Information Extraction using Natural Language Processing (NLP) produces entities along with some of the relationships that may exist among them. To be semantically useful, however, such discrete extractions must be put into context through some form of intelligent analysis. This paper offers a two-part architecture that employs the statistical methods of traditional NLP to extract discrete information elements in a relatively domain-agnostic manner, which are then injected into an inference-enabled environment where they can be semantically analyzed. Within this semantic environment, extractions are woven into the contextual fabric of a user-provided, domain-centric ontology where users together with user-provided logic can analyze these extractions within a more contextually complete picture. Our demonstration system infers the possibility of a terrorist plot by extracting key events and relationships from a collection of news articles and intelligence reports.
Semantically-enhanced information extraction
2011 Aerospace Conference ; 1-14
2011-03-01
1159426 byte
Conference paper
Electronic Resource
English
Semantically enhanced containers for concurrent real-time systems
Tema Archive | 2009
|CORRECTING ERRONEOUS UAV POSITIONING INFORMATION USING SEMANTICALLY SEGMENTED IMAGES
European Patent Office | 2025
|CORRECTING ERRONEOUS UAV POSITIONING INFORMATION USING SEMANTICALLY SEGMENTED IMAGES
European Patent Office | 2024
|Semantically Enriched Multi-Modal Routing
Springer Verlag | 2014
|