NetOwl Extractor offers unique semantic-based relationship extraction capabilities in addition to entity extraction using AI-based natural language processing technologies. NetOwl's relationship extraction provides a wide variety of relationship types applicable to many domains.
Extensive Ontology for Relationship Extraction

Extensive Ontology for Relationship Extraction

NetOwl performs relationship extraction between two entities by making use of the linguistic features and context within a text, not by simple co-occurrence.  NetOwl’s relationship ontology offers a large set of semantic relationships between many different types of entities, such as person, organization, place, artifact, address, phone number, etc., leveraging NetOwl’s broad entity extraction ontology.

Sample semantic relationships include affiliations, associations, family relationships, various location-related relationships, etc. that are useful in many domains.

Relationship Extraction for Link Discovery

Relationship Extraction for Link Discovery

Traditionally relationships between entities have been extracted manually by human analysts for use in a link analysis or visualization tool.  With its automated relationship extraction, NetOwl can discover potentially useful new relationships from massive amounts of text data that could not have been thoroughly analyzed manually.

Analysts can then do what they do best, finding key insights and patterns from those newly discovered relationships, utilizing their deep subject matter expertise and extensive background knowledge.  This type of human-AI collaboration can truly revolutionize knowledge discovery from Big Data.

Advanced Relationship Extraction beyond Names

Advanced Relationship Extraction beyond Names

NetOwl not only extracts named entities (e.g., “Mary Smith”) but also descriptive noun phrases (e.g., “the CEO of XYZ Corp.”) for each entity ontology type.  Thanks to its advanced coreference resolution capability, NetOwl connects anaphors such as pronouns (“his parent”) and definite noun phrases (“the subsidiary of ZZZ Inc.”) to corresponding named entities, enabling information-rich relationship extraction and event extraction as well as more intelligent entity-based sentiment analysis.

Additionally many important attributes, such as age, personal physical characteristics, and latitude/longitude (through Smart Geocoding), are also extracted, adding rich information to each extracted entity.

Key Features of Relationship Extraction

  • High Accuracy & Speed

    High Accuracy & Speed

    Provides state-of-the-art relationship extraction accuracy on many different types of text.  Extremely fast and scalable for real-time analysis.

  • On Premise or In the Cloud

    On Premise or In the Cloud

    Supports Docker and Kubernetes for easy and scalable deployment.

  • Easy Integration

    Easy Integration

    REST APIs offer easy integration. Pre-integrated with popular search and analytics tools like Elasticsearch and Esri ArcGIS.

  • Coreference Resolution

    Coreference Resolution

    Resolves co-referring entities, whether they are names, pronouns, or definite noun phrases:

    • “she” ➞ “Mary Smith”
    • “the incoming CEO” ➞ “Mary Smith”
  • Customizable

    Customizable

    Creator Edition (CE) enables the customization of existing entity/relationship types or addition of new entity/relationship types.

  • Multilingual

    Multilingual

    Relationship extraction is currently available in English and Arabic. Other languages are on the way.

Contact us and discover what NetOwl can do for you!

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