Automated precision in action
Enormous, rapidly scaling collections of digital assets are unavoidable today. Manual management of these assets is crippling and undermining our highly valued employees, and there is a better way.
The SURROUND Ontology-powered Records Triage (SORT) Robot effectively triages electronic records, aids record discoverability, and decreases operational expenditure through the ability to near-fully automate what is currently a manual task.
SORT has been designed to automate electronic record triaging processes smoothly, accurately, and accountably. SORT auto-classifies and sentences electronic records deemed to be of significance, using a powerful situational awareness capability for determining the context of the document, and therefore the appropriate actions required for retention or disposal. Based on its enhancements to electronic records, SORT supports improved record discoverability and faceted searching.
Auto-classification outcome accuracy is measurable, consistent, auditable, and comparable with manual sentencing practices. SORT provides a human-readable explanation for each assessment action.
Dashboards provide visibility of auto-classification events, which are aggregated to provide a wide range of KPIs connecting organisational strategy to business outcomes.
Under the hood … traceable, explainable AI
The SORT Robot uses a combination of advanced artificial intelligence, semantics, and machine learning techniques to:
- Characterise electronic records with Semantic metadata
- Execute classification rules against the Records - the rules themselves are managed as reference data
- Using different Record content and metadata analyses
- Incorporate feedback on classification results to improve methods
- Employing both supervised and unsupervised Machine Learning
- Track provenance of all classifications and system executions to preserve decision transparency
SURROUND’s Outstanding Technology Applications
The SORT Robot stores both generated data and reference data used in sorting as Semantic Web knowledge graphs. It uses the SURROUND Ontology Platform [insert hyperlink to SOP page] to manage the multi-part reference data knowledge graphs relevant to a sorting scenario.
System actions and generated data have provenance recorded using the PROV Data Model standard. This ensures maximal operating transparency.
The SORT Robot accesses reference data and also presents interfaces for down-stream systems via a leading-edge GraphQL API as well as other specialised APIs, such as SPARQL Endpoints. This means it can be readily meshed into multi-part systems.
The SORT Robot incorporates several Machine Learning (ML) scenarios operating at different conceptual levels that together allow a Robot deployment to improve its performance over time. The Robot can "learn" what better results look like from human feedback. It can "learn" what more efficient part-classifications of records look like by correlating reference data and results. It can also learn about drifting result trends by re-examining its processing history which is preserved in a comprehensive provenance trace.
The SORT Robot's ML scenarios all read from and write back to RDF Knowledge Graphs and all learning is tracked using the PROV Data Model. Using a very flexible and yet standards-based data model for ML means we can adapt the learning scenarios per-application and present results and decisions to demonstrate ML traceability.
Adapting the SORT Robot for you
The SORT Robot can be adapted to classifying any kind of digital asset.
The particular record (asset) reading functions can be adapted for your data types and then you can specify the classifications that you want to see records sorted according to. We can build up a collection of reference data as structured Knowledge Graphs that represent the knowledge in your domain so that the SORT Robot can clear your backlogs, automate the mundane tasks and free-up your human experts.
Whatever the deployment scenario: the SORT Robot will track all decisions and learning made using standardised provenance so that you have auditable outcomes.