In addition, harnessing Semantic-Web-style ontologies and reaching into Deep-Web sources can contribute towards a grand vision of turning the Web into a comprehensive knowledge base that can be efficiently searched with high precision.
This talk presents ongoing research at the Max-Planck Institute for Informatics towards this objective, centered around the YAGO knowledge base and the NAGA search engine. YAGO is a large collection of entities and relational facts that are harvested from Wikipedia and WordNet with high accuracy and reconciled into a consistent RDF-style "semantic" graph. NAGA provides graph-template-based search over this data, with powerful ranking capabilities based on a statistical language model for graphs.
Advanced queries and the need for ranking approximate matches pose efficiency and scalability challenges that are addressed by algorithmic and indexing techniques.