Deployment of Ontology MediationOf Information Flow

Modified from Presentations made in 2002, 2003 and 2004

This material is not specific to any project

Communicated by Paul Stephen Prueitt, PhD

DataTransfer Object

(SOI) Scoped Ontology Individual

Transactions

Script

SOI pushes information

Portal pulls information

databases

Script pulls information

Ontology Individuals have a subsumption relationship to upper abstract ontologies

Ontology Framework

Ontology Reasoner

Scoped Ontology Individuals

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Knowledge Management visualization

Knowledge Engineer visualization

client visualization

An event

This diagram is only suggestive of the architecture

DataAccess Object

(SOI) Scoped Ontology Individual

Transactions

Script

SOI pushes information

Portal pulls information

databases

Script pulls information

Ontology Individuals have a subsumption relationship to upper abstract ontologies

An event

Ontology Frameworks

Scoped ontology has a minimal context so that information in the ontology is clear, consistent and complete (the 3Cs). The transaction is a reporting event about an incident where a government official has delayed an economic transaction. If this delay is unreasonable, the government begins to violate its own rules on search and seizure activities.

Most current day government search-type reports are being recorded into one of many relational databases. In an ontology mediation of report analysis, a data access object (DAO) is created that knows the reporting structure in these databases. The DAO is populated with data specific to the transaction.

A script pulls information from databases to fill out information on entities reported in the DAO. The script pulls concept, and concept structures, from indicated upper-level and domain specific ontologies.

A Scoped Ontology Individual (SOI) has the data that was in the DAO, plus data added from SQL retrievals. The data is organized as instance data “over” concepts. The specific concepts in the SOI are those that were subsetted from upper and domain specific ontology.

Consistency checks can be run, based on various types of tests – some based on computational theories like explanatory coherence and evolutionary programming, others based on advanced link analysis.

Completeness and clarity come from how well the upper level and domain specific ontologies were developed.

Ontology Reasoner

Scoped Ontology Individuals

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Knowledge Management visualization

client visualization

The ontology reasoner is a rule type engine that “knows” OWL DL, and can perform the several types of computation that DL reasoners are designed to do. Jena is an open source reasoner that most experts feel is a good implementation of DL. The reasoner loads an ontology that is used to reason with. A second ontology is then used as the object of reasoning. A script, containing a SQL type constraint, can also be the object of reasoning.

The object of reasoning, and scripts, can be used to select that subset of the reasoning ontology that is relevant to the object of reasoning. The result can be simple, such as a Boolean value determining the computed truth of a statement, or complex such as a subsetted ontology.

The notion of Scoped Ontology Individual is one that was created for purpose of the proposed architecture. It is grounded in work on cognitive neuroscience, as will become apparent after a while.

There are two paradigms that this architecture supports (1) ontology augmentation of standard rule engines and (2) ontology mediation of information flow within a Human-centric Information Production (HIP) user interface design.

Both paradigms can reside in the same architecture, with the augmentation functions using scripts to answer specific questions for value in predicates of the rules.

Human-centric Information Production requires that the user see and understand a larger context that is given to data by the ontology. Information is then produced as the human added to information by making judgments and corrections to machine computed results.

Scoped Ontology Individuals

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The ontology reasoner can be used to take in Scoped Ontology Individuals and produce Scoped Ontology Individuals . So the reasoning process can be embedded into a cyclic process where both the reasoning ontology and the object of reasoning is being adjusted each cycle.

Scoped Ontology Individual data can be encoded into a form that is minimal in length due to the categorical collapse that is defined using the ontology frameworks. This form can be encoded into a bit stream with average length < 2,000 bytes. Visualization is over only a single ontology individual (OI) at a time.

However, the user can take some actions that make that OI go away, only to be replaced by an new OI. Transmission times are small as computational processes run over these OI. OI encodes as Ontology referential based (Orbs). Once the data is encoded as Orbs, the search process is an order of magnitude faster than indexed relational databases.

SOI

SOI

SOI

SOI

SOI

Stack of SOIs supporting analysis of analysis

Ontology Framework

Ontology Reasoner

A figure-ground relationship is made between the upper-level and domain specific ontologies and the single SOI that “comes into focus” through intentional acts made by the human.

The iterations that SOI’s allow depend on a user to drive an Actionable Intelligence Process. This visualization interface is now being designed at OntologyStream Inc.

Zackman

Ontology Framework

present

past future{ who, where, what, how, why, when }

x

{ some descriptive enumeration of types of people involved in building }

{ who, where, what, how, why }

x

{ past, present, future }

Structural Event

Ontology Framework

Producing a framework for

Semantic “expansion/contraction”

Each instance of the framework produces 15 questions.How these questions are answered depends on (a) viewpoint and (b) context

present

Memory invariances

Anticipatory mechanisms

{ who, where, what, how, why }

x

{ past, present, future}

Structural Event

Ontology Framework

Aggregating data from real world use develops historical profiles of various types. Risk/Gain abstract ontology organizes profiled transactions into Ontology Individuals (OIs).

{ who, where, what, how, why }

x

{ past, present, future}

Structural Event

Ontology Framework

The classical, existing from Greek times, six interrogatives is partitioned into three parts; {people, places, things} + { event structure with causality } + time

{ people, places, things } event structure

If we take the first two dimensions of a framework to be  

{ Anomality, Trend } union { Gain, Risk }  

And the other dimension to be: 

{ measurement, assessment, name, group, event, context, rule, policy, component, function/behavior }

 Then, in the cross product, we have four sets of ten concepts. In fact the ten concepts are five sets of two concepts – each with an interesting “oppositional scale type” relationship. 

{ measurement, assessment, name, group,

event, context, rule, policy,

component, function/behavior }

Gain Risk

Ontology Framework

Gain / Risk

Ontology

Framework .

Vulnerabilities Assessmentevent structure

Structural Event .

Ontology

Framework

Ontology is best developed in a stratified architecture, with modular ontologies having well defined ontology services (web services).

The relationship between all concepts in all upper level ontologies and the lower level ontology is that there does not exist a concept in the upper level that is not referred to by a concept in the lower levels.

What is meant by modular ontology management?

Cyber Risk upper level ontology“other” upper level ontology

Vulnerabilities ontology Attack ontologyTreat ontology

sources of data

Problem: categorize the “measurements” from a set of events

Transaction processes

Notation

e(i) = w(i)/s(i)

An event is measured by filling in slots in a data entry form, and by typing in natural language into comments fields in these entry forms.

{ e(i) }{ w(i) }

{ s(i) }

Semantic extraction

Discrete analysis

Semantic extraction is performed using one of several tools, or tools in combination with each other

Discrete analysis is mostly the manual development of ontology through the study of natural categories in how the data is understood by humans.

DataTransfer Object

(SOI) Scoped Ontology Individual

Transactions

Script

SOI pushes information

Portal pulls information

databases

Script pulls information

Ontology Individuals have a subsumption relationship to upper abstract ontologies

Ontology Framework

Ontology Reasoner

Scoped Ontology Individuals

Hum

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Knowledge Management visualization

Knowledge Engineer visualization

client visualization

An event

Review of high level architecture