Microsoft Technology Licensing, LLC

Ying Tu

Franco Salvetti

Srinivas Vadrevu

David D Ahn

Architecture that addresses at least the problem of ranking the relevant attributes for a given entity within the context of a structured knowledge base (SKB). The architecture utilizes the attribute, entity type statistics, and the taxonomy of the attributes to consistently and efficiently rank attributes for each and every type of entity in the SKB. Using the SKB, intermediate features are computed, including the importance or popularity each entity type for every entity, inverse document frequency (IDF) computation for each attribute on a global basis, IDF computation for entity types, and the popularity of attributes for each entity type. The intermediate features are aggregated to obtain a final feature set, which can be used in combination with human judgments to train a machine learned classifier model to produce and predict a relevance score for a given entity and each of its attributes. The attributes are ranked for each entity using this score.

Ranking relevant attributes of entity in structured knowledge base

Answers are provided to users in response to queries as a supplement to any responsive documents. Query formats for entity and attribute combinations are identified. The query formats can be substituted with entity and attribute combinations that have a corresponding attribute value to form a list of answered queries. The attribute value corresponding to an answered query can be provided when a query is received that matches an answered query.

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Ying Tu