Ontology Design & Data Mining Research for BATC

KBSI is researching the application of advanced semantic technologies to layered sensing networks (LSN) and the use of data mining technologies for discovering and extracting information from large data stores. The research also investigates the automatic generation of ontologies from unprocessed data sources.

The increased use of networked sensor platforms to perform various command and control, surveillance, and reconnaissance missions has resulted in a corresponding increase in the quantity (and variety) of data that must be organized and analyzed as a precursor to extracting useful information.  The purpose of this initiative with Ball Aerospace and Technologies Corporation (BATC) and Air Force Research Laboratories, Anti Tamper and Software Protection (AFRL/RYT) is to research various methods and technologies for extracting valuable information from vast quantities of data.

This initiative, performed in collaboration with BATC and AFRL/RYT, focused on the application of advanced semantic technologies to layered sensing networks (LSN) and the use of data mining technologies for discovering and extracting information from large data stores.  Research on semantic technologies included the automatic generation of ontologies or ontology fragments from unprocessed data sources.  KBSI worked with BATC and AFRL/RYT to prioritize the focus of these semantic analysis efforts and applied existing KBSI statistical, text mining, natural language processing (NLP), and pattern based tools in controlled experiments to both develop applicable ontologies and to characterize the aspects of the LSN domain where particular technologies would be most effective.

webbanner_dataminingdiscforeKBSI also explored the agent-based application of existing KBSI technology for the monitoring, extraction, analysis, and dissemination of information contained in network data resources. Our data mining technologies were leveraged in researching advanced techniques for information discovery and extraction, and in investigating the ability of these techniques to recognize and extract patterns and relationships that exist among data elements.  Using the CRIS methodology, the systems and knowledge engineering refinement standard for data mining initiatives, KBSI examined knowledge discovery methods, data understanding, model design, data preparation, analysis process specification, process execution, and results interpretation in support of mining the LSN data.