AI Concept Resolution
Concept resolution not only aids in recognizing and differentiating different types of entities within a dataset, but also links concepts such as diseases, topics, and events. This categorization of related concepts simplifies analysis by matching events and product mentions, reducing the need to manually go through high volumes of data or track updates.
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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
Improve the overall quality of your data assets, by accurately matching and merging data, even when messy or conflicted.
Enhanced Data Analysis
Uncover valuable insights and make better decisions by improving your data's accuracy and integrating external or unstructured information.
Entity Resolution
Entity resolution is crucial in managing datasets as they may contain multiple records with different names or pseudonyms referring to the same real-world entity. Incorporating entity resolution in your data solution can help identify whether certain information relates to a specific customer or determine if two news articles are discussing the same real-world event, at a massive scale.
Concept Resolution
Organising similar concepts makes analysis and reporting easier. For instance, it matches different ways of referencing the same events, the many names that may exist for a disease or topic, and all the varieties in product mentions, against your own or any external taxonomy.