Microsoft Technology Licensing, LLC

Linkedin Corporation

Alex Ching Lai

Songtao Guo

Ke Wang

Aarti Kumar

Christopher Matthew Degiere

Baoshi Yan

Xian Li

Jingjing Huang

Chris Degiere

Song Lin

Keith Wai Kit Tsang

Rekha Thakur

In an example embodiment, a member profile corresponding to a member of a social networking service is obtained. Usage information for the member is then obtained, and one or more member metrics are calculated based on the member profile and usage information for the corresponding member. A plurality of features are extracted from the member profile and the one or more member metrics. The plurality of features is inserted into an organization name confidence score model to obtain a confidence score for an organization name in the member profile.

Inferring attributes of organizations using member graph

Methods, systems and computer program products for identifying a relationship between sub-units of an entity are described. The sub-units are segmented into one or more candidate related groups based on one or more general attributes and a pair of the sub-units of the one or more candidate related sub-units is selected. The pair of sub-units is analyzed to determine a relationship between the sub-units and the relationship between the sub-units is identified based on the determined relationship.

Determining an entity's hierarchical relationship via a social graph

In an example embodiment, a web page is obtained using a web page address stored in a first record and is parsed to extract one or more images from the web page along with a first plurality of features for each of the one or more images from the web page. Information about each image of the web page and the extracted first plurality of features for the web page are input into a supervised machine learning classifier to calculate a logo confidence score for each image of the web page, the logo confidence score indicating the probability that the image is an organization logo. In response to a particular image in the web page having a logo confidence score transgressing a first threshold, the particular image is injected into an organization logo field of the first record.

Organizational logo enrichment

In an example embodiment, a fuzzy join operation is performed by, for each pair of records, evaluating a first plurality of features for both records in the pair of records by calculating term frequency-inverse term frequency (TF-IDF) for each token of each field relevant to each feature and based on the calculated TF-IDF for each token of each field relevant to each feature, computing a similarity score based on the similarity function by adding a weight assigned to the TF-IDF for any token that appears in both records. Then a graph data structure is created, having a node for each record in the plurality of records and edges between each of the nodes, except, for each record pair having a similarity score that does not transgress a first threshold, causing no edge between the nodes for the record pair to appear in the graph data structure.

Organizational data enrichment

Techniques are provided for automatically creating online accounts based on digital images, such as digital images of business cards. In one technique, multiple data items that have been extracted from a digital image of a business card are identified. A particular data item is contact information of a user associated with (or identified by) the business card. A verification code is sent, based on the particular data item, to a computing device of the user. The verification code is received from the computing device of the user. In response to receiving the verification code an account is created for the user and the account is modified to include a least some of the multiple data items.

Leveraging digital images of user information in a social network

Techniques for determining a location of an entity are described. A determination module using member profile data may determine the location of the entity. Member profile data can include information about a member's employer and the employer's location. The determination module accesses member profiles from a social network. Additionally, the determination module may create a subgroup of entity-related member profiles from the accessed member profiles. The entity-related member profiles can be associated with a specific entity. Furthermore, the determination module may determine a suggested location for the specific entity based on location information derived from the related member profiles. Moreover, the determination module may calculate a connection density for the specific entity based on connections associated with the entity-related member profiles. Subsequently, the determination module may determine that the suggested location is a physical location when the calculated connection density is above a pre-determined threshold value.

Surfacing an entity's physical locations via social graph

Techniques for determining a location of an entity are described. A determination module using member profile data may determine the location of the entity. Member profile data can include information about a member's employer and the employer's location. The module accesses member profiles from a social network. Additionally, the module may create a subgroup of entity-related member profiles from the accessed member profiles. The entity-related member profiles can be associated with a specific entity. Furthermore, the module may determine a suggested location for the specific entity based on location information derived from the related member profiles. Moreover, the module may calculate a connection density for the specific entity based on connections associated with the entity-related member profiles. Subsequently, the module may determine that the suggested location is a physical location when the calculated connection density is above a pre-determined threshold value.

Calculating an entity'S location size via social graph

In an example embodiment, a web page is obtained using a web page address stored in a first record and is parsed to extract one or more images from the web page along with a second plurality of features for each of the one or more images from the web page. Information about each image of the web page and the extracted second plurality of features for the web page are input into a supervised machine learning classifier to calculate a logo confidence score for each image of the web page, the logo confidence score indicating the probability that the image is an organization logo. In response to a particular image in the web page having a logo confidence score transgressing a first threshold, the particular image is injected into an organization logo field of the first record.

Automatic online registration based on data extracted from a digital image

The disclosed embodiments relate to a system for identifying entities (such as companies) that are associated with members of an online social network. During operation, the system obtains text strings from member records in the online social network, wherein each text string is an identifier for an entity that is associated with a member. The system also obtains connection information specifying connections among members of the online social network, and possibly location information for the members. Finally, the system uses the text strings, the connection information and the location information to identify entities that are associated with members of the online social network.

Growing community of inventors

Menlo Park, CA, United States of America

Alex Ching Lai