The program of SUM 2014 will feature the following invited talks:
Abstract: In persuasion technologies, a system enters into a dialogue with a user to persuade them to undertake some action. An action might be a mental action such as believing something, or deciding something, or it might be a physical action such as buying something, voting for somebody, eating something, or taking some medicine, or it might be to not do some action such as not buying something, or not eating something, etc. Since a persuasion dialogue normally involves the exchange of arguments, computational models of argument are being developed for modelling persuasion dialogues. In this talk, we will review some of these developments, in particular for handling uncertainty that is inherent in persuasion dialogues, and consider what they offer for argument-based persuasion technologies.
Abstract: Over the past decade Linked Data has emerged as a model for managing distributed data. Methods for analysing data and interlinking it with other datasets become increasingly important due to the growth of data on the web. In this talk, we will cast data analysis as machine learning problem and then look at some ways to approach this problem. In particular, so called refinement operators from inductive logic programming will play a role to order and search the vast space of possible solutions to the machine learning task at hand. I will also show some novel ideas on how those techniques carry over to link discovery. This crucial problem for several large IT companies involves finding methods to determine whether different resources actually refer to the same real world entity.
Abstract: In this talk I will give a brief account to probabilistic databases with a strong bias towards the SPROUT project at Oxford. After a fruitful decade that brought a good understanding of probabilistic data models and efficient query evaluation, the attention of our community to this area is diminishing. Nevertheless, there is new excitement at the horizon! This comes in several flavors, some of which I will highlight in the talk: Cross-fertilization with PL work on probabilistic programming for expressing computation beyond queries and with AI inference engines for probabilistic data integration; also, the availability of ever-larger probabilistic Web data repositories such as Google Knowledge Vault.