Customer Use Cases

Defense agency customers use NetOwl products for a variety of large-scale text and identity analytics systems to support their missions. Some common use cases include:

Utilizing NetOwl Extractor’s entity extraction and smart geotagging capabilities, our customers exploit unstructured open source and internal text data for advanced geospatial analysis. NetOwl can not only extract, disambiguate, and geocode place names referenced in texts but also geocode and place people, organizations, and events for display on maps with other GIS software.

Full-text search is enhanced by NetOwl through semantic-based faceted search and discovery capabilities. The facet values are automatically generated by NetOwl extracting entities and events from input documents, which can be in English and other languages. Users are able to refine their search intelligently with the key extracted entities or events exposed through the facets as well as discover new entities, which are used as new leads for further investigation.

By utilizing NetOwl’s intelligent name matching in addition to entity extraction, our customers are able to find documents that mention the specific people, organizations, or places they are looking for without worrying about having to know all of the potential different spelling variations of those entities. NetOwl’s AI/ML-based NameMatcher can match names with varying transliterations and spellings For example, a search for a key person such as “Abu Bakr al-Baghdadi” could identify other variants that have appeared in various texts such as “Bakr alBaghdad”, “Abu Bakr al-B” and even “أبو عمر البغدادي”.

Intelligence agency customers use NetOwl products for a variety of large-scale text and identity analytics systems to support their intelligence analysis missions. Some common use cases include:

NetOwl Extractor’s very rich ontology of not only entities, but attributes, relationships, and events allows our customers to automatically create “biographies” of key entities from massive amounts of documents. The biographical information extracted from unstructured text data is combined with existing structured data to provide a complete and evolving picture of these entities.

Full-text search is enhanced by NetOwl through semantic-based faceted search and discovery capabilities. The facet values are automatically generated by NetOwl extracting entities and events from input documents, which can be in English and other languages. The users are able to refine their search intelligently with the key extracted entities or events exposed through the facets as well as discover new entities, which are used as new leads for further research.

NetOwl’s name and entity matching capabilities enable our customers to match person records against watchlists much more intelligently than ever before. NetOwl’s AI/ML-based name matching handles a wide variety of name variations in multiple languages and scripts.  In addition, its entity matching allows not only for matching and disambiguating entity records based on attributes beyond names (e.g., DoB and PoB) but also allows for previously unknown associations between different entities to be discovered such as different people associated with the same or similar address or phone number.

Our customers in Law Enforcement use NetOwl products for a variety of large-scale text and entity analytics systems to support their missions. Some common use cases include:

NetOwl EntityMatcher enables our customers to match any entity records against watchlists much more intelligently than ever before. NetOwl’s AI/ML-based name matching handles a wide variety of name variations in multiple languages, and its entity matching handles not only names but also other entity attributes such as address, phone number, DoB, PoB, physical characteristics, etc. intelligently.

NetOwl Extractor’s ability to identify key entities and attributes helps our customers combine information mentioned in “narrative” sections of various documents, such as incident reports, with semi-structured metadata or with centralized databases of entity and incident information. Often, key facts are present in the unstructured, free-form comments section of these reports that are not captured in the more traditional structured data. With NetOwl users can connect the dots and locate these facts without reading through a mountain of documents.

NetOwl extracts not only over 100 types of entities and but also over 150 types of relationships and events, including key entities and events related to cyber-related crime and activities, from unstructured data to help discover new relationships. Our customers utilize this link and event information to perform advanced link analysis, using such tools as Analyst’s Notebook and Palantir.
NetOwl’s name and entity matching capabilities enable our customers to match any entity records against watch lists much more intelligently than ever before. NetOwl’s award-winning name matching handles a wide variety of multi-ethnicity name variations, and its entity matching handles not only names but also other entity attributes such as address, phone number, DoB, PoB, physical characteristics, etc. intelligently.

Civilian agency customers use NetOwl products for a variety of large-scale text and entity analytics systems to support their missions. Some common use cases include:

One of our customers in the health research area expanded the NetOwl Extractor ontology to a bioinformatics domain. The expanded NetOwl was used to extract pathogen and genetic information from a large volume of MEDLINE abstracts for better search and discovery.

Customers in the cyber security area use NetOwl Extractor for Cyber Security to monitor open source data to detect trends and patterns in cyber attacks. NetOwl extracts various types of cyber attack events along with their targets, attackers, malware, etc. to provide building blocks for predictive analysis.

Homeland Security customers use NetOwl products for a variety of large-scale text and identity analytics systems to support their missions. Some common use cases include:

Our customers use NetOwl NameMatcher and EntityMatcher to match any entity records against sanction lists and watchlists much more intelligently than ever before to protect the borders. NetOwl’s AI/ML-based name matching handles a wide variety of name variations in multiple languages, and its entity matching handles not only names but also other entity attributes such as address, phone number, DoB, PoB, physical characteristics, etc. intelligently.

Full-text search is enhanced by NetOwl Extractor through semantic-based faceted search and discovery. The facets are automatically generated by NetOwl extracting entities and events from input documents, which can be in English and other languages. The users are able to refine their search intelligently as well as discover new entities, which are used as new clues for further research.

Utilizing NetOwl’s entity extraction and smart geotagging capabilities, our customers exploit unstructured text data for more advanced geospatial analysis. NetOwl can not only extract, disambiguate, and geocode place names referenced in texts but also geocode and place various events on a map leveraging other GIS software.

NetOwl is used by not only the public sector customers in the United States but also those in other countries. All our customers have the same goal of using the best-of-breed products to address the critical challenge of keeping their countries safe and secure. Our international customers often use NetOwl for both English and non-English languages.