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Integrated Ontology for Maritime Operations

This repository contains a collection of ontologies related to maritime operations, designed to support decision-making for the U.S. Navy in the Pacific Command Counter Intelligence Surveillance and Reconnaissance and Targeting (PACOM C-ISRT) and the Joint Interagency Task Force (JIATF)-South counter-narcotics operations.

Ontologies Included

  1. Scene Ontology: Represents the entities, relationships, and events in a maritime scene, including vessels, ships, and weather events.
  2. System Ontology: Covers the systems and sensors involved in collecting and analyzing data in the maritime environment, like sensor systems and radars.
  3. Common Semantic Core: The fundamental concepts and relationships shared across both domains, serving as a foundation for further development and ensuring consistency between the ontologies.
  4. Geospatial Model: Encapsulates the physical locations and relationships within the naval context, including maritime boundaries, naval bases, and shipping lanes.
  5. Spatiotemporal Ontology: A structured representation of time and temporal relationships within naval data, integrating with the geospatial model to represent the movement and changes of naval assets over time.
  6. Knowledge Graph: A comprehensive, interconnected representation of maritime scenarios pertinent to the Navy, integrating and unifying data from diverse modalities like text, audio, video, and multispectral imagery. It aligns closely with the principles defined in the Integrated Ontology Framework and the Geospatial/Spatiotemporal Models.

Usage

The ontologies are represented in OWL (Web Ontology Language) format, which is a standard semantic web language for representing rich and complex knowledge about things, groups of things, and relations between things.

You can use ontology editors like Protégé to visualize, edit, and manage these ontologies. For the knowledge graph, we provide a Cypher script that can be used in Neo4j to create and populate the graph database.

Machine Learning Applications

These ontologies are designed to support machine learning algorithms to reason over the data, uncover latent contextual features, and classify events. Example applications could include recognizing patterns in ship movements, identifying anomalous activities, or predicting potential areas of interest for naval operations.

  1. Protégé
  2. Neo4j
  3. National Geospatial-Intelligence Agency's Maritime Safety Information portal