Not part of three month project Advanced Architectures: Simple and Advanced Orb Architecture OntologyStream architecture The first part of this presentation (Slides 2 – 12) can be demonstrated within six weeks and refined within the time frame of 3 and ½ months 50% time in May 2004 and 100% time June – August 2004 Dr. Prueitt and programming support from Nathan Einwechter The Simple Architecture develops Orbs using human in the loop activities to produce and use simple Orb resources The Advanced Architecture (slides 14 – 24) develops Orbs using human information production techniques plus Orb based data mining processes. Advanced architecture is an additional 6 months to one year effort.

Encoding Engine Orb Visualization Event Chemistry Repository Human knowledge of sub-structure supports mutual induction Human Abduction about event Chemistry supports Anticipation Data Real time analysis categoricalAbstraction sub-structure Repository Real time or legacy Simple architecture Diagram from Prueitt (2004) Simple Architecture: depends strongly on human knowledge

Compact encoding of ordered triples in hash tables Event parser Events of interest with selected sub-structure content Generalized n-gram looks for conditional co-occurrence of sub- structure in the data Manual selection of measurement Instrumentation/measurement 1)Perl code – simple and independent from Orb code 2)Rules can be redeveloped by subject matter expert easily 3)Output always in the form of Orbs SLIP Analysis and Visualization Knowledge Operating System mutual induction abduction Work product re-use Diagram from Prueitt (2004) Simple Architecture: instrumentation and measurement

Confirmed/refined Subject Matter Indictors Selection from Reified Indicators Orb technologies provide concept and metaconcept metadata Editor, subject matter expert Diagram from Prueitt (2004) Simple Architecture: existing research tool capability Existing research tool capability Can be demonstrated in prototype in May 2004

Confirmed/refined Subject Matter Indictors retrieval engine Case histories with selected subject matter content Selection from Reified Indicators Knowledge work / feed-back Full-cycle event profile retrieval Compact encoding of ordered triples in hash tables Orbs Diagram from Prueitt (2004) Simple Architecture: Human reification of Orb structures

Text analysis technologies provide ontology services Use of ASCII encoding of event structure allows event structure mining functions base on simple general framework instrumentation and encoding Diagram from Prueitt (2004) Simple Architecture: Encoding innovations generalized from text analysis

Patent pending technologies provide knowledge representational services, based on ASCII word descriptors and co-occurrence patterns Diagram from Prueitt (2004) Simple Architecture: encoding innovations

Precision Recall # of relevant event-profiles retrieved total # of event-profiles retrieved # of relevant event-profiles retrieved total # of relevant event-profiles in collection Precision = Recall = Diagram from Prueitt (2004) Simple Architecture: precision recall

If you enter a descriptor, and that descriptor is in an Orb or database, you get all data linked to that descriptor, regardless of whether or not that descriptor is relevant Search Characteristics Precision Recall High recall, low precision = Time wasted with irrelevant data Relevant items may be retrieved but overlooked Diagram from Prueitt (2004) Simple Architecture: precision recall

If highly relevant data is in the Orb or database but none of the descriptors you enter are really relevant, then the data needed will not be retrieved Search Characteristics (cont.) Precision Recall High precision, low recall = Greater chance for error Inconsistent results Time wasted through redundant effort Diagram from Prueitt (2004) Simple Architecture: precision recall

Question: How do we “bend the curve so we get more of what we really need and less of what we don’t need? Diagram from Prueitt (2004) Simple Architecture: precision recall

Answer: The Orbs initially have local structure but no global structure and yet are easy to organize into a specific global structure Categorical Abstraction creates similarity relationships which can be modified in real time using mutual induction (human reification through cognitive priming and visualization) Event Chemistry stores past work product into things-to-try, where the resulting organization expresses real science regarding event structure and sub-structure to function relationships Now the retrieval task has by-passed schema structure imposed in classical database models and allows real time intuition to play an un-encumbered role Diagram from Prueitt (2004) Simple Architecture: basic intuition behind Orbs

Not part of three month project Advanced Architectures: Advanced Architecture developed as part of OntologyStream work on “Total Information Awareness” architecture The foundation of this work is established in the Simple Architecture, which gives a stepping stone to the Advanced work The Simple Architecture develops Orbs using human in the loop activities The Advanced Architecture develops Orbs using human information production techniques plus Orb based data mining processes

Transaction Layer Presentation Layer Orbs Parsing, tagging, routing, categorizing, clustering DOF Ontology Lens Schema Logics Topic Maps Event parsing RAM memory System files Hash tables Legend Orb = Ontology referential base DOF = Differential Ontology Framework, including stochastic, latent semantic indexing and ontology services Data encoding Subject-matter Experts Not part of three month project Advanced Architectures: Complete architecture

Inter-Role Collaboration using Ontology Synchronous Collaboration Periodic Update Knowledge Repository Role and Event Specific View Views Knowledge Worker Not part of three month project Advanced Architectures: Distributed Collaboration

Production of Event Measurement Metrics Implicit Ontology Implicit Ontology d  C response T ground ( B 1 ) T ground ( B 2 ) T ground ( B 3 ) T ground ( B 4 ) T ground ( B 5 ) Explicit Ontology For each response, d, the implicit ontology produces a set of metrics, { m k }, and these metrics are used as the atoms of a logic. These atoms are used to produce an explicit ontology. The logic is then equipped with a set of inference rules. Evaluations rules are then added to produce an inference about the response set. Diagram from Prueitt (2002) Advanced Architecture: event metrics

Differential Ontology Framework By the expression “Differential Ontology” we mean the interchange of structural information between Implicit (machine-based) Ontology and Explicit (machine-based) Ontology by Implicit Ontology the variations of latent semantic indexing. These are continuum mathematics with only partial representation on the computer. by Explicit Ontology we mean an bag of ordered triples { }, where a and b are locations and r is a relational type, organized into a graph structure, accompanied by first order predicate logic. This is a discrete formalism. Implicit Ontology Implicit Ontology Explicit Ontology Diagram from Prueitt (2002) Advanced Architecture: differential ontology framework

Tri-level Architecture : bases for abduction of event function and mutual induction from substructure memory with human in the loop The Tri-level architecture is based on the study of natural systems that exist as transient stabilities far from equilibrium. The most basic element of this study is the Process Compartment Hypothesis (PCH) that makes the observation that “systems” come into being, have a stable period (of autopoiesis) and then collapse. Human cognition is modeled in exactly the same way. Human mental events are modeled as the aggregation of elements of memory shaped by anticipation. The tri-level architecture for machine intelligence is developed to reflect the PCH. A set of basic event atoms are developed through observation and human analysis. Event structures are then expressed using these atoms, and only these atoms, and over time a theory of event chemistry is developed and reified. Diagrams from Prueitt (1996) Advanced Architecture: Tri-level Architecture

cA/eC : categoricalAbstraction and eventChemistry Neuroscience informs us that the physical process that brings the human experience of the past to the present moment involves three stages. 1) First, measured states of the world are parceled into substructural categories. 2) An accommodation process organizes substructural categories as a by-product of learning. 3) Finally, the substructural elements are evoked by the properties of real time stimulus to produce an emergent composition in which the memory is mixed with anticipation. Each of these three processes involves the emergence of attractor points in physically distinct organizational strata. The study of Stratified Complexity appeals first to foundational work in quantum mechanics and then to disciplines such as cultural anthropology and social-biology. categoricalAbstraction (cA) is the measurement of the invariance of data patterns using finite set of logical atoms derived from the measurement. eventChemistry (eC) is a theory of type that depends on having anticipatory processes modeled in the form of aggregation rules, where the aggregation is of the cA logical atoms. Diagram from Prueitt (1995) Advanced Architecture: eventChemistry from Dr. Paul Prueitt

gF : two examples We have generalized from a physical theory of about formative processes, to a computational architecture based on frameworks. Various forms are conjectured to exist as part of emergent classes, and to have a periodic table – like, in many ways, the atomic period table. The Sowa-Ballard Framework has 18 “semantic atoms”. The Zachman has 30 atoms. Zackman Framework “According to Alvin Toffler, knowledge will become the central resource of the advanced economy, and because it reduces the need for other resources, its value will soar. (Alvin Toffler, Power Shift, 1990). Using architectural constructs such as the Zackman Framework, can prepare organizations to tap their inner banks of knowledge to improve their competitive positions in the twenty-first- century. generalFramework (gF) theory constructs cA/eC knowledge based on “conversation” with humans. The general form of a framework is constructed based on specific knowledge of an application domain Ballard/Sowa Framework Diagram from Prueitt (2002) Advanced Architecture: gF from Dr. Paul Prueitt

Diagram from Prueitt (1995) Advanced Architecture: mutual Induction from Dr. Paul Prueitt mI : mutual Induction between machine memory and human Introspection Mutual induction occurs when cognitive priming triggers mental events in humans. If an incomplete pattern is presented using SLIP visualization of topological neighborhoods, in Orb structure, then Human- centric Information is Produced (HIP). SAR = structure activity relationships

Situational Logic Construction A latent technology transform, T ground, is used to produce simple metrics on membership of sub-structure in event structure from the response collection C response in the categories defined by the contents of the bins C exemplar = B 1  B n2  B q These bins are represented in the situational logic as the logical atoms A, from which a specific logic is constructed. These atoms are then endowed with a set of q real numbers that are passed to an Inference Processor. The set of q real numbers are computed from a formal “evaluations of the structural relationship between logic atoms” using the Ontology Lens. Atom a  { r 1, r 2,..., r q } The process of developing a situational logic treats new data structure as axioms, and then a process of reduction of axioms to logical atoms occurs (Russian Semiotics). The reduction also requires the Ontology Lens, (invented 2002 by Prueitt). Diagram from Prueitt (2003) Advanced Architecture: situational logics from Dr. Paul Prueitt

The Ontology Len (discovered by Prueitt, 2002) is a structural focus “instrument” that is designed to allow non-computer scientists to specify high quality exemplar sets. This is done with an Implicit Ontology to Explicit Ontology (IO-EO) loop. When the user puts a new unit into a bin or removes a unit from a bin, then the IO-EO loop will produce a different result. It is the human responsibility to govern the IO-EO loops so that the results have the properties that the human wants, mostly independence of categories, but perhaps some specific (and maybe interesting) “category entanglements”. A graphic representation of what we call a “Latent Semantics Index structural similarity matrix”. The similarity is called structural because the exact notion of semantic similarity is not known from this algorithmic computation by itself. The paragraphs of a small exemplar set (see appendix A) are ordered as labels for the columns and rows. One would expect that a paragraph would be structurally similar with itself, and this is in fact what one sees as a set of dots (representing a value of 1) down the diagonal. Diagram from Prueitt (2002) Advanced Architecture: ontology lens from Dr. Paul Prueitt

Minimal Work Flow Production of the Explicit Ontology Implicit Ontology Implicit Ontology C ground = C exemplar = C response T ground (B 1 ) T ground (B 2 ) T ground (B 3 ) T ground (B 4 ) T ground (B 5 ) Ontology Lens Schema-independent data Schema-independent data is developed from the Ontology Lens, in the form { } Where a and b are categories defined by the exemplar set,, and r is a measure of relationship. Diagram from Prueitt (2002) Advanced Architecture: work flow