The popularity of Web 2.0, characterized by a proliferation of social media sites, and Web 3.0, with more richly semantically annotated objects and relationships, brings to light a variety of important prediction, ranking, and extraction tasks. The input to these tasks is often best seen as a (noisy) multi-relational graph, such as the graph of the Web itself; the click graph, defined by user interactions with Web sites; and the social graph, defined by friendships and affiliations on social media sites.
This tutorial will provide an overview of statistical relational learning and inference techniques, motivating and illustrating them using web and social media applications. We will start by briefly surveying some of the sources of statistical and relational information on the web and in social media and will then dedicate most of the tutorial time to an introduction to representations and techniques for learning and reasoning with multi-relational information, viewing them through the lens of web and social media domains. We will end with a discussion of current trends and related fields, such as privacy in social networks and probabilistic databases.