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Use Cases

Data Cleaning and Preprocessing

  • Anomaly detection Identify errors, gaps, or inconsistencies in text data.
  • Text normalization Convert different forms of text into a standard format, such as changing abbreviations or synonyms to a standard term.
  • Semantic mapping Translate free-text terms to standard codes or vocabularies.

Data Cataloging

  • Automatic tagging Use NLP to automatically tag or label datasets based on their content.
  • Metadata extraction Extract metadata from unstructured data sources automatically.Dataset summarization Generate brief summaries or overviews of large datasets.
  • Dataset summarization Generate brief summaries or overviews of large datasets.

Querying and Access

  • Natural language querying allows users to search databases using natural language queries instead of SQL or other formal querying languages.
  • Data exploration Assist in uncovering patterns, correlations, and insights from vast datasets through semantic understanding.

ETL (Extract, Transform, Load) Processes

  • Text extraction Extract structured information from unstructured or semi-structured sources, such as scraping text data from web pages or extracting entities from documents.
  • Transformation logic explanation Convert complex transformation logic into human-understandable descriptions or visualizations.

Documentation and Metadata Generation

  • Auto-documentation Automatically generate documentation for databases, data lakes, or pipelines.
  • Sentiment analysis On feedback about data products or tools, to understand user sentiment and improve systems accordingly.

Schema Matching and Integration

  • Entity matching Identify and link the same entities across different datasets.
  • Semantic schema matching Link database schemas based on semantics rather than just structural or syntactic similarity.

Data Governance and Compliance

  • Sensitive data detection Identify sensitive information in datasets, such as Personal Identifiable Information (PII), to ensure compliance.
  • Policy enforcement Check datasets and data operations against natural language policies to ensure adherence.

Knowledge Graphs and Ontology Creation

  • Entity and relationship extraction Populate knowledge graphs by extracting entities and their relationships from text.
  • Ontology augmentation Expand or refine existing ontologies based on new textual information.

Automated Data Lineage Discovery

  • Trace data origins Understand where data comes from and its journey through various systems.
  • Explain data lineage Convert complex data lineage graphs into human-understandable narratives.

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