ontology case

8/13/2019 Ontology Case

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The DHS Ontology Case

Presentation by OntologyStream Inc

Paul Stephen Prueitt, PhD

19 March 2005

Ontology Tutorial 1, copyright, Paul S Prueitt 2005


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Ontology technology exists in the market

However,practical problems block progress in designing and implementing DHS wide

ontology technology. Business process mythology, like Earned Value program management,

is not focusing on the right questions and maturity models for software development haveprecluded most innovations that come from outside the relational database paradigm.

These practical problems are also partially a consequent of

1. Some specific institutional behaviors related to traditional program management.

2. Confusion caused by long term, and heavily funded Artificial Intelligence marketing activities


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As a general proposition, through out the federal government, quality metrics

are not guiding management decisions supporting:

1) Quick transitions from database centered information technology to XML

based Semantic Web technology.

2) Transitions from XML repositories to ontology mediated Total Information

Awareness, with Informational Transparency, in Secure Channels.

Emerging Semantic Web

Standards

DHS Ontology

1) World-wide Trade Data

2) Investigation Targeting

3) Risks, Threats and

Vulnerabilities

4) Policy Enforcement

?


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Diagram from Prueitt, 2003


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Diagram from Prueitt, 2003

First two steps

are missing

Seven step

AIPM

is not

completeRDBMS


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The measurement/instrumentation task

First two steps

in the AIPM

Measurement is part of the semantic

extraction task, and is accomplished with a

known set of techniques

Latent semantic technologies

Some sort of n-gram measurement

with encoding into hash tables or internal

ontology representation (CCM and

NdCore, perhaps AeroText and

Converas process ontology (?), Orbs,

Hilbert encoding, CoreTalk/Cubicon.

Stochastic and neural/ genetic

architectures


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One model for semantic extraction explicitly focuses on the first two aspects of

the AIPM; e.g. instrumentation/measurement and data-encoding/interpretation


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Actionable Intelligence Process Modelhas an action-perception event

cycle.

Stratified ontologysupports the use of this cycle to produce knowledge of

attack and anticipatory mechanismsbased on the measurement of sub-structural categorical invariance.

Work flow and process

ontologyis available as a

basis for encoding knowledge

of anticipatory response

mechanisms.

Categorical Invariance is

measured, using Orbs

(Ontology referential bases)

for example or CCM(Contiguous Connection

Model) encoding, and

organized as a resource for

RDF triples using some lite

OWL OIL.


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Distributed ontology management is already

available in some military activities


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distributed Ontology

Management Architecture

d-OMA

Ontology ArchitectureMarch 10, 2005

Version 3.0

Part of a series on the nature of machine encoding of sets of concepts


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Community

Transition

BenchmarkHigher level Ontologies as

part of the d-OMA

Localized Ontologies within

an DAML type agency

structure

CommunityCommunity


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Higher level Ontologies

Localized Ontologies

Links to Web services

and Internal R&D

Community

Community

Community


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A reconciliation process

is required between

ontology services.

Ontology users have the

following roles:

Knowledge engineers

Information mediators

( e.g. ontology librarians )

Community workers

(e.g. analysts)

Knowledge

Engineers

Ontology

librarian

Intelligence TargetingAnalyst

Community


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Higher level Ontologies

Localized Ontologies

Real time acquisition of

new concepts, and

modifications toexisting concepts are

made via an piece of

software called

Ontology Use Agent

Ontology Use Agents

have various type of

functions expressed in

accordance with roles

Mature scholarship from

evolutionary psychology

research communities.

Community


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Human and system interaction with a

common Ontology Structure

Ontology Presentation

Part of a series on the nature of machine encodingof sets of concepts


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First, and b efore al l else, an computer based ontology is a

{ concepts }

In the natural, physical, sciences an ontology is the causes of those

things that one can interact with in Nature.

Physical science informs usthat a formative process is involved

in the expression of natural ontology in natural settings.

First principles

The set of our personal and private conceptsis often thought to be the causes of how we

reason as hum ans. This metaphoris operational in many peoples understanding about the

nature and usefulness of machine encoded ontology. But this metaphor can also mislead us!!!!

Extensive literatures indicate that the Artificial Intelligence (AI) mytho logyhas leadmany to believe that the reasoning of an ontology might be the same as the

reasoning of a human in all cases.

This inference is not properbecause the truthful-ness of this inference has not been demonstrated by natural science,

and perhaps cannot be d emonstratedno matter what the levels of government funding for AI.

AI is discounted in Tim Berners Lees notion of the Semantic Web.


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Tim Berners Lee Notion of Semantic Web

One consequence of acknowledging this difference is to elevate the work of the

authors of the OASIS standards, in part icular Top ic Maps. In Topic Maps wehave an open wor ld assumpt ionand very little emphasis on computational

inference. Human knowledge is represented in a shallow form, and

visualization is used to manage this representation.

Computation with topic maps ANDOWL ontologies work together withXML

repositories.

The point being

made here is thatthe notion of

inference is very

different depending

if one is talking

about the human

sideor themachine sideof

the Semantic Web.


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{ concepts }First principles

Let is use only set theory to consider Tim Berners Lees

notion of Semantic Web.

Let C = { concepts } and B be a subset ofC

Human and/or machine computation

creates a well formed query

Some software

B is a subset ofC

Subsetting

function

The subsetting function might be an

ask-tell

interaction between two ontologies


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First principles

Situational Ontology

SoftwareThe knowledge repository acts as aperceptual groundin a figure-ground

relationship.

The ontology sub-setting function has pulled part, but not all, of the background into a

situational focus. This first principle is consistent with perceptual physics and thus is

informed by natural science.


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Extending Ontology Structure over

legacy information systems

Part of a series on the nature of machine encoding of sets of concepts

(The following slides are from OntologyStreams Ontology Presentation VII General Background)


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Functional specs

Ontology Use Start-up Use Model

Model: Steady State Ontology System

Components: Framework for Query Entity

Data Access: Steady State Ontology System

Framework for Query Entity

Building ontology from data flow

Using Ontology

Ontology Generating Framework

The inverse problem: generating synthetic data

Finding data regularity in its natural contextualization

Presentation Contents


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Functional specs

Functional specs:

1. Human-centric: must be human (individual) centric in design and

function

2. Support data retrieval:must act as a data retrieval mechanism3. Event structure measurement:must assist in the definition of data

acquisition requirements on an on-going basis

4. Interactive:must support multiple interacting ontologies

5. Real Time:must aid in real time problem solving and in the long term

management of specific sets of concepts

Note:Ontology mediated knowledge systems have operational properties that are quite different from traditional

relational database information systems. These five functional specs have been reviewed by a small community of

professional ontologists, as has been deemed correct for knowledge systems.


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Start-up Use Model II

Transaction process

Knowledge

Base

Entity

updates

ReasonerQuery

entities

startup

inferences

Start-up Use Case Step 2

Since instance data is much larger (2 or more

magnitudes larger) than the knowledge base, the

instance data is managed in a separate start-up

process.

Instance data


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Model: Steady State Ontology System

Transaction process

Knowledge

Base

Entity

updates

Query

entities

inferences

Inference Mgr

Query Mgr

Data Access Mgr

Reasoner

Ontology Mgr

Instance dataInstance data may be remote or local.

Local data is on the same network as the

knowledge base.


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Knowledge

Base

Data

Data Access Mgr

Data Access: Steady State Ontology System

using the OWL standard **

The RDF knowledge base ** is a set of conceptsexpressed as a set:

{ < subject, verb, predicate > }

and the data is either XML or a data structure

such as one would have as a C construct.

The Data Access Manager must manage the

mapping between local data stores (sometimes

having millions of elements) and the set ofconcepts.

The remote data may have many persistence

forms, and will be accessed via a data object.

Instance data

Data Object

Pipes and

Threads

** We use RDF and OWL as a standard to create minimal and well knowledge inference capabilities.


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Framework from Knowledge Management point of view

Transaction processes

Query

entities

Query Manager

Real time analysis is

supported through the

development and use of query

entities.

These entities have regular

structure and are managed

within a Framework.


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Building ontology from data flow

Transaction processes A model of the "causes" of transactiondata.

The model is based on, grounded in,

the concept of "occurrence in the real

world", or "event".

Associated with each event, we may

have a "measurement".

So we have a set of events

{ e(i) }

where i is a counter.

Some of the fields MAY not be used.

Later the number of fields in any

"findings data flow" may increase or

decrease without us caring at all.

Objective: We convert a stream of

event measurements into an

transaction ontology, and createauxiliary processes that will use a

general r isk onto log y, an onto logy

about proc ess opt im ization, and other

utility ontologies.


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Using Ontology


Queryentities

Query Manager

Consider a set of events { e(i) }where i is a

counter.

Each event will have a weakly wstructured (free

form text) and structured scomponent. So we use

the notation

e = w/s or

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

Knowledge

Base

Data

Instance data


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Ontology Generating Framework


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.

Observation: Given real data, one can categorize

the set of events due to the nature of the

information filled in.

{ 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 analysisis mostly the manual

development of ontology through the study of natural

categories in how the data is understood by

humans.


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{ w(i) }

{ s(i) }

Semantic extraction

Discrete analysis

Each s(i)is a record from a single table.

Suppose there are 120 columns. Each column has

values, sometimes empty. Fix the counter at *.

Let s(*, j) , j = 1, . . . , 120 be the columns. We can call

these columns also using the term slot.

Now for each s(*, j) list the values that are observed to

be in that column. These values are the possible

fillers for the associated slot.

Free form text is weakly structured.

The set

{ w(i) }

is a text corpus that we would like to associate with

several ontologies.

Each association is made as exploratory activities with

specific goals.

For each eventwe may have zero or more free text

fields. Suppose we concatenate these, into one text

unit, and perhaps also develop some metadata (in

some way) that will help contextualize the semanticextraction process. We label this unit as w(i).

Finding data regularity in its natural contextualization

Regularity in data flowis caused by the events

occurring in the external world. Thus the instances

of specific data in data records provide to the

knowledge system a measurement of the events in

the external world.


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Data regularity in context . Patterns and invariance

XML and related standards

community open and protected standards (CoreTalk, Rosetta Net,

ebXML)

.NET component management

J2EE frameworksSpring

General framework constructions

autonomous extraction (Hilbert encoding of data into keyless hash

tables, SLIP Shallow Link analysis Iterated Scatter-Gather an

Parcelation) Core, AeroText (etc) semantic extraction using processknowledge and text input


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The role of community

1) A community of practice provide a reif ication processthat is

Human centric (geographical-community / functional-community)

2) Each community may have locally instantiatedOWL ontology

with topic map visualization.

a) Consistently and completeness is managed locally as a property of the human individuals,

acting within a community, and a locally persisted history of informational tr ansactions

with his/her agent

b) I ndividual agentscan query for and acquire XML based Web Services, negotiate with other

agents and create reconciliation processes involving designated agencies.

3) Knowledge engineers act on the behalf of policy makers to

reify new concepts,

delete concepts and to

instantiate reconcil iation containers


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Establishing coherence in natural knowledge representation

1) Coherence is a physical phenomenonseen in lasers

a) Brain function depends critically on electromagnetic coherence

b) Incoherence, e.g. non-rationality, and in-completeness are two separate contrasting issues

to the issue of coherence

c) Mathematics , and therefore computer science and logic, have completeness and

consistency issues that are well established and well studied

2) Logical coherence is sometimes treated as consistency in logic

a) One may think that one has logical consistency and yet this property of the full system was lost at the

last transaction within the Ontology

b) The act of attempting to find a complete representation of information organization is sometimes

called reification, and reification efforts works against efforts to retain consistency

3) Human usabil i tyoften is a function of a proper balance

between logic and agility


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Understanding multiple viewpoints

1) Logical consistency and single minded-nessare operationallinked together in most current generation decision support

systems. Database schema legacy issues. Schema servers, FEA (FederalEnterprise Architecture) standards, schema independent data systems

2) Observation: Human cognitive capabil itieshave far moreagility than current generation decision support systems

3) The topic map standard(2001, Newcomb and Briezski ) was

specifically developed to address the non-agility of Semantic

Web standards based on OWL and RDF. (Ontopia, Steve Pepper)

4) Combining XML repositories, OWL, distributed agent

architectures and Topic Maps is expressed as Stratified

Ontology Management


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Detection of novelty

Scenario: an targeting and search analyst at the Port of Seattle is

only partially aware of why she feels uncomfortableaboutsome characteristic of a shipment from Sweden. The feeling is

expressed in a hand written finding and fed into a document

management repository for findings. A targeting and search

analyst at the Port of LA expresses a factabout a similarshipment without knowing of her colleagues sense of

discomfort.

1) Conceptual roll-up techniquesare used on a minute by minute basis to create a

viewable topic map over occurrences of concepts expressed in findings.

2) L ink analysisconnects an alert about uncertainty in the Seattle finding to the factfrom LA to produce new information related to a known vulnerability and attack

pattern.

3) New knowledge forms are propagated into OWL instanti ated ontology

and rules and viewed using Topic Maps.


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Agent architecture

Scenario: Human analysts provide situational awareness via tacit

knowledge, personally agent mediated interactions with agentstructures, and human to human communications. A model of

threats and vulnerabil i tieshas evolved but does not account

for a specific new strategy being developed by a smart

smuggler. The smuggler games the current practices in orderto bring illegal elements into the United States

1) The model of threats and vulnerabilities expresses as a reification process from

various techniques and encoded OWL/Protg ontology with rules

2) Global Ontology: The model is maintained via near real time agencyprocesses

under the observation, active review, of knowledge engineers and program

managers working with knowledge of policy and event structure

3) Local Ontology: Information is propagated to individual analysts via alerts and

ontology management services controlled by the localized agent (of the person)


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New (1/30/2005) tutorial on automated

extraction of ontology from free form text:

http://www.bcngroup.org/beadgames/anticipatoryWeb/23.htm
http://www.bcngroup.org/beadgames/anticipatoryWeb/23.htmhttp://www.bcngroup.org/beadgames/anticipatoryWeb/23.htm

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