LinkedIn data is semi-structured, as shown in the below member profile example. It consists of structured attributes (including image, name, position, organization, geography and company size) and unstructured attributes in the form of the profile summary written in free text. We use machine learning techniques to extract skills, years of experience and other relevant organizations from the profile summary. Then, all structured and extracted attributes are mapped to standardized entities so that this LinkedIn member (\u201cDeepak Agarwal\u201d) can be represented by a set of entity identifiers.<\/p>\n
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This process of standardization is the foundation of the LinkedIn Knowledge Graph, where various kinds of content are tagged explicitly by identifiers without ambiguity. Equipped with it, applications can search and recommend content with a clear intent, and organize and display content in rich results, both of which increase user engagement and revenue.<\/p>\n
Data-Sharing Platform as Single Source of Truth<\/strong><\/p>\n The global identifier scheme allows for knowledge sharing across the entire LinkedIn ecosystem. We build a data-sharing platform as the single source truth to generate and serve the Knowledge Graph. For example, Ads Targeting and People Search both consume data from the same platform. Given a title (\u201cSoftware Engineer\u201d), the set of LinkedIn members targeted by Ads is the same as the set of LinkedIn members returned by the search engine.<\/p>\n Sharing knowledge across different applications can significantly reduce the duplicated engineering work needed by different applications, and conveniently unifies company-wide data analytics and insight generation.<\/p>\n Bring Value to Our Members<\/strong><\/p>\n Not only has the Knowledge Graph driven a disproportionate share of total LinkedIn monetization value (e.g., members with high-quality, standardized profiles are more valuable than members with little or poorly-structured profile information), but it also brings unique value back to our members. For example, LinkedIn auto-generates a personalized profile summary based on professional entities inferred by the Knowledge Graph, and recommends it to members who don\u2019t have completely standardized profiles.<\/p>\n LinkedIn also leverages the Knowledge Graph to generate suggested additions to member profiles, e.g., \u201cpeers with this skill receive 30% more messages\u201d or \u201cpeers with this skill have a 15% higher chance of getting a new job.\u201d<\/p>\n In both examples, the LinkedIn Knowledge Graph brings value to our members by interactively engaging them to complete their profiles. The collected user feedback (\u201caccept,\u201d \u201cdecline,\u201d \u201cignore\u201d) in turn reinforces the learning of the Knowledge Graph, creating a robust LinkedIn data ecosystem.<\/p>\n![\"\"](\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2017\/03\/LinkedIn_fig2.png\")
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Contributed by: Qi He, Senior Engineering Manager – Machine Learning & Data Mining, Head of Data Standardization at LinkedIn. In his role, he leads a team of machine learning scientists and software engineers to help LinkedIn realize its vision of creating economic opportunities through building the world\u2019s best universal knowledge base for entities. Bee-Chung Chen, a Principal Staff Engineer & Applied Researcher at LinkedIn. Prior to joining the company in 2012, he spent four years as a research scientist at Yahoo. He was also previous at research assistant at the University of Wisconsin-Madison.<\/em><\/p>\n![\"\"](\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2017\/03\/Bee-Chung.jpg\")
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