NetOwl

NetOwl is a suite of Text Analytics and Identity Analytics products offered by the vendor NetOwl. These products utilize AI and Machine Learning technologies to analyze Big Data. NetOwl is suitable for companies of all sizes and is used by financial firms, intelligence analysts, cybersecurity organizations, government agencies, and law enforcement agencies.

Key Features

Entity Extraction: NetOwl utilizes AI-based natural language processing and machine learning technologies to provide accurate and scalable entity extraction in multiple languages. It supports various types of entities, including people, organizations, places, addresses, artifacts, phone numbers, and titles.

Relationship Extraction: NetOwl Extractor offers semantic-based relationship extraction in addition to entity extraction. It leverages AI-based natural language processing technologies to identify relationships between entities based on linguistic features and context. NetOwl's relationship ontology includes a wide range of semantic relationships, such as affiliations, associations, family relationships, and location-related relationships.

Event Extraction: NetOwl Extractor enables event extraction capabilities alongside entity and relationship extraction. It identifies events and event participants within text data, providing valuable insights. NetOwl's event ontology includes over 100 types of events, facilitating detailed event extraction and analysis.

Sentiment Analysis: NetOwl's sentiment analysis software provides entity-based and aspect-based sentiment analysis. It captures sentiments related to different entities and specific aspects. NetOwl's sentiment analysis tool incorporates a refined sentiment ontology, enabling detailed sentiment analysis and prioritized responses.

Geotagging: NetOwl's Smart Geotagging accurately assigns latitude/longitude values to place names and other geocodable entities and events extracted from text. It utilizes advanced natural language processing and geospatial calculations to disambiguate place names and assign the most appropriate location based on context. NetOwl's geotagging also handles relative locations, MGRS and UTM coordinates, and integrates with leading GIS providers.

Categorization: NetOwl offers multiple approaches to document categorization, including machine learning-based, topic tagging-based, and semantic extraction-based categorization. It supports accurate and fast categorization in multiple languages.

Name Matching: NetOwl's advanced machine learning-based name matching approach effectively addresses complex fuzzy name matching challenges. It handles various variations, such as transliteration variants, nicknames, misspellings, orthographic variations, and tokenization variations. NetOwl supports multilingual and cross-lingual name matching, making it suitable for diverse languages and ethnicities.

Identity Resolution: NetOwl EntityMatcher provides accurate and scalable identity resolution based on similarities of entity names and other key attributes. It performs identity resolution by leveraging a proprietary search and indexing engine, supporting multilingual and cross-lingual matching for multiple entity types.