Abstract: 

Beamery is a funded startup based in London and the US, selling talent management software to enterprise, and is industry leading in the talent management sector. The company has invested heavily in practical AI solutions. Specifically, we have invested in a dedicated team to build applied data science solutions into our products - The Beamery Edge Team. This is a multi-disciplinary group of data scientists, knowledge engineers, software engineers and product managers.

At the heart of the team is the Beamery Knowledge Graph - a collection of RDF standard graph databases comprising >20billion facts. All AI solutions are built on top of the knowledge graph. This knowledge is derived from:

Public web sources. Eg. Job adverts and news articles
Company artifacts Eg. HRIS information, applicant tracking data, HR database records, financial information
Public linked data - Eg. DBpedia

In addition to the core knowledge, the graph databases have been heavily normalised and augmented with new links and reconciled entities.

Session Outline
This session will explain:
Why a knowledge graph approach was chosen to power Beamery AI
Why RDF standard was chosen
How our core ontology was designed
How our data provenance is managed, and updates are made using the ontology.
How data from virtually any available data source can be integrated
How the knowledge is used to power applications in the Beamery App

Background Knowledge
The session is suitable for:
People interested in using RDF graph databases for the first time
People interested in realising the benefits of linked data
People interested in ontology design
People bewildered by multiple disparate and messy datasets or a data lake

Bio: 

Henri is Senior Knowledge Engineering at Beamery - a full-talent lifecycle scaleup making sense of enterprise people data, based in London and the US. At work, he specialises in ontology design, entity reconciliation and data provenance for semantic web databases. Henri has worked at a number of HRTech startups, and is passionate about modelling and serving AI models in the domain of people, skills, companies and occupations.