Maritime Knowledge Base Semantic Application Semantic Exchange Workshop February 17th, 2009 Eric Freese Semantic Web, XML & Geospatial Technologist Copyright

Maritime Knowledge Base Semantic Application

Semantic Exchange WorkshopFebruary 17th, 2009

Eric FreeseSemantic Web, XML &

Geospatial Technologist

Copyright 2009 Northrop Grumman Corporation

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Northrop Grumman Semantic Research

• Geospatial Semantic Web– Distributed Geospatial Data Sources

• Developed framework for geospatial information integration using OWL, SWRL and backwards rule processing with data sources using OGC standards

– Conceptual Query - developed query templates to allow users to easily create complex queries

– Geospatial Ontology Trade Study - investigated, compared and contrasted ontologies in geospatial domains

– Developed Snoggle, a graphical tool to assist in ontology alignment and generate SWRL to transform RDF into target ontology

• Independent Research and Development (IRAD)– Advanced Geospatial Intelligence

• Application of Knowledge & Presentation of a Maritime Domain Awareness capability using Variable Rules Semantic Query

• Semantic Data Acquisition - Features and Unstructured Text Extraction– Advanced Search and Discovery

• Enables the integration, discovery, query, and ontology-based search of multiple, heterogeneous data sources and web services using semantic technologies

– Geospatial Data Fusion• Apply semantic technology to the fusion of multiple geospatial feature data

sources (conflation) into a merged product– Scalable Holistic Discovery and Search

• Semantically, spatially, and temporally enhance Enterprise SearchCopyright 2009 Northrop Grumman Corporation

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Maritime Knowledge Base

MaritimeKnowledge Base

Client

Documents

Uses ontology-based interface to

search multiple maritime data

holdingsWWW

• Digitalseas.com – vessel data

• USCG – vessel data

• MarineTraffic.com – vessel, port and location (via AIS) data

• Worldwide Threat to Shipping – incident data

• International Maritime Bureau – piracy reports

• CIA World Factbook – country and port data

• E-ships.net – port data

• WorldPortSource.com – port data

• DBPedia – semantic Wikipedia

Allegrograph Triple Store


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• Data Discovery & Collection– Screen scraping – Java program developed to read HTML pages from

websites, extract useful data using XQuery, and output RDF/OWL– Data conversion – databases or spreadsheets converted to XML and

then converted to RDF/OWL using XSLT

• Custom Ontologies– Data driven ontologies were developed based on analysis of available

data– Where possible, ontology items were reused from standard ontologies– owl:sameAs used to connect identical resources from different sources

• Connecting the Dots– SPARQL queries written to identify common items from datasets to

connect them• e.g., a ship with same name and same identifier (IMO, MMSI, etc.)

probably the same ship.• originally used inverse functional properties to do this automatically but

dirty data made this unworkable


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• Types of Data– Vessels – name, type, registration, capacity, propulsion, ownership– Positions – lat/long, time, destination, ETA– Ports – lat/long & country locations, sub-ports, aliases– Incidents – ships involved, dates/times, description, general area– Geographic – countries and other cultural data

• Data Volume – Over 4.9 million RDF triples– ~ 80,000 ships– ~ 118,000 locations– ~ 5,000 ports in 245 countries– 964 incidents from 2006 to Present


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Cross-Ontology Querying (SPARQL)

• Ontologies contain connection points for simpler references– Vessels reference location points; incidents reference ships

involved; etc.

• Query examples:– List all data about a given ship (including positions and incidents)– List all the ports in a given country– List all ships that departed from a given port/country during a

specific period– List incidents in a specified period by country– List all ships departing from ports in predominantly Muslim

countries that are bound for the United States

• RDF/OWL data modeled to be self-describing – Possible to query a dataset without knowing anything about the

data set at the outset


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Conclusion

• Standardized ontologies will allow easier migration to Semantic Web– e.g., USCG or Lloyd’s could define a standard ontology for

maritime data

• Standardized ontologies will facilitate interchange and processing of data between stakeholders

• Currently existing structured and unstructured data can be made ready for the Semantic Web fairly easily– Database schemata can provide an initial stepping off point– Tools exist that allow relational databases to be treated as

Semantic Web repositories, including querying, inferencing, reasoning, etc.


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Contact Information

• Eric Freese– [email protected]– (314) 259-7875

• Jim Ressler– [email protected]– (314) 259-7847

• Mailing address:– 1010 Market St. Suite 1740, St. Louis, MO 63101-2000


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