KEYWORDS: Databases, Knowledge management, Data modeling, Information technology, Space operations, Data integration, Information architecture, Associative arrays, Systems modeling, Data conversion
The Knowledge Management Framework (KMF) of the US Air Force 45th Space Wing's Knowledge Management
Initiative (KMI) is a semantic service-oriented architecture that provides Eastern Range stakeholders with a semantically
unified, web-based view of distributed range information-a Single Integrated Range Picture-through a virtual,
federated, ontology-based enterprise model. Design time activities include the creation of physical data services and
mapping of those physical data services to logical data services corresponding to the concepts described in an OWLDL[
1] ontology. The physical data services aggregate and normalize information stored within federated relational
databases and XML[2] files. Runtime activities are managed through a single web service providing methods for
ontology discovery, ontology inspection and retrieval of concept instances from federated data sources. We present
lessons learned and the technology currently under development to support ontology-driven EII, reasoning, and search.
We finish by discussing how these lessons have reshaped our thinking about the role of semantics in enriching
information to make it more meaningful for stakeholders, and the impact of these new concepts on our evolving KMF
architecture.
KEYWORDS: Data fusion, Data modeling, Associative arrays, Data processing, Statistical analysis, Sensors, Defense and security, Bridges, Fourier transforms, Human-machine interfaces
The challenges of predictive battlespace awareness and transformation of TCPED to TPPU processes in a netcentric
environment are numerous and complex. One of these challenges is how to post the information with the
right metadata so that it can be effectively discovered and used in an ad hoc manner. We have been working on the
development of a semantic enrichment capability that provides concept and relationship extraction and automatic
metadata tagging of multi-INT sensor data. Specifically, this process maps multi-source data to concepts and
relationships specified within a semantic model (ontology). We are using semantic enrichment for development of
data fusion services to support Army and Air Force programs. This paper presents an example of using the semantic
enrichment architecture for concept and relationship extraction from USMTF data. The process of semantic
enrichment adds semantic metadata tags to the original data enabling advanced correlation and fusion. A geospatial
user interface leverages the semantically-enriched data to provide powerful search, correlation, and fusion
capabilities.
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