#Data scientist linkedin summary professional

Structured Data on top of Entity Taxonomies In the following, we discuss three LinkedIn strategies for enriching data value from the perspective of the Knowledge Graph. To extend the comprehensiveness of LinkedIn data, external data sources are ingested and integrated into the LinkedIn Knowledge Graph.

To improve the quality of knowledge generation, all intra-entity relationships (e.g., parent-child relationships between organizations) and inter-entity relationships (e.g., a member has a certain skill, that certain skill is needed by a job) in the Knowledge Graph are computed by state-of-the-art artificial intelligence methods and, when necessary, verified by domain experts. Compared to the raw data inputted by members, the LinkedIn Knowledge Graph fundamentally improves the quality of knowledge representation.

#Data scientist linkedin summary professional

The LinkedIn Knowledge Graph standardizes entities and relationships by forming the ontology of the professional world on top of entity taxonomies, which define the identity and attributes of each entity and the relationships among the entities. Knowledge derived from LinkedIn data needs to be represented without ambiguity in a machine-legible way. For example, the successfulness of LinkedIn Sales Navigator is determined by how accurately it finds the right decision makers in a company for salespeople to contact, and how many such candidates are discovered. Data value is usually measured by revenue and user engagement with the products, both of which depend on the accuracy and comprehensiveness of the data. This data provides the foundation of consumer products for our members and monetization products for premium members. LinkedIn data represents the world’s largest online professional network, with relationships among more than 467M members, 290M jobs and 9M organizations through professional entities and attributes.