Gossip Galore: A Conversational Web Agent for Collecting and Sharing Pop Trivia

This paper presents a novel approach to a self-learning agent who collects and learns new knowledge from the web and exchanges her knowledge via dialogues with the users. The application domain is gossip about celebrities in the music world. The agent can inform herself and update the acquired knowledge by observing the web. Fans of musicians can ask for gossip information about stars, bands or people and groups related to them. This agent is built on top of information extraction, web mining, question answering and dialogue system technologies. The minimally supervised machine learning method for relation extraction gives the agent the capability to learn and update knowledge constantly from the web. The extracted relations are structured and linked with each other. Data mining is applied to the learned data to induce the social network among the artists and related people. The knowledge-intensive question answering technology enhanced by domain-specific inference and active memory allows the agent to have vivid and interactive conversations with users by utilizing natural language processing. Users can freely formulate their questions within the gossip data domain and access the answers in different ways: textual response, graph-based visualization of the related concepts and speech output.