection between nodes in a network. A second and perhaps more interesting extension is the use of the fuzzy set based paradigm of computing with words to provide a bridge between a human network analyst's linguistic description of social network concepts and the formal model of the network. We also will describe some methods for sharing information obtained in these types of networks. We be used for computer mediated group decision making.