Principles of Physical Dispersion Technology
OntoCast solves polysemy and cross-document references through contextual modeling: when the same entity name (e.g., "Apple") appears in different contexts, the system automatically classifies it into the correct category (tech company or fruit) based on semantic features.
Realization mechanism
- Cross-block correlation analysis: Create a map of entity references for different text blocks within a document.
- Ontological constraints: Semantic validation using predefined or automatically generated ontology type systems
- vector similarity: Calculate the contextual similarity of entity referents through embedding models
Typical Application Scenarios
When dealing with academic papers: 1) distinguishing gene names from common terms; 2) merging authors' different spelling forms; and 3) correlating graphical data with in-text descriptions. Tests show that it can improve the entity linking accuracy of knowledge graphs by more than 40%.
This answer comes from the articleOntoCast: an intelligent framework for extracting semantic triples from documentsThe
May not be reproduced without permission:AI productivity tools " How does OntoCast's entity disambiguation work?
