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Ontology

The ontology is the foundation of RootCause.ai. It provides a structured map of your enterprise data, aligning everything to entities, time, and location. By standardizing how data from different sources connects, it removes ambiguity and makes downstream causal analysis reliable and explainable.

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Definition & Purpose

An ontology is more than just a schema. It is a unified model of your enterprise that organizes raw, messy inputs into consistent anchors.

Different datasets rarely use the same format or schema. RootCause.ai resolves this by detecting when records refer to the same entity, time, or location, even if the formats differ (e.g. VIN vs. plate number, timestamp vs. date field, postcode vs. lat/long). This alignment creates a coherent, shared ontology across systems.

The purpose of the ontology is to create a shared foundation across disparate systems so RootCause.ai can reason about cause and effect in a consistent way.

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How Data Links Together (Entity / Time / Location)

Different datasets rarely use the same schema. RootCause.ai resolves this by aligning records to common anchors:

  • Entity: Identifies when datasets are referring to the same real-world object (e.g. VIN numbers both pointing to the same truck, product SKUs, employee IDs).

  • Time: Matches events happening at the same point or interval (e.g. shipment timestamp vs. finance posting date).

  • Location: Reconciles references to places across formats (e.g. street addresses, post codes, warehouse IDs, GPS coordinates).

By resolving entities, times, and locations into a shared ontology, RootCause.ai lets disparate data sources align even when their formats differ. The ontology brings heterogeneous datasets into one coherent representation.

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Editing & Oversight

Ontologies aren’t static. RootCause.ai gives you tools to:

  • Review and adjust mappings for entities, times, or locations.

  • Validate joins and hierarchies to catch errors or ambiguities early.

  • Apply governance rules so that business-critical relationships stay correct.

This layer of oversight ensures data quality before it flows into causal graphs or simulations.

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Data Views

RootCause.ai generates 360° views - combined tables that reflect the joins across entities, times, and locations.

  • Examples: Customer 360 View, VIN 360 View, Contract 360 View.

  • Each view consolidates records into a single, auditable table that can be exported as CSV, JSON, or Parquet.

  • Views are dynamically generated from the ontology, not fixed pipelines.

These 360° views form the structured inputs for causal analysis and digital twin simulations.

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