Harvesting, Searching, and Ranking Knowledge from the Web

Опубликовано 6 сентября 2016, 17:23

There is a trend to advance the functionality of search engines to a more expressive semantic level. This is enabled by employing large-scale information extraction of entities and relationships from semistructured as well as natural-language Web sources. 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. This is joint work with Georgiana Ifrim, Gjergji Kasneci, Maya Ramanath, and Fabian Suchanek.