Einstein: Physicist or vegetarian? Summarizing semantic type graphs for knowledge discovery

The Web and, in particular, knowledge-sharing communities such as Wikipedia contain a huge amount of information encompassing disparate and diverse fields. Knowledge bases such as DBpedia or Yago represent the data in a concise and more structured way bearing the potential of bringing database tools to Web Search. The wealth of data, however, poses the challenge of how to retrieve important and valuable information, which is often intertwined with trivial and less important details. This calls for an efficient and automatic summarization method. In this demonstration proposal, we consider the novel problem of summarizing the information related to a given entity, like a person or an organization. To this end, we utilize the rich type graph that knowledge bases provide for each entity, and define the problem of selecting the best cost-restricted subset of types as summary with good coverage of salient properties. We propose a demonstration of our system which allows the user to specify the entity to summarize, an upper bound on the cost of the resulting summary, as well as to browse the knowledge base in a more simple and intuitive manner.