VELOXITI’S KNOWLEDGE GRAPH TOOLS
Click through the tabs below to view Veloxiti’s knowledge graph tool’s screenshot examples and explanations. If you have any questions about our tools and how we implement them to benefit our clients please contact us!
“Magnify relationships to make the haystack disappear.”
Representing your world as a structured model is a design activity for business teams focused on productivity, competitive advantage, innovation or sharing expertise. Developing models is a key cost driver for stronger artificial intelligence. The tools your development team chooses will determine a return on investment but thanks to decades of successful projects, we have innovations and talent to share.
Text-based sources contain structured insights about relationships between people, journal articles, training materials or analyst reports. Creating assets from text sources to enhance enterprise knowledge management efforts.
vExtract™ helps your team build operational graphs from text faster and easier. Businesses utilize our knowledge-extraction tool to identify and validate domain specific expertise organized in a structured knowledge graph.
vExtract™ is the second platform in our workflow for cognitive solutions. While most of our customers ask us to develop models closely with their teams, it is possible for your team to design using our graph tools. Come together with your best subject matter experts and our excellent knowledge engineering team to harness structured expertise in a graph.
Most artificial intelligence techniques require the creation of a model. For example machine learning systems create a model from large data sets. This approach works for low demand applications, while other, more dynamic environments, require broader intelligence capabilities. Stronger artificial intelligence is synonymous with knowledge capture and knowledge graph design.
We utilize multiple graph formats to model objects, behaviors, and concepts to acquire a unique domain model. Our team assists in exporting and compiling your graph in code format. This saves time and programming cost by shortening the development life cycle.
We help your team extract and organize text-based information so that algorithms can utilize the same information. The resulting graph is the foundation for a variety of artificial intelligence techniques. The dynamic nature of our graph platform allows for additional development considerations where stronger artificial intelligence is required.
vExtract™ is the next step for integration and continuous improvement where structured expertise is a strategic asset taking your team beyond data.
“Describe Complex Solutions By Engineering Human Expertise Into Structured Graphs”
Inspired by proven Artificial Intelligence techniques, vStudio tools focus on the development of domain specific graphs optimized for an algorithm. Each graph part, node or link hold descriptions and operators which algorithms process. This overview of algorithm activity across the graph occurs extremely fast. After decades of evaluation and testing, our knowledge graph engineering tools have emerged as a critical part of our work. The process to engineer graphs often combines philosophy, psychology, and computer science.
vGraph is the core visual knowledge graph editor in the vStudio stack. This where engineers turned graphs into executable software. Users design structured representations of their systems in two formal graph templates. vGraph’s are acyclic, directed graphs based on Belief Desire and Intent (BDI) architectures. Our graph templates are linked which provides a broader and stable representation, thereby increasing capability and reliability in critical domains like controlling robots, flying aircraft, or identifying cyber-attacks.
Users of our knowledge graph development tool can embed many neuroscience-based software techniques like Associative Memory, Case-Based Reasoning (CBR), or Natural Language Processing (NLP). All of these approaches are driven by underlying knowledge representation strategies and each approach has strengths and weaknesses depending upon the problem to be solved. This knowledge graph technology is based on early work in Artificial Intelligence which focused on the development of efficient graph algorithms and engines.
vGraph’s templates were specifically optimized over decades of testing and evaluation. It represents the State-of-the-World in two formal graph templates, one called Observe-Orient Graph the other Decide-Act Graph. Observe-Orient Graph holds representations of beliefs, and the Decide-Act Graph holds representations of Goals (i.e., desires) and Plans (i.e., intentions). Though the graphs differ with respect to their detailed implementation, both the OO and DA Graphs are acyclic, directed graphs. vGraph connects a variety of techniques to meet complexity, budget, or requirements – and can even connect to Machine Learning algorithms.
“What gets measured, gets improved.”
Today’s businesses utilize numerous data points to better understand both internal and external team performance data. New insights are required to enhance digital productivity but also to monitor human responses for critical deficiencies.
vHumanMetrics software was originally designed for battlefield leadership so that they could monitor the productivity of analysts at a workstation. Leaders began to discover real-time human-computer interaction deficiencies – and now this successful technology is ready for enterprise applications.
vHumanMetrics tracks productivity from each user class or across the world, it identifies and catalogs novel metrics, automatically tracking behaviors, all while integrating multiple data sources to build actionable real-time performance intelligence. This human-centered tool is unique in the degree to which it fuses subject matter expertise and powerful technology.
Generating user level productivity intelligence is extremely important for enterprise management teams. Measuring and improving digital information management has the potential to greatly influence the success real-time operations such as medical billing, supply chain, network administration, display controls or any interaction in a digital domain. Effective enterprise managers can now measure reactions to unexpected and unanticipated changes across their business while remaining focused on resolving problems. Empower your employees or users to identify problems and integrate shared tasks to accomplish team goals.
vHumanMetrics is a multi-faceted tool that can be customized to understand the effectiveness of human interaction within any digital domain. It is not a tool to rate employees or displace human workforces, it is simply a software measurement tool used to conduct team performance diagnostics. This new tool provides data about training inefficiencies, interface design issues, cognitive limitations under stress, or building an overall human workforce health score.
Demonstrating the value of a knowledge graph and its engine is no trivial task especially when the domain is large. To facilitate system evaluation, we developed a real-time knowledge graph analysis tools. Customer’s use our graph metrics to evaluate both individual and team performance while interacting with large complex graphs. Essentially this provides a window into the working components of our technology.
Our engine traverses the graph model and records the systems beliefs about the world, human-computer goals to be achieved, and required plans to be executed. The essential idea behind the metrics tool is to capture real-time instances of graph pathways to document what is happening in the world and what its human users intend to do about what they know. By measuring what is happening in the graphs, we can make inferences about employee and team performance.
The underlying activation of the knowledge graph structures can provide a dynamic real-time model of ‘human intention’ that has the potential to support the analytical community with novel classes of metrics. Some of these novel metrics might be:
- Cognitive workload (number of concurrently active goals across time)
- Current active plans and goals in the DA Graph
- Time and predicted time to complete ongoing tasks
- Predict collaborations between users based on shared plans & goals
- Force synchronization (timeliness of distributed sub-goal satisfaction)
Curious about how knowledge graph based tools can be put to use? Check out some examples below of successful projects we’ve done. Still have questions on how this technology could be used for your purposes? Contact us and tell us about your project needs.
Prescient® / VehicleDNA®
Note: VehicleDNA® is the commercial version of Prescient® – which has been used by the U.S. Department of Defense.
Prescient® / VehicleDNA® makes it possible to sample a vehicle in less than 10 minutes.
Oil evaluation is a two-step process. The first step is sample testing to identify wear metals and contaminants such as iron, copper, or soot and to determine sample viscosity. The second step is sample evaluation, a task that focuses on interpreting the meaning of test data. Evaluation provides the expert insight needed for determining a component’s condition.
For many years, oil testing equipment has been too expensive for use in maintenance depots or smaller service areas. However, reasonably priced test equipment exists today that is simple to use and robust enough for severe environments.
Of course, test data have no value until they are interpreted. Prescient® / VehicleDNA® addresses the all-important need for accurate sample evaluation. It uses both statistical algorithms and an expert rule-base that captures the knowledge of a world class oil evaluator. In addition, it not only diagnoses and recommends corrective actions but also explains the reasons for its assessment.
On-site analysis eliminates frustrating, time-consuming, and expensive delays. VehicleDNA® makes it possible to evaluation a truck’s condition while it is in the depot, not when it is on the road. On-site analysis dramatically reduces lost revenue due to maintenance recalls.
Furthermore, since Prescient®/VehicleDNA® software lives “in the cloud”, a vehicle’s condition history is always available at any depot and any time.