Abstract: An approach is described for performing a name search using a name search operation and a ranking operation. The name search operation may take text as input and apply a fuzzy matching operation and a lookup operation to generate a collection of candidate names with respective probability scores. In other cases, speech or handwriting recognition may generate the collection of candidate names and probability scores. The ranking operation may then rank these candidate names using a ranking function. The ranking function may rank the candidate names based on the probability scores associated with the names and at least one other factor. One such factor may reflect whether information provided by a user matches profile information associated with a candidate name under consideration. Another factor may reflect an extent of a nexus between the user and a person associated with the candidate name. Other types of factors can be used.

Abstract: An approach is described for performing a name search using a name search operation and a ranking operation. The name search operation may take text as input and apply a fuzzy matching operation and a lookup operation to generate a collection of candidate names with respective probability scores. In other cases, speech or handwriting recognition may generate the collection of candidate names and probability scores. The ranking operation may then rank these candidate names using a ranking function. The ranking function may rank the candidate names based on the probability scores associated with the names and at least one other factor. One such factor may reflect whether information provided by a user matches profile information associated with a candidate name under consideration. Another factor may reflect an extent of a nexus between the user and a person associated with the candidate name. Other types of factors can be used.

Abstract: Embodiments are configured to provide information relevant to individuals of interest to a searching user. In an embodiment, a method includes identifying relevant individuals of a network using a relevance model that includes the use of a number of managed properties and ranking features to identify relevant individuals of a defined network. The relevance model of one embodiment is defined by a schema that includes a textual matching ranking feature, social distance ranking feature, a levels to top ranking feature, and a proximity ranking feature.

Abstract: An approach is described for performing a name search using a name search operation and a ranking operation. The name search operation may take text as input and apply a fuzzy matching operation and a lookup operation to generate a collection of candidate names with respective probability scores. In other cases, speech or handwriting recognition may generate the collection of candidate names and probability scores. The ranking operation may then rank these candidate names using a ranking function. The ranking function may rank the candidate names based on the probability scores associated with the names and at least one other factor. One such factor may reflect whether information provided by a user matches profile information associated with a candidate name under consideration. Another factor may reflect an extent of a nexus between the user and a person associated with the candidate name. Other types of factors can be used.

Abstract: Embodiments are configured to provide information relevant to individuals of interest to a searching user. In an embodiment, a method includes identifying relevant individuals of a network using a relevance model that includes the use of a number of managed properties and ranking features to identify relevant individuals of a defined network. The relevance model of one embodiment is defined by a schema that includes a textual matching ranking feature, social distance ranking feature, a levels to top ranking feature, and a proximity ranking feature.

Abstract: Embodiments are configured to provide information relevant to individuals of interest to a searching user. In an embodiment, a method includes identifying relevant individuals of a network using a relevance model that includes the use of a number of managed properties and ranking features to identify relevant individuals of a defined network. The relevance model of one embodiment is defined by a schema that includes a textual matching ranking feature, social distance ranking feature, a levels to top ranking feature, and a proximity ranking feature.

Abstract: Embodiments are configured to provide information relevant to individuals of interest to a searching user. In an embodiment, a method includes identifying relevant individuals of a network using a relevance model that includes the use of a number of managed properties and ranking features to identify relevant individuals of a defined network. The relevance model of one embodiment is defined by a schema that includes a textual matching ranking feature, social distance ranking feature, a levels to top ranking feature, and a proximity ranking feature.

Abstract: An approach is described for performing a name search using a name search operation and a ranking operation. The name search operation may take text as input and apply a fuzzy matching operation and a lookup operation to generate a collection of candidate names with respective probability scores. In other cases, speech or handwriting recognition may generate the collection of candidate names and probability scores. The ranking operation may then rank these candidate names using a ranking function. The ranking function may rank the candidate names based on the probability scores associated with the names and at least one other factor. One such factor may reflect whether information provided by a user matches profile information associated with a candidate name under consideration. Another factor may reflect an extent of a nexus between the user and a person associated with the candidate name. Other types of factors can be used.