AI Studio
Ontologies, Knowledge Graphs & Semantic Classification
Software and services for
Enterprise Taxonomy Management
Ontology Schema Management
RESTful API Integration
Graph Database
Corpora Analytics
Auto-Categorization
Semantic Classification
How it works
Semantic Tagging
identifies the many taxonomy concepts and named entities that are mentioned with in the full text of a document. It uses concept labels, disambiguators and contextual rules.
Categorization
identifies the few concepts and named entities that best describe the aboutness of a whole document. It uses term frequency, document zone relevancy, semantic proximity, inferencing, and TF-IDF.
Entity Extraction
identifies new named entities (people, places, organizations, etc and/or conceptual entities, that are found in the full text of a document and not present in the taxonomy. It uses Natural Language Processing (NLP).
Big Knowledge Graphs
such as DBpedia, GeoNames and Wikidata, can be used for disambiguation and to identify content recommendations. They use inferencing and similarity matching to identify connections between concepts and content.
Human in the Loop
regardless of whether rules-based or ML methods are employed, the process of developing and refining tagging and categorization pipelines iterative and incorporates human feedback.
Graphite simplifies the management of enterprise taxonomies and ontologies. Squirro AI Studio enables taxonomists and content managers to automate the tagging and categorization of enterprise content.
AI Studio gives taxonomists and content managers full control of how enterprise content is tagged and categorized through simple and transparent rules that yield explainable results.
AI Studio delivers multiple benefits:
- Plug-and-play utilization of existing single source of truth enterprise taxonomies and ontologies
- Out-of-the-box tagging based on standard taxonomy semantic schema such as SKOS
- Rapid refinement of tagging accuracy by adding tagging contexts to the taxonomy
- Improve both recall and precision by harmonizing the semantics of search and content terminology
- Identify what is most relevant or salient – the aboutness of a document
- Support content recommendation and discovery
Semantic tagging and auto-categorization improve the accuracy and consistency of metadata.
Semantic metadata powers precision search and supports content recommendations and discovery.
