The proliferation of knowledge-sharing communities such as Wikipedia and the progress in scalable information extraction from Web and text sources have enabled the automatic construction of very large knowledge bases. Endeavors of this kind include projects such as DBpedia, Freebase, KnowItAll, ReadTheWeb, and YAGO. These projects provide automatically constructed knowledge bases of facts about named entities, their semantic classes, and their mutual relationships. They contain millions of entities and hundreds of millions of facts about them. Such world knowledge in turn enables cognitive applications and knowledge-centric services like disambiguating natural-language text, semantic search for entities and relations in Web and enterprise data, and entity-oriented analytics over unstructured contents. Prominent examples of how knowledge bases can be harnessed include the Google Knowledge Graph and the IBM Watson question answering system.
This tutorial gives an overview on state-of-the-art methods for the automatic construction of large knowledge bases and harnessing them for data and text analytics. It covers both big-data methods for building knowledge bases and knowledge bases being assets for big-data applications. The tutorial also points out challenges and research opportunities.
The tutorial takes place at the VLDB 2014 conference in Hangzhou/China on Wednesday, September 3rd, 10:30-12:15 and 13:45-15:15, in the Bauhinia 1 room.