Semantic Technologies

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Advanced Semantic Technologies

KBSI is an industry leader in semantic technologies, assisting government and commercial organizations to more effectively leverage their information and data in their business operations. The growth and availability of data, both externally and internally, and the systems warehousing of this data have hindered the ability to effectively utilize it in decision-making and planning. Semantic technologies provide an intelligent platform for analyzing and utilizing data, delivering new insights to organizations and improving their products and services.

KBSI has extensive experience in the creation and application of advanced and innovative semantic technologies that are being applied and delivering value in a wide variety of organizations today. KBSI's semantic technologies have become central to a broad range of our research and development initiatives, including work with the DoD, NASA, DOE and the Department of Homeland Security in areas such as medical data mining, threat detection, computer network security, discovery of disease outbreaks, cash reserves management and financial fraud detection, decision making and training, target decoy recognition, the redesigning of complex, multi-agent systems and business intelligence.

Open-Source Information Tactical Exploitation (ONSITE)

KBSI is researching, designing, and demonstrating technology that uses an innovative approach to natural language processing (NLP) to enable state of the art processing rates for the automated understanding of natural language in support of military tactical operations. The ONSITE initiative is investigating a specially constructed ontology that will lead to higher processing throughput and increased semantic resolution of extracted information.

Ontology Driven Integration Framework (ODIF)

KBSI is designing and developing technology that will automate the sharing and integration of information among different organizations. The development of a common ontology for integrating diverse data sources, combined with knowledge extraction techniques and ontology analysis methods, will allow users to extract knowledge from distributed, unstructured text sources and share knowledge among large-scale project teams.

Human Enemy Network Influence Operations Map (HEN-IO-MAP)

KBSI is researching and designing and demonstrating an advanced utility-theoretic preference model for influence operations (IO) modeling. The HEN-IO-MAP system will apply utility theoretic models in creating a human enemy network influence operations map that incorporates cultural, social, and other factors using a simple knowledge acquisition process. The system will assist influence operations (IO) planners in the evaluation and formulation of influence operations.

Framework for Adaptive Modeling and Ontology-driven Simulation (FAMOS™)

KBSI is building a system for the development and execution of distributed military simulation applications. The FAMOS™ technology will reduce the time and effort needed for developing and deploying distributed simulation applications and will lay the groundwork for knowledge sharing, communication, and semantic integration among modeling and simulation applications.

Toolkit for Agent-based Knowledge Extraction (TAKE™)

KBSI applied a hybrid approach that combines ontology engineering methods with cutting-edge knowledge discovery techniques to extract, analyze, and map ontologies from distributed and disparate knowledge and data sources. The TAKE™ technology enables the rapid and cost effective analysis, redesign, integration and/or reengineering of complex systems.

Adaptive Toolkit for the Discovery of Threats (ATDT)

KBSI developed a toolkit that integrates intelligent knowledge discovery techniques, advanced data mining technologies, and knowledge-based methods for automated threat detection. ATDT is an agent-based decision support system that mines data (news feeds, web databases, traffic reports, radio intercepts, human intelligence, etc.) to extract meaningful knowledge or "indicators" of emerging threats.

Information-Fusion based Indication & Warning Assessment and Recognition System (IIWARS)

KBSI developed a system that, using text mining technologies and information fusion, improves the DoD's terrorist threat assessment capabilities. IIWARS uses advanced text and data mining methods to mine data from multiple, distributed text sources (news feeds, web databases, traffic reports, radio intercepts, human intelligence) and extract multiple features and "indicators" of an emerging or actual threat. These indicators are then fused into a threat model which can be compared to hypothesized threat Course Of Action (COA) templates.

Data Display Translation Framework (DDTF)

KBSI is developing an advanced component-based Data Display Markup Language (DDML), an XML-based neutral format also developed by KBSI, as the inter-lingua in translating the data display languages supported by different vendors.

Faraday Chromate

KBSI is performing analyses of Electro Impedance Spectroscopy (EIS) data in order to study the effectiveness and life of particular coatings on accelerated corrosion environments.

Framework for Enabling Adaptive Scenario Generation for Training (FEAST)

KBSI is designing an innovative methodology that enables dynamic scenario generation and adaptation for individual and team learning in simulation-based environments.

Framework for Interoperability of Executable Architectures (FIEA)

KBSI is developing a language, tool framework, and methodology that will enable users to share data among the many vendor specific tools and applications they use.

Geopolitical Region of Interest Projection System (GRIPS)

KBSI developed methodology and tool suite that allows military planners to consistently define, systematically evaluate, and globally share data concerning future geo-political contexts. This new planning environment, with improved quantitative analysis and knowledge sharing capabilities, produces higher quality products in standardized formats to help military planners evaluate the context or future conditions for the force plans being developed.

Hybrid Decision Wizard (HDWizard™)

KBSI developed, built, and validated a hybrid decision support toolkit that utilizes a combination of common sense reasoning, domain knowledge based reasoning, and soft computing techniques to support intelligent decision making. HDWizard™ is an agent-based decision support system that facilitates the automated generation of information from disparate and distributed data to support user-defined decision support goals.

Hybrid Framework for Information Visualization Enablers (HI-FIVE)

KBSI is designing a novel, model-based, “no-programming required” approach to information visualization. HI-FIVE provides an extendible framework for rapidly developing, configuring, and managing data displays, enabling the understanding of system of systems data.

Knowledge Discovery Wizard (KDWizard)

KBSI designed, developed, and validated an adaptive framework for knowledge discovery. This framework provides mechanisms for generating knowledge-discovery applications beginning with end-user specifications of decision objectives and sets of data sources, provides users with a larger and more focused selection of decision alternatives.

Mixed-mode Simulation-based Executable Architectures (MiMoSEA)

KBSI is developing a methodology that will rapidly and accurately support the generation and execution of mixed-mode simulations for quantitative assessments of system of system architectures.

Medical Materiel Knowledge Discoverer (MMKD)

KBSI developed a knowledge discovery system that goes beyond simple data mining to help the Department of Defense (DoD) Medical Logistics community meet the logistical challenges that follow from the new emphasis on the rapid deployment of troops in an ever widening variety of scenarios and locales.

Netcentric Operations Defense Environment (NODE™)

KBSI developed a system that uses data mining and machine learning technologies to provide a more advanced and adaptable computer network defense. Current approaches to network security focus on characterizing known attack exploits and on a perimeter-based defense, making them vulnerable to new attack variants and leaving them open to integrity compromises and insider attacks. NODE™ addresses these shortcomings by executing data mining and machine learning technologies and algorithms over the network hosts: i.e., over the entire computing fabric.

XML/IDEF3-based Training

KBSI defined a neutral format for capturing, organizing, and sharing process-related knowledge that is based on the Integrated Definition family of methods and the eXtended Markup Language (XML). Using this standard, KBSI developed an advanced authoring tool-kit for situation-based, process-centric computer-based training systems.

Adaptive Toolkit for Pattern Discovery (ATPD)

KBSI is combining new machine learning techniques with advanced rule based methods for automated data utilization pattern discovery. The ATPD technology will allow users to monitor information use, discover patterns in that use, and develop ontology models that reflect discovered relations and patterns.

Scenario Model Interoperability Toolkit (SMIT)

KBSI designed and developed a language, suite of tools, and methodology to facilitate the syntactic and semantic interoperability of scenarios and simulation models among diverse vendor-specific tools and applications. The SMIT technology provides a scenario definition language that allows for easy inter-operability among simulators and enhances scenario data sharing.

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