In this paper we propose an alternative method for generating topic suggestions to be used in an Open Innovation based expert identification system. An important requirement within this challenge is the identification of topics lateral to a given innovation problem, and use them to broaden the broadcast of the problem without compromising on relevancy. We propose an approach based on DBPedia, using which we can recommend topics on certain proximity in the DBPedia concept graph. We show the important impact of the use of suggested lateral keywords to the raised awareness about the problem in a real problem broadcast.

In this paper we introduce a system that automatically adds different types of non-verbal behavior to a given dialogue script between two virtual embodied agents. It allows us to transform a dialogue in text format into an agent behavior script enriched by eye gaze and conversational gesture behavior. The agents' gaze behavior is informed by theories of human face-to-face gaze behavior. Gestures are generated based on the analysis of linguistic and contextual information of the input text. The resulting annotated dialogue script is then transformed into the Multimodal Presentation Markup Language for 3D agents (MPML3D), which controls the multi-modal behavior of animated life-like agents, including facial and body animation and synthetic speech. Using our system makes it very easy to add appropriate non-verbal behavior to a given dialogue text, a task that would otherwise be very cumbersome and time consuming.