Pragatiís Expozť knowledge engineering environment, is an integrated suite of cognitive assistance tools, based around the core capability of clustering concepts which have structural and semantic similarities in their descriptions. Pragati Inc. has focused on the core technology, MVP-CA (Multi-ViewPoint Clustering Analysis) for the last 13 years developing proprietary heuristic algorithms for clustering as well as devising cognitive aids for exposing the meaningful results from clustering.
Pragati's core technology provides the necessary infrastructure for analyzing large knowledge-based systems. The basic philosophy of the MVP-CA technology is that any large system has to be first decomposed understood with different perspectives so that it can be analyzed to meet any software engineering goals. The multi-view point methodology is founded on the principle that no single structuring principle or abstraction hierarchy is sufficient to understand complex systems. The presentation of a knowledge-based system from several different viewpoints gives valuable leverage for various life-cycle activities by making the knowledge-based system more comprehensible and tractable.
Expozť is a human-machine interface tool that eliminates/greatly reduces the cognitive overload found in currently used analysis tools and manual methods. It suitably abstracts, structures and clusters knowledge in various information systems such as, expert systems, knowledge-bases, databases, and stylized natural-language text, in a manner that facilitates the following goals:
High-level understanding of the software system, both from hierarchical (detail to abstract) and orthogonal (contextually distinct) perspectives.
Recognition of reuse opportunities in the systems through semi-automated component and template formation.
Automated mapping and alignment support for multiply authored systems by drawing attention to common/overlapping contexts in software.
Long-term quality assurance of software systems by exposing "infelicitous" knowledge entry patterns, inconsistent and redundant concepts for meaningful knowledge repair on the information system.
Ontological engineering support for building ontologies and taxonomies by exposing salient concepts from a legacy information system and suggesting appropriate placement of domain terms in an ontological hierarchy.
Expozť Tool Snapshots
Iterative Ontology Development (IOD) Tool
Large amounts of unstructured but stylized natural language text exist in today's data and knowledge repositories. While simple text-based search techniques can often retrieve documents that are at least somewhat relevant to a human analyst's needs, the information contained in those documents is effectively opaque to knowledge-based systems and databases. This means that the information is also inaccessible to analysts who wish to employ query, manipulation, and reasoning tools based on the semantic content of the text.
Iterative Ontology Development (IOD) Tool, is a prototype semi-automated, cluster-based information extraction tool that enables analysts to rapidly create structured representations of the information present in stylized natural language text, thereby rendering the content accessible to semantically aware query, manipulation, and reasoning tools.