AI Concept Resolution

Concept resolution not only aids in recognizing and differentiating different types of entities within a dataset, but also links various concepts such as diseases, topics, and events. This categorization of related concepts simplifies analysis by matching events and product mentions, reducing the need to read multiple headlines or track updates.

We focus on transforming your data representing things not strings. (phrasing shamelessly borrowed from Google's intro to Knowledge Graphs)

Key Advantages

Increased Efficiency

Automatically identify and merge duplicate entities or concepts, reducing the need for manual data cleansing and reconciliation.

Improved Data Quality

By accurately matching and merging data, concept resolution improves the overall quality of your data assets.

Enhanced Data Analysis

Uncover valuable insights and make better decisions by improving your data's accuracy and integrating external or unstructured data sources.

If you’d like more information about our features, get in touch today.

Get in Touch

Entity Resolution

Entity resolution is crucial in managing datasets as they may contain multiple records referring to the same real-world entity with different names or pseudonyms. It is important to differentiate between these entities. Incorporating entity resolution in your data solution can help answer questions such as identifying a specific customer or determining if two news articles are discussing the same real-world event.

Concept Resolution

Concept resolution not only helps in identifying and distinguishing entities in a dataset, but also matches various concepts like events, topics, diseases, etc. This grouping of similar concepts makes analysis easier. For instance, it matches events and product mentions, saving time and effort in reading multiple headlines or tracking updates.