Microsoft Technology Licensing, LLC

Linkedin Corporation

Workday, Inc.

Peter Nicholas Skomoroch

Matthew Terence Hayes

Vinodh Jayaram

Elliot Shmukler

Avery Randolph Moon

David Michael Breger

Prachi Gupta

Adam Nash

Abhishek Gupta

Dhanurjay A S Patil

Gloria Lau

Zhigang Hua

Saveliy Uryasev

Samir Mavji Shah

Michael Conover

Kin Fai Kan

Mathieu Bastian

Zal Yaz Bilimoria

Matt Burgess

Kin Kan

Scott Mitchell Blackburn

Jessica Zuniga

A system for disambiguation of noisy text includes an interface and processor. The interface is configured to receive a set of words. The processor is configured to determine a token from the set of words, wherein the token includes a subset of the set of words; determine candidates for the token; determine a ranking for the candidates based at least in part on a semantic similarity between the token and each of the candidates; determine whether to select a top ranked candidate or to abstain; and in response to a select determination to select the top ranked candidate, indicate the top ranked candidate is selected.

Fast entity linking in noisy text environments

Techniques for inferring the identity (e.g., member profile attributes) of members of an online social network service are described. According to various embodiments, a member profile attribute missing from a member profile page associated with a particular member of an online social network service is identified. Member profile data and behavioral log data associated with a plurality of members of the online social network service is then accessed. Thereafter, a prediction modeling process is performed, based on a prediction model and feature data including the member profile data and the behavioral log data, to generate a confidence score associated with the particular member and the missing member profile attribute, the confidence score indicating a likelihood that the missing member profile attribute corresponds to a candidate value.

Inferred identity

This disclosure provides for systems and methods directed to generating colleague recommendations within a social network service. The systems and methods may include identifying a sample member of a social network service and determining an organizational unit associated with the sample member. The sample member may be associated with prior member activity involving a source member and the organizational unit may represent a department within an organization. The systems and methods may also include identifying various candidate members, where the candidate members are associated with the organizational unit. The systems and methods may also include surfacing the plurality of candidate members to a computer system being operated by the source member.

Recommending decision makers in an organization

Disclosed in some examples is a method comprising determining a first set of high ranking skills, the first set containing skills possessed by a member of the social networking service based upon the member's user profile; determining a second set of high ranking skills, the second set containing skills for a second member of the social networking service based on the second member's user profile; determining a third set of high ranking skills, the third set being the intersection between the first and second set of high ranking skills; and suggesting one or more of the skills in the third set of high ranking skills to the member for endorsement of the second member with respect to that skill.

Skills endorsements

In an example, disclosed is a machine automated method of identifying a set of skills. In some examples, the method includes extracting a plurality of skill seed phrases from a plurality of member profiles of a social networking site, creating a plurality of disambiguated skill seed phrases by disambiguating the plurality of skill seed phrases using one or more computer processors, and de-duplicating the plurality of disambiguated skill seed phrases to create a plurality of de-duplicated skill seed phrases.

Skill extraction system

Disclosed in some examples are systems, methods, and machine readable mediums which allow for the automatic creation of a skills hierarchy. The skills hierarchy comprises an organization of a standardized list of skills into a hierarchy that describes category relationships between the skills in the hierarchy. The category relationships may include no relationships, parent relationships, and child relationships. A skill may be considered a parent of another skill if the parent skill describes a broader category of skill that includes the child. Other relationships such as grandparent (e.g., a parent's parent), great-grandparent, grandchild, great grandchild and so on may be defined inferentially as well. In some examples, the constructed hierarchy may be organized with broader skills at higher levels and narrower skills at lower levels.

Skills ontology creation

Techniques for modifying search results associated with a search request based on a determination that a member profile attribute is inaccurate are described. According to various embodiments, an existing member profile attribute included in a member profile page of a particular member of an online social network service is identified. Member profile data and behavioral log data associated with a plurality of members of the online social network service is then accessed. Prediction modeling to verify the existing member profile attribute is performed using the accessed data. Additionally, a confidence score associated with the existing member profile attribute is generated based on the prediction modeling. Moreover, the existing member profile attribute is determined to be inaccurate based on the generated confidence score. Furthermore, the search results associated with a search request is modified based on the determination.

Growing community of inventors

San Francisco, CA, United States of America

Peter Nicholas Skomoroch