Abstract: A method for merging two taxonomies is disclosed. Top levels of first and second taxonomies are merged. For the second taxonomy nodes are evaluated and selectively merged with nodes of the first taxonomy according to comparison of scores for these nodes with a threshold. The score for a node of the first taxonomy is a combination of one or more of a lineage quality score, Jaccard distance, string edit distance, and category depth score. After an iteration, mergings between nodes of the first and second taxonomies may be reversed if child nodes of the merged nodes were not likewise merged. Iterations may be repeated until no nodes are merged in an iteration.

Abstract: Disclosed are methods for inferring interests of a user based on declared interests of the user. Text for which a user has expressed interest, e.g. “liked” is evaluated to identify at least one principal concept. A principal article for the principal concept is located in a taxonomy and the link structure of the taxonomy analyzed to identify candidate articles related to the principal article. The candidate articles are scored according to a plurality of metrics and these scored are weighted and combined for a final score. Candidate articles are selected for the score and recommendations are generated and recommendations generated based on the concepts of the selected candidate articles.

Abstract: Disclosed are methods for returning to a user an answer to the question “what is <string>.” Concepts and classes to which the concepts belong are determined from a corpus, such as taxonomy. The concepts are mapped to categories according to the structure of the taxonomy. Homonyms for words are collected and scored according to likeliness of use. Concept vectors are assembled for the identified concepts based on articles in the corpus and social media usage. Words are evaluated for generic-ness and a generic score is associated therewith. In responding to a query, the generic-ness of the terms of the query is evaluated and additional context solicited if the terms are generic. Candidate homonym concepts for a string in the query are selected according to context vectors for the homonym concepts. One or more homonym concepts are selected and the one or more categories corresponding to these concepts are returned.

Abstract: Disclosed are methods for returning to a user an answer to the question “what is <string>.” Concepts and classes to which the concepts belong are determined from a corpus, such as taxonomy. The concepts are mapped to categories according to the structure of the taxonomy. Homonyms for words are collected and scored according to likeliness of use. Concept vectors are assembled for the identified concepts based on articles in the corpus and social media usage. Words are evaluated for generic-ness and a generic score is associated therewith. In responding to a query, the generic-ness of the terms of the query is evaluated and additional context solicited if the terms are generic. Candidate homonym concepts for a string in the query are selected according to context vectors for the homonym concepts. One or more homonym concepts are selected and the one or more categories corresponding to these concepts are returned.

Abstract: Methods are disclosed for converting a directed graph to a taxonomy using guidelines from a user. An initial tree is output from a first pruning step in which subtree preferences (and other weights) are applied to preserve or remove paths from a node to one or more levels of descendent nodes. Subtree preferences (and infoboxes) may specify rules for automatically generating recommendations during application to nodes. In a second pruning step, the directed graph is again processed with additional weightings applied to edges in the graph in accordance with the recommendations. The recommendations may be human defined. Recommendations may specify a recommended ancestor for a particular node and may include a weighting to be applied to the recommendation itself, if there are multiple conflicting recommendations for the same node. Recommendations may also specify what standard weight to apply to the edge of the best parent.

Abstract: Determining one or more concepts associated with a query is disclosed. A query is received. A list of concepts and associated scores is received. The concepts fit within a concept hierarchy. A density function is used to evaluate the received concepts. One or more concepts are associated with the query based at least in part on the results of the density function.

Abstract: Disclosed are methods for returning to a user an answer to the question “what is <string>.” Concepts and classes to which the concepts belong are determined from a corpus, such as taxonomy. The concepts are mapped to categories according to the structure of the taxonomy. Homonyms for words are collected and scored according to likeliness of use. Concept vectors are assembled for the identified concepts based on articles in the corpus and social media usage. Words are evaluated for generic-ness and a generic score is associated therewith. In responding to a query, the generic-ness of the terms of the query is evaluated and additional context solicited if the terms are generic. Candidate homonym concepts for a string in the query are selected according to context vectors for the homonym concepts. One or more homonym concepts are selected and the one or more categories corresponding to these concepts are returned.

Abstract: Exemplary embodiments allow performance of stream computations on real-time data streams using one or more map operations and/or one or more update operations. A map operation is a stream computation in which stream events in one or more real-time data streams are processed in a real-time manner to generate zero, one or more new stream events. An update operation is a stream computation in which stream events in one or more real-time data streams are processed in a real-time manner to create or update one or more static “slate” data structures that are stored in a durable manner.

Abstract: Exemplary embodiments allow performance of stream computations on real-time data streams using one or more map operations and/or one or more update operations. A map operation is a stream computation in which stream events in one or more real-time data streams are processed in a real-time manner to generate zero, one or more new stream events. An update operation is a stream computation in which stream events in one or more real-time data streams are processed in a real-time manner to create or update one or more static “slate” data structures that are stored in a durable manner.

Abstract: Determining one or more concepts associated with a query is disclosed. A query is received. A list of concepts and associated scores is received. The concepts fit within a concept hierarchy. A density function is used to evaluate the received concepts. One or more concepts are associated with the query based at least in part on the results of the density function.

Abstract: Determining one or more concepts associated with a query is disclosed. A query is received. A list of concepts and associated scores is received. The concepts fit within a concept hierarchy. A density function is used to evaluate the received concepts. One or more concepts are associated with the query based at least in part on the results of the density function.

Abstract: Exemplary embodiments provide methods, systems and devices for recommending one or more products for a selected individual. The products may be recommended to a user for gifting to the selected individual. Exemplary embodiments may automatically determine one or more topics of interest to the selected individual. In an exemplary embodiment, semantic analysis may be performed on textual content associated with the selected individual to automatically extract the topics of interest. Exemplary embodiments may use the topics of interest to automatically determine one or more products that are likely to be of interest to the selected individual.

Abstract: Systems and methods are provided for processing a data stream in real time to identify connections between one or more social elements social elements and aggregating and storing the identified connections over a rolling time window. Social elements may be stored as nodes and connection between social elements may be stored as labeled connections between nodes.

Abstract: Exemplary embodiments allow performance of stream computations on real-time data streams using one or more map operations and/or one or more update operations. A map operation is a stream computation in which stream events in one or more real-time data streams are processed in a real-time manner to generate zero, one or more new stream events. An update operation is a stream computation in which stream events in one or more real-time data streams are processed in a real-time manner to create or update one or more static “slate” data structures that are stored in a durable manner.

Abstract: Determining one or more concepts associated with a query is disclosed. A query is received. A list of concepts and associated scores is received. The concepts fit within a concept hierarchy. A density function is used to evaluate the received concepts. One or more concepts are associated with the query based at least in part on the results of the density function.