- 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.
Use Cases
Data Cleaning and Preprocessing
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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