EntityCube is a research prototype and is a test bed for exploring entity mining and search technologies, which automatically summarizes the Web for entities (such as people, locations and organizations) with a substantial presence.
The need for collecting and understanding Web information about a real-world entity (such as a person or a product) is currently fulfilled manually through search engines. However, information about a single entity might appear in thousands of Web pages. Even if a search engine could find all the relevant Web pages about an entity, the user would need to sift through all these pages to get a complete view of the entity. EntityCube generates summaries of Web entities from billions of public Web pages that contain information about people, locations, and organizations, and allows for exploration of their relationships. For example, users can use EntityCube to find an automatically generated biography page and social-network graph for a person, and use it to discover a relationship path between two people.
Renlifang is the Chinese version of EntityCube. Renlifang currently has millions of daily page-views during the peak days, and its twenty questions game has become a very popular crowdsourcing game in China for us to collect knowledge about entities and to train our entity mining algorithms through user interaction.
By our entity mining and search technologies, we have created the Microsoft academic search engine (code name: Libra) to facilitate the exchange of ideas and communications between academic communities. A user entering search queries in Libra can retrieve relevant information on academic papers, scientists, conferences, journals, and interest groups thus generates more accurate, relevant, and efficient results in comparison to document-level ranking.