Copyright
2005 BCNGroup
Why a
roadmap is needed for semantic technology adoption
In 2005 everyone knows what a horse and buggy is
and what an automobile is. Each person
in our society knows the story of the emergence of the automobile manufacturing
business sector and the American love affair with the automobile.
We do not know to what extent meaning might be
captured in a “semantic web”.
We have not experienced anything that informs us about what semantic technology
might become. Relevant issues in linguistics, social theory and the nature of
science are not well known in departments of computer science. Most of computer science is shaped by
engineering theory and scientific reductionism.
A roadmap is needed. One is provided here by a group of natural scientists and
mathematicians.
We propose a broad program to establish the intellectual
and technology foundation to a science of knowledge systems, and to integrate
and deliver to the market place information science based on the proper use of
what is called “machine encoded ontology”.
The first delivery of ontology-based technology
can be accomplished within a few months, given our previous work and the
existing technology components.
BCNGroup scientists recommend a demonstration
program that has three parts organized in two phases.
Phase 1
1) Technology integration
Phase 2:
2) Advanced knowledge
management certification
3) Ontology development.
Our proposal addresses these parts one at a time,
the first part is proposed at a cost of $750,000 over period of six
months.
The first part depends on our (already) having
completed a principled selection of advanced knowledge management systems,
semantic extraction systems and data persistence systems.
In each case patents protect the underlying
technology and allow a science advisory board to develop an in-depth description
of each technology component. We have
developed curriculum that exposits the philosophical principles on which their
software user interfaces are dependant.
This curriculum is being readied for delivery as knowledge management
certification and as text books designed for university curriculum.
Beyond the first deployment, the concept of a
knowledge sharing foundation
is proposed, and is being readied as a “Red Hat” type business model. This is not, however, designed as a business. Rather the knowledge sharing foundation is
designed as a cultural institution directed to found the knowledge sciences and
to develop curriculum that helps average Americans and individuals all over the
world.
Executive
overview
A human-centric
information production
capability is defined and existing, commercial, software identified. A distributed information system is
specified that will enable the real time representation and sharing of human
knowledge about situations. A global
information framework is used as a human control interface over complex
ontology.
Example:
Aircraft landing at a specific airport will express behavioral patterns. An airport ontology and aircraft landing
ontology is used to provide an interpretation of the behavioral patterns
expressed in each landing. Over time,
the observed behavioral patterns lead to early diagnosis of risks. A human looks at the patterns and makes
judgments based on personally held tacit knowledge. New concepts about the patterns are encoded as meta-data. The patterns themselves are encoded as new
behavioral ontology reflecting the history of observations about aircraft
landing at a specific airport.
Inputs come
from any reporting-software-system. An
example is US Customs and Border Protection reports on search and targeting
operations or administrative rulings on tariff codes. Inputs can be developed from any event reporting mechanism,
whether written reports or reports that involve the manual development or
modification of ontology.
Outputs
include a computable and visualizable historical record about situations
reported. The record is expressed as
ontology and human visualization of this record is provided.
Visualization of
graphical structure requires human perception to evoke an experience of
knowledge about a specific situation, or an event space. Graph labels suggest meaning in much the
same way as sentences suggest meaning when humans compose sentences.
Figure 2: Visualization of concept
indicators in a collection of fables
Language dependence is not fully achieved, for reasons that have to do with differences between
natural languages. Each natural language has language dependant
characteristics. In principle, our
technology establishes a foundation for using ontological models having
correspondences to sets of concept representations.
Ontological models are
envisioned as being a type of “interlingua”, not of words composed in grammar,
but as systems of signs that are interpreted by humans in various natural
language settings.
Ontological models
provide metadata that indicate where possible misunderstanding might occur,
thus ontological models provide a complex formalism to help in the translation
and transcription of meaning from one human language to another. Like mathematical models, ontologies are
useful as enablers of computations based on the structure of the defined sets
of concepts. In mathematic the concepts
are those related to field dynamics and to the conservation laws of
physics. The ontological models we are
developing are about more complex subjects, such as the intentions of a
determined enemy to bring elements of bioterrorism into the United
States.
Ontology extends
Hilbert mathematics from
deterministic systems to complex systems having uncertainty and
under-determined constraints.
Representing real advances in objective science, ontological models
provide a computational basis for the real time projection of human knowledge
within communities of practice. Medical
informatics and bioinformatics are demonstrating value in two early
utilizations of ontological modeling. A
construction over conceptual representations of topics in social discourse has
been applied to medical literatures.
These constructions bring relevant information to medical research
communities. Bio-event defense
architecture has been outlined and proposed by members of the BCNGroup. Application in pharmaceutical research and
development can be identified under non-disclosure agreements. These applications involve conceptual
modeling as well as direct modeling of the ontology involved in gene
expression.
Subject matter
indicators are represented in computable ontology constructions, including the
most simple, and yet most powerful, one based on a concept label and one to n
(undetermined integer) word, word stem or word phrases. These concept labels are formally represented
in several knowledge base technologies as an n-tuple.
< a0, a1, . . . an
>
Our proposed
deployment, takes these technologies and integrates them. We also add a data mining process where fast
convolution transforms have both a mathematical formulation as well as
operational realizations of these transforms as precise data retrieval methods.
Convolution operators
are fast computed over the elements of a set of concept representations. The
convolution operator results in the separation of context and merging of
ontology. One pass over an in-memory
data structure is sufficient.
Figure 1: Co-occurrence patterns with
“hash”
From these
technologies, sets of concept representations are developed and made accessible
to search and analysis. Several
semantic extraction tools are integrated with a commercially available RDF
repository . The output of these semantic extraction
tools is a set of subject matter indicators, represented as RDF
statements. The basic set operations
are available as formalism and thus the standardization of these constructions
are a matter of public record.
It is vital to
recognize that the subject matter indicators are given final interpretations by
knowledgeable humans.
Our
Phase 1 proposal is to integrate five stand-alone COTS
products; two semantic extraction systems, two knowledge management systems,
and a taxonomy acquisition system, with an Open Source ontology editing tool
(Protégé), an Open Source document repository system, and a simple RDF
(Resource Description Framework) data repository.
Knowledge management systems address the need to
enhance the quality of reporting as well as to make managed vocabularies
available as an interface between ontology constructions and normal human
use. Semantic extraction systems
convert free form text into structured metadata. Existing taxonomy is draw from
across the world.
These products are to be configured using J2EE web
services and a server binding protocol based on a high level scripting language
called Python.
An
advanced RDF repository is used to provide persistent storage for organized
sets of concept representations. Concept representations and organized
collections of these representations are convertible to standard ontology
representation languages. A formal
theory about co-occurrence patterns is used to express a category of
mathematical constructions called convolution operators.
Section 1: Proof of concept
The customer wants a
global information framework that:
1.
Has
a high degree of language independence.
2.
Compresses
data regularity, primarily co-occurrence patterns, into structurally organized
concept representations
3.
Converts
uncertain, sketchy, sometimes incorrect instance information into clear,
concise and complete reports about a situation.
4.
Provides
a means to develop global synthesis over a large event space.
Ontological modeling
also provides new types of information technology features that are not
anticipated by the customer. For
example, the set of concept representation, and ontological model encoding
structures, allows access to past information instantaneously, without
relational database indexing.
The BCNGroup has
specified a six month technology integration and a fully functional beta site
deployment. The beta deployment will
serve as a prototype for additional deployments based on similar
principles.
Our technology delivers
means for deriving language independent situation and global event analysis
based on ontological models. The
software systems integrate semantic extraction in English, Arabic and
German. Other languages are possible
using the same techniques.
General principles
related to the differential ontology framework are laid out so that multiple
languages are integrated into constructed elements of a single explicit
ontology. The integrated system will
demonstrate features that are not available from any current semantic or
knowledge management system.
Our general principles
are part of an emerging discipline related to the measurement of complex
systems and the use of formal ontology as a means to abstract knowledge about
situations that arise in a complex world.
Iterative modifications to visualized ontology lead to an adaptation of
ontological models of these situations.
The Global Information
Framework depends critically in having a user interface that allows any subject
matter expert to have visual access over situationally relevant concept
representations. Situational relevance requires a subsetting mechanism. Control over concept subsetting mechanisms
serves to focus the attention of the user into part of the elements of an
ontological model. Once elements are
identified, by our selective attention mechanisms, these elements are extended
using ontological inferencing to produce a coherent view of what is known and
encoded within the representational space.
User input in each phase of this process is not merely supported, user
input is required to achieve relevant and fidelity.
This human-centric,
ontological model based, approach creates a distinct alternative to classical
expert systems and artificial intelligence approaches. Our alternative creates a higher dependency
on human involvement and requires that some humans accept responsibility over
decisions. Clearly the cultural
barriers we, the BCNGroup scientists, have experienced have something to do
with the requirement that humans accept more responsibility and are subject to
rational outcome metrics. We have been
forced to take the position that the artificial intelligence funding is wrong
minded both based on arguments from the natural sciences and because the effect
of artificial intelligence expenditures is to allow the consulting IT
industries to not take complete responsibility for past, current of future performance
outcomes.
Relevant cognitive
neuroscience tells us that attentional focus evokes cognitive responses. This science also tells us a great deal
about how this attentional focus is managed by the brain system
. As the BCNGroup moves forward the Second
School (see Section 10) we
will develop user profiles that use the Human-markup language standard
to bring elements of cognitive engineering into the interface design. A simple control interface has been designed
from existing text based and mouse based interfaces . The beta deployment of this interface is
within two months of funding ($750,000).
Results from cognitive
neuroscience have been used to design user interface elements that change the
visualization based on user commands and actions. These changes in visualized
state produce shifts in figure/ground perception.
Section 2: Context
and objectives
As in physical and
engineering sciences, the results of collective intellectual work lead to
advances in science, including economic and biological science.
In the context of other types of collective work, such as in
financial services, intelligence analysis, fiduciary reporting, compliance
reporting, complex control, and biological science; the GIFT provides a means
to produce a type of collective intelligence.
Subject matter experts create this collective intelligence using our
software components.
Global information frameworks provide features that
are not available from any current semantic technology or knowledge management
system. It is in this sense that the
technology is a gift to our society.
GIFT was designed specifically to address global
analysis of US Customs and Border Protection selectivity of commodity shipments
for targeted examination of containers. However, GIF technology is applicable
to far more than the current critical problems in information technology
modernization efforts at Treasury, State, Department of Defense and Department
of Homeland Security. GIFT provides a principled ground in which to extend
formal models of natural event structure those objects of investigation are by
nature complex. The gift has to be
accepted, however, and so far the revolutionary nature of the approach on which
these integrated technologies depends has been counter intuitive to mainstream
artificial intelligence and to the IT procurement process.
Over the past decade a revolutionary ground has been
prepared by scientists and technologists who felt that intelligence and military
activity required a new information science paradigm. We have faced an entrenched discipline and procurement
process. The individual involved in
maintaining this process have been, so far, unwilling to even accept the
possibility of a paradigm shift.
So natural scientists have developed the “Second
School of Semantic Science”. The Second
School points out that the First School treats intelligence as if it is a
merely a mechanism that can be decomposed into a set of fixed semantic states
and a first order logic defined on this set.
Natural science, and common sense, tells us that intelligence is not
proper characterized in this way.
The following have been our long term design
objectives for Treasury and DoD:
·
Improve the quality of
analysis, and utility of complex intelligence products;
·
Provide specific and tailored
intelligence to enhance our ability to visualize the battlespaces, including
the terrorism engagement space, and ensure total operational awareness;
·
Improve the throughput
and speed of delivery of National intelligence;
·
Reduce or eliminate
unnecessary redundancy and duplication in intelligence products;
·
Strengthen information
and production management and ensure policies, procedures, concept development,
training, and technical-human engineering;
·
Establish and integrate
standards (based on mandated Department of Defense (DoD) community
standards/architectures) for commonality, interoperability, and modernization
in coordination with appropriate elements and activities;
·
Explore and examine very
advanced technology and concepts for future integration;
·
Provide a thematic
analysis as the basis for information warfare, both defensive and offensive
activities.
Our proposed beta deployment demonstrates the
viability of a specific roadmap. The
roadmap starts where our industries are today and shows a specific path to the
design, development and deployment of next generation tools.
We have interoperability with W3C standards, but our
capabilities are forward looking.
Perhaps the most critical contribution is a data encoding mechanism that
supports the development of collective intelligence and work products that can
be re-used as models of complex phenomenon.
Our proof of concept involves the deployment of a
prototype that is fully operational and is to be used in critical context. This prototype can be deployed at any site
and requires only part of the deployment team have high levels of security
clearance.
Generality: Nothing in this roadmap excludes the
development of GIFT deployments in bio-chemical engineering, banking,
manufacturing, publishing or any other complex human activity. The technology is considered to be more advanced than any existing
e-commerce system; or any deployed knowledge management system. Several of our teaming corporations are
precisely those corporations who are regarded as having the leading edge
deployed systems. There participation
is made at or below costs simply because the methods and capabilities of these
systems are under-appreciated due to the break they make from classical
artificial intelligence and expert system based IT deployments.
Section 3:
Ontology architecture
We have available a
package of patented innovations in data encoding technology.
Figure 3: distributed Ontology
Management Architecture (d-OMA)
In several layers of our
existing software, data regularity in context is discovered using semantic
extraction techniques. The patterns
made from regularity are made explicit in the form of a set of concept
indicators. For us, R&D does not
mean “research and development”, because this term has been deemed or “no
value” in the IT industry or in government IT procurement circles. The political incorrectness of funding long
term R&D stems from the failure of research and development using the
classical approaches.
For us, “R&D” is
research and discovery. The research,
in this context, is an individual investigation of some complex natural
phenomenon, such as the purchasing interests of an on-line shopper. The GIFT provides human-centric investigator
tools. Like microscopes and carpentry
tools, the GIFT tools do nothing by themselves. These tools become useful when they are used by skillful domain
experts.
What are observed by
the tools are conceptual structures in social discourse. What is constructed is a model of how these
structures set within the various thematic expressions. The subject indicators have structural
relationships to individual natural language terms and patterns of term
occurrences.
Subject matter indicators are identified using several types of patented
semantic extraction processes. These
include two forms of patented conceptual aggregation from a letter, stem, word
or word phrase, n-gram measurement of text ;
as well as newly patented probabilistic Latent Semantic Analysis (PLSA) .
Ontology construction, no matter how they are developed, consists of
representations of concept schemas and their relationships. In GIFT, natural language terms, and
patterns of co-occurrence, provide ontology definition as sets of concepts,
with properties and attributes, organized with visual navigational aids.
A subsetting mechanism brings into a visual focus all and only that part of
extensive ontology repositories persisted in RDF repository and hash
tables. Human interfaces to shared
ontology repositories are designed to mimic the perceptual figure/group
relationship observed by natural science to be the key mechanism involved in
individual action-perception cycles.
These human interfaces allow local manipulation and editing of sets of
concepts found to be relevant to individual analysis of specific events, such
as US Customs and Border Patrol selecting a container for a search
procedure.
A local analysis by an individual occurs. Using the new software, this analysis occurs with
the greatest amount of flexibility. At
each of many sites, human analysts edit small details, modify underlying
assumptions, and otherwise examine how concepts identified locally might be
related to sets of concepts being maintained in global repositories. The concepts themselves are equipped with
metadata about how these concepts might be identified in text, and
co-occurrence relationships that concepts have with other concepts in the
repository.
A collective intelligence can be expected.
Subject matter experts enable a type of global analysis due to local
manipulation. Local manipulation occurs
based on direct experience, but this experience is conditioned by the recent
global analysis. Real time intelligence
response is therefore very likely.
Collective global analysis occurs because individual human interfaces to concept
repositories have selective attention mechanisms. BCNGroup scientists understand the physical and mental activity
involved in individual human action, cognition and perception cycles. This understanding is part of several
academic literatures; “cognitive engineering” and “evolutionary
psychology”. The focus of this science
is on the behavioral patterns of people and systems of living systems.
Collective global
analysis occurs within contexts that are implicitly (not necessarily
explicitly) structured by relationships that are established when many
individuals work with localized ontology. Individuals produce reports based on
the subsetting mechanisms that “retrieve” that part of the globally stored RDF
repository that is deemed relevant. As
local analysis produces reports, these reports themselves are subjected to
linguistic and ontological methods where reconciliation of terminological and
viewpoint differences become critical.
SchemaLogic’s SchemaServer product will be interfaced with web services that
manage the specifications of a global terminology library. Terminological reconciliation is a current
capability provided by SchemaLogic Inc to a variety of commercial clients.
Ontological science
tells us that local manipulation of concepts by an individual within the
contingencies of the moment involve human tacit knowledge and can rapidly lead
to a deep understanding of a specific event in the context of larger issues and
concerns. However, human understanding
is both highly situational and strongly shaped by opinion. Any specific
understanding depends on individual(s) defining terms so that these fit within
a coherent view of the events occurring.
In key situations, a single common viewpoint is not possible, nor is a
single viewpoint always desirable.
The control of managed
vocabulary is essential to uniform work on enterprise wide ontological
models. One key failure of the Tom
Berners Lee (W3C standards bodies)
is the absence standard methods for the reconciliation of terminological
differences. Our system has several
layers of methodology that are tied by first principles to the way our data is
encoded into computer memory. The data
encoding specifications are simple, non-proprietary, and available to review
and use.
Figure 4: The production of scoped
ontology with humans in the loop
A second knowledge
management system is a product from Acappella Software Inc. The regularity of responses to standard situations
can be studied, resulting in patterns of expression that are captured in
pre-existing textual snippets of expression.
This allows a patented process to assist in flexible report generation
having the 3Cs, clarity, completeness and consistency.
Both
knowledge management systems are tied together with standard knowledge
representational data encoding based on RDF (Resource Description Framework)
and Orbs (Ontological referential bases).
Ontology representations can be used within the difficult contexts of
uncertain information, shifts in context, and changes in the underlying
situation. In most cases, a human
analyst will easily alter interpretations and schema properties in real time to
accommodate these practical limitations.
The two commercial knowledge management systems
provide support for cultural transitions.
Section
4: The ontology encoding innovation
Scoped ontology
sits on an exceedingly simple data structure standard, developed and published
by OntologyStream Inc.
Bypasses to the well known XML persistence and search limitations are
found by using this encoding.
This data
structure is a topic taxonomy organized in a specific fashion, disclosed as a
matter of public information.
Differential ontology framework works in a specific fashion to create a
global information framework where managed vocabulary and ontology is generated
and used as a knowledge management capability.
Several small
deployments have been completed. For
one of the state governments, a consultant/specialist created 216 concept
representations and organized them into the upper two layers of a differential
ontology framework. A prototype for a
large deployment in US Customs was developed but not deployed (as of May 2005). We are seeking a contractual means to deploy
based on the team agreements between ten leading, but small, innovative
knowledge technology corporations.
Non-deployment of the prototype is deemed by our group to be one
manifestation of profound incompetence by specific Lockheed Martin
management. A GAO investigation was
initiated in May 2005.
Situationally
focused models of specific events were considered as targeting software to be
used in a future modernized US Customs and Border Protection. Work stopped on this deployment as of March
2005, due to contracting issues . However the concept of scoped ontology has
now been demonstrated in the state (DHS) deployment and in a commercial
deployment (not disclosed). These are
small deployments which act as a proof of product.
The upper layer
of the differential ontology framework is a set of universal abstractions, such
as abstractions about the flow of time. The middle layer contains domain
specific concepts and utilities such as security policies, concepts about how
containers are searched, or concepts about what is a commodity.
In our small
state DHS project, several specific systemic risks were identified, leading to
corrections in risk management policies.
Differential
ontology is deployed within a action oriented process model called AIPM, see
Section 7, Figure 8). Working from
event reports, semantic extraction activities are developed and data instances
are parsed to produce reporting triggers.
Triggers launch
processes that construct scoped ontology.
The development of small (5 -20 topics) situationally specific scoped
ontology is the usual outcome from automatic scoping processes. These ontology representations can be used
for rapid communication of structured information and for building histories. We need the larger deployment to show how
ontology streaming might aid in global analysis and responses.
In some
domains, for example Custom’s Harmonized Tariff Schedule, there may be hundreds
of thousands of concepts, but a small set of organizing principles that
generate categories over these topics.
The categories are suggested by algorithms, and then reified by human
analysts.
Event specific
categories developed as a means to visualize elements of event space
phenomenon. The event phenomenon is then “understood” using the concepts in
upper abstract and domain specific ontology.
Figure 5: The GIFT architecture as of
2002
The full GIFT
architecture is being realized using a server glue language called Python. The key is to bring the required products
together in a work environment.
Figure 5 (first
seen in 2002) expresses our long term interest in Visual Text (the Text
Analysis International Corporation Inc (TAI) ), semantic extraction and
schema logic (SchemaServer).
NLP++
is the language that TAI founder Amnon Meyers
developed and used to build a, now patented, “system for developing (rapidly)
text analysis systems”. Our scientists
know that each targeted domain of text data elements has different structure to
functions relationships. Textural semantics, or meaning, is critically
dependant on the specific domain of text data elements. The NLP++ language is
used to instrument the focused measurement of function to structure
relationships.
Probabilistic
latent semantic analysis (PLSA), patented in 2005 by Recommind
Inc, is used to develop n-ary
representations of subject matter indicators.
NdCore (Applied Technical Systems Inc), Readware (MITi Inc);
and SLIP analysis (OntologyStream Inc) is used to get different looks at the same
data. As the set of subject matter
indicators are developed, RDF encoded concept representations are developed and
the NLP++ based software is now used to instrument the detection of these
concepts in text. The “two sides” of
the differential ontology framework are established.
The
SchemaServer product from SchemaLogic provides the knowledge management
features required to management controlled vocabularies and thus to allow human
use of natural language to control the development of use of sets of concepts
(ontology).
Acappella
Software provides a product that helps to create clear, complete and concise,
the 3Cs; written reports in the first place.
The
development of our data layer has been in conjunction with our work on
extending some intellectual property for Applied Technical Systems, and is
discussed in a public document titled “Notational System for the Ontology
Referential Base (Orb)” .
Our
data layer is different, in that the ontology encoding innovation provides a
primary user interface and many of the key features related to real time use of
ontology for situational analysis. Orb
data encoding are agile and free of pre-imposed data schema. Our architecture separates the data layer
from the transaction and presentation layers.
The data layer has a simple, non-proprietary, encoding into computer
memory and a simple read and write to a word processing text file.
A
standard RDF repository persists the data structure as sets of
concept-representations. The Orb
repository provides real time data mining and very advanced methods related to
categorical abstraction and event chemistry rotationally defined transformation
operators . Interoperability between Orbs and RDF is
simple and unencumbered.
Using
the standard Orb encoding we define very fast parsing and convolution operator
to act directly on mapped memory. We
can show that this encoding supports almost instantaneous transformation,
search and retrieval.
Figure 6: The key-less hash table Orb
encoding
The hash table has
become a central tool in the development of very agile encoding of data in a
“structure free” form. Orb encoding is
very similar to a classical hash table, and yet requires no hash function and
no indexing. Data is located by
interpreting the ASCII string as if it were a base 64 number. Thus location is context. Orbs (Ontology referential bases) have a
slightly different standard form when compared with RDF. However, Orb structure can be placed into
the Intellidimension Inc RDF repository using a mechanism defined in the
notational paper. Intellidimension
provides one of the commercial off the shelf software systems that we use at
very low costs.
We
suggest that, in the near future, digital signal processing will perform the
convolution of Orb structures. Because
of a notational illustration of how the Orb convolutions map to standard
digital signal processing, we conjecture that scalability issues can be
resolved by simple engineering using electromagnetic spectrum.
A
quick mathematics like “analytic proof” is used to demonstrate that our
approach is not likely to run up against any scalability issue. A time delay currently does exist in
processing convolutions. However, we
are able to demonstrate what may be optimal processing times using an implementation
of the key-less hash table in the current SLIP browsers .
The
real deployment issues are in developing an understanding of how to use sets of
concepts encoded as Orb construction and having ambiguation/disambiguation and
terminological reconciliation issues worked out. Theses educational and cultural are the issues that we still hope
to work out with US Customs and Border Protection, as we continue to argue that
the approach outlined to them in January and February 2005 is both wise and
productive.
Section 5: Informational convolution
Informational convolution is defined to be a process that visits each element in
a hash table and performs some logic using a small rule engine and the contents
of the hashed element and associated bucket.
Our work is a generalization of patents, and partially owes its origin
to a contract that Applied Technical Systems gave to one of our group in
2002.
The mechanisms involved
in informational convolution have a deeper grounding into situational control
and topological logics .
Our work on convolution mechanisms has been connected, notationally, to the
internal parser and ontology within MITi’s Readware product and within Applied
Technical Systems’ NdCore product.
Several other patents also inspire the work.
Because of the modern
object oriented hash table, one is able to perform localization of information
in an efficient manner. As is
empirically verified, “categorical” localization of information may be derived
from very large data sources and produce very small subject indicators. This “categorical collapse” is due to
“ontological regularity” in textual expressions about similar topics.
These subject
indicators can then be applied to entirely different data sources. Commonalities are observed about how words
are used in everyday activities. The
natural “standard” is “common terminological usage” in relevant communities. If
one ignores, as most OWL standardization of semantic web applications do, then
one develops non-functional concept interoperability. Our team has fielded commercial knowledge management systems that
recognize the true nature of human use of natural language.
One can allow more than
one object to be placed into a variable length hash table bucket, giving the
system a response degeneracy that is needed to model the passage of events
through what is sometimes called a tipping point. The same bit structure allows metadata to be gathered and stored,
and used to help separate the contexts imposed quite naturally in everyday
activity. This use of metadata does not
occur without reminders that humans have cognitive capability that computer
programs simply to not, and likely will not ever have.
Figure 7: The semantic extraction
process
The key to the Ontology
Reference Base (Orb) is a simple mechanism that encodes informational
bits. The bits are required to have the
form (class, object) where the class gets its definition from what is a
stochastically defined neighborhood “around” all of the occurrences of the
object, and the object gets its definition from a specific occurrence of a
neighborhood.
The definition may be
best achieved using probability Latent Semantic Analysis patents by Recommind
Inc. However, several reasonable methods may be used to define and refine class
definition. Once class definitions
begin to reflect the organization of concepts about the organization then we
have made explicit a control structure over information processes about that
organization.
This control structure
is not expected to replace human judgment.
Our natural scientists point out that a fundamental error is made by
academic and consultant groups in acting as if human judgment is reducible to
algorithms. This error continues
unabated at government institutions like DARPA, NIST and NSF. Because funding continues to be poured into
this false paradigm, many small and large IT consulting companies develop and
market software based on the AI mythology.
The Roadmap starts by
debunking this mythology and demonstrating that human centric ontology use
creates high value to those markets that have been wasting time and money on a
failed paradigm.
Section 6: The minimal
deployment
The first step
develops a multi-user web based set of web servers for managing relevant
processes and the ontology persistence repository.
The second step involves the formalization of specific information
vetting processes in line with accepted cultural practices. This is to be accomplished using use cases,
in which the step-by-step enumeration of information processes is written
down. The enumerated steps from a model
of cognitive, communicative, and behavioral aspects related to analytic
practice.
The third step involves the development of a knowledge-use map,
indicating where in the enumerated processes one might deploy scoped ontology
formalism to capture the structure of typical information flow. The knowledge-use map is part of a
consulting methodology developed by our team. Once the use map is developed;
additional analysis can be extended regarding how knowledge is used.
The fourth step is to develop medium size enterprise ontology. Enterprise-scoped ontology is developed
using consulting methodology and results in a model of information flow within
part of the organization. The model
specifies information paths as well as the details that should be considered in
the perception of information through introspection.
The fifth step is to develop a number of component ontologies that
encode specific information structure.
Section 7: Regularity in
report generation
Any system within the
Global Information Framework depends critically on knowledgeable users. Humans provide situational awareness focused
by cognitive clues. These cognitive
clues are presented via a computer screen in the form of small graph structure
(subsets of community defined ontological models). The graphs show specific
concepts and relationships between concepts.
GIFT
architecture assumes an "intelligent design" to the way that things
in the real world work. Intelligent
design is reflected in the order that emerges from apparent chaos. When phenomenon is properly measured the
order structured by this intelligent design can be mapped to a pre-existing
ontological model. Perhaps this is the way that natural language works. Traditional “semantic extraction” technology
almost, but not quite, makes this kind of measurement from full text written by
humans.
Differential
ontology framework opens up the extraction process to human judgment. It does so by having an explicit set of
concepts well enumerated, by the community, and made available to the community
as a means to support human communication.
The notion of a framework is one that is used to organize these sets of
concepts, and to map semantic extraction algorithms to specific concepts. These frameworks provide a sufficient basis
for an enumeration of concepts organized to provide the community with formal
structure. The formal structure also
allows machine computations, and interoperability, that is understood within
the context of the communities’ needs.
Before
semantic technology can be properly deployed, the potential role of ontology
frameworks has to be understood. The
Roadmap addresses this need through our support for the development of
knowledge management certification programs for those who will be first
adopters.
Principles
derived from cognitive science and other scholarly literatures provide this
understanding. The understanding is
consistent with our everyday experience.
In
each of the semantic extraction tools, a framework measures text for patterns
indicating subject matter. This
measurement process is common to the methodology that we propose as the core
technology for all future semantic technology.
What is measured is a set of subject matter indicators. How the measurement occurs does vary
depending on which of the semantic extraction software systems we use. Readware, NdCore and latent semantic
analysis each achieves a framework function.
Human
eyes and cognition makes sense of these measurements and supply details that
are not available in the explicit, pre-existing, concept representation. The small graph constructions are visualized
(see Figure 1) and are modified by the user, resulting in both the use of
concept representations and the modification of the small graphs.
Ontological models are situational in nature and cannot be fixed in
advance. The regularity of structure in context is complex and situationally
exposed. Situationally scoped ontology, the named
graphs produced by differential ontology, has the role of representing
knowledge. These named graphs capture
human knowledge in a way that is similar to a written report.
In many cases, federal
law mandates clear, complete and consistent reports when a government agent
takes certain actions. An example of a
report covered by such mandate is an administrative ruling about the Harmonized
Tariff Schedule code that is assigned to each commodity imported into the United
States.
Administrative rulings, as one can see from our experimental site ,
are often written as precise reports using legal language. These reports are clear, complete and
consistent: the 3Cs.
The HTS administrative
rulings are well written and have the quality of judicial rulings, and often
carry the same effect as a ruling by a court.
On the other hand, many other reports generated by US Customs and Border
Protection Reports are not 3C complaint.
In the global
information framework, the productions of reports are enhanced in four ways.
1) Software is used to
enhance the productivity of agents by presenting a survey type software
interface leading the analyst quickly through a set of situationally dependant
questions.
2) Whether by automated
means or by standard relational database interfaces, reports generated by
analysis are parsed to produce semantic expansion of the concepts found by
semantic extraction processes run on an individual report in real time.
3) The concepts found to be
associated with a report are iteratively refined by allowing the analysts to
view situationally scoped ontology individuals. These “ontology individuals” (OIs) are developed computationally
from subsetting mechanisms that use upper abstract and middle domain ontologies.
4) Global predictive
analysis methodology is developed from the global organization of OIs into
event space models.
Figure 8: Modification of
intelligence community model
In 2002, one of the BCNGroup scientists was working on a
project supporting the Total Information Awareness program at DARPA. He was given a seven step actionable
intelligence process model. As is well
known, the perception and measurement problems are often left out in classical
information science. The inclusion of
two additional steps was deemed necessary to allow the process model to work as
a complete paradigm, see Figure 8.
Section
8: Predictive Analysis Methodology
Members
of our team have developed a document/data repository based on the Open Source
document repository system called Greenstone.
We have extended this system and are integrating it with components that
we recommended deploying together.
Once
assessment objects, like written reports and database elements, are in our
repository, various existing concept recognition and trending technologies are
used to produce a model of the evolution of various situations. In the prototype systems, we have looked at
cyber threats and cyber attack mechanisms, and thematic analysis over
administrative reports. Discussions
have occurred about how to generalize the modeling capabilities to use
ontological modeling as a control aid in human centered analysis of processes
occurring in complex environments, such as an ideological war or the events
occurring in an aquatic system.
Predictive analysis methodology has evolved from these preliminary
projects and from scholarly literatures.
Before
all else, predictive analysis methodology depends on the quality of the model
that is produced. So how does one
evolve quality ontological models?
Stephenson,
Prueitt and Einwechter used or data/document repository as well as cyber attack
ontology and cyber risk analysis called Formal Analysis of Risk in Enterprise
or FARES project. The FARES system
develops scoped ontology about possible cyber attacks, and about risks that are
defined by the interaction between explicitly developed ontology, distributed
data and specific data instances.
User
defined and community confirmed ontology and assessment elements from this
repository are retrieved as required by users.
Iterative processes refine theories based on specified ontology.
Figure 9: Definition of new ontology
in the Protégé editor
The
ontology is used in a fashion similar to how mathematics is used in modeling
physical phenomenon. In the FARES
project we have also defined several complex behavioural models using formalism
called colored Petri nets. The Petri
net is the formalism used in a number of commercial simulation packages. The principle concept is that states of the
“system” are modeled as nodes of a graph and transitions between states by a
movement of a token placed on the nodes of the graph.
Scoped
ontology elements expose an anticipatory process model over Risks and
Gains. This more general methodology
will use an (1998) extension by Prueitt and Kugler of the J. S. Mill’s
logic.
Information from
applied knowledge management systems can be reviewed by human introspection in
order to enrich scoped ontology structure and related assessment objects. Scoped ontology allows human users to work
with complex problems modeled as ontology and yet not be over whelmed by
extensive ontology structure.
The
scoped ontology serves two purposes.
First, ontology reminds users about specific details of a global model
for information flow within the enterprise.
The scoped ontology serves to focus a human’s awareness on part of a
larger representation about possibly relevant concepts. Second, the scoped ontology provides consistency
and uniformity for many intelligence products across a large distributed enterprise. One behavioral consequence of this
consistency is that the users tend to conform naturally to a global information
flow model, thus providing uniform alignment to policy. The model is agile and human communities are
to be comfortable with it.
Informational
transparency may also increase due to increased expectation that work products
have specific character. Informational transparence results as individual
humans work with the three types of resources,
·
Scoped ontology
delivered through a subsetting mechanism
·
Assessment elements
being used to develop theories about events and event structure
·
Explicit ontology
defined as OWL (lite) and encoded into either the Intellidimension Inc RDF
repository or an Orb repository element
The
use of ontology breaks predictive analysis methodology into small steps.
Styles
of scoped ontology use and construction will reinforce a sense of community and
allow the development of personalization and familiarization so essential to
real time community.
Figure 10: Process model
Workflow
can be instituted. For example, scoped
ontology components can be pulled from a repository in order to conduct
incremental steps, A, B, C, D. These
steps start with a Screening Assessment and end up with a Situational
Assessment.
The
development of new components by staff allows one domain of expertise to be a
prototype for multiple extensions of policy and knowledge into new areas. Once enterprise ontology are in place one
may extend scoped ontology usage as a reporting and communication medium. The enterprise ontology organizes a universe
of ideas into topics and questions. The
scoped ontology brings part, but not all, of this universe into a perceptual
focus, represented sometimes by a small graph.
One
data flow model for implementing scoped ontology technology might consist of
the following:
A)
An analyst, or team,
uses scoped ontology resources to develop structured assessments based on
question answering within small-specialized components. These assessments are
forwarded into an enterprise ontology subsetting system and routed using
workflow.
B)
New ontology elements
are created, or modified from framework prototypes.
C)
Component scoped
ontology are generated from enterprise ontology.
D)
A meta-object facility
is used to link information content to data elements
E)
Scoped ontology
assessments are archived for future reference.
The relationship
between scoped ontology and enterprise ontology can be complex and yet shaped
to the features of emergent situations.
For example, a scoped ontology may capture part of a larger process
conducting a review of a situation.
This part can become a view of a subordinate process and thus change
context.
The scoped ontology can
also serve to produce a specific order to questions, or components, that are
then viewed within the enterprise scoped ontology by a larger community.
Figure 11: Model of individual ontology use
A presentation of the
core technologies is necessary to obtain a first hand experience.
Advanced knowledge
management certification programs are designed and will be offered by KM
Institute and several universities (Phase 2).
Four aspects: One may model the
differential ontology framework as having the following aspects:
1) the creation of
the enterprise ontology and its resources
2) the use of
ontology by a specialist to conduct an assessment (for example, a screening
review)
3) the answering of
questions by a respondent
4) the generation of a report
Creation: Ontology provides an organization to thought about the
flow of information in a large enterprise.
Many, but not all, of the details have been worked out in advance. There is a natural and easy just-in-time
selection of topics that allow the specialist to “navigate” through a universe
of ideas. Ideas can be organized, as
they are in the real world, into worldviews and the concepts in different
worldviews kept separate using controlled vocabularies, mapping between
terminology use and subject matter indicators.
Use: Scoped ontology provides an overall structure to the conceptual representation
of informed mental universes. A navigation process causes an additional (and
separate) structuring of elements of ontology.
The structuring of navigation through topics is adaptive to how the
specialist has navigated the topics up to a certain point. As the navigation occurs, the specialist
will easily generate new scoped ontology.
The structure of navigational bias can also be imposed using portal
technologies and special situational logics, such as are being developed in a
number of labs, for inclusion in meta-object facilities.
Answering: Parts of the enterprise ontology can be viewed in
order to make reminders that information is needed. A respondent or algorithm supplies this information using one or
more question frameworks. The respondent
has choices defined within a knowledge management system where messages are
sent back and forth.
Report: A report can be generated based on the questions
answered, or based on a specific scoped ontology.
Section 9: A future anticipatory technology
The Roadmap delineates an approach
towards ontological modeling of complex natural systems, like the information
flow within the US Customs and Border Protection. Ten commercial software systems are to be integrated at the
patent level and then deployed based on a specific notational system .
This selection of technologies is based on a principled selection of components
that fit together and provide something more than merely the sum of the
parts.
Differential ontology framework creates
abilities at the individual level for a community of knowledgeable person to
interact with the knowledge of many others within their community in near real
time. A collective intelligence is made
possible. The notation acts as a super standardization constraint, serving the
same role as Hilbert mathematics serves for engineering and physical sciences.
One of the technologies that have been
layered on top of our proof of concept is called Anticipatory Technology. It is derived from a complex systems point
of view and from certain cybernetic schools developed in the former Soviet
Union. It has been grounded
in empirical sciences related to human perception, memory and anticipatory
responses. The technology is a
methodology supported by advanced knowledge management practices. We disclose the foundational elements of
this work as part of a knowledge management certification program and have
published the notational framework on which this certification program is
designed.
In everyday activity humans exhibit anticipation
about what will happens next. Humans,
and all living systems, do exhibit anticipatory behavior as part of our
survival mechanisms. Each of us
understands, from our direct experience, the natures of anticipation. Anticipation is not, for example,
certain. It is not a prediction of the
future. Anticipation merely gets us
ready for certain types of outcomes.
Semantic science suggests that what
happens next is not solely caused by what is happening now. This suggestion is in direct contradiction to
the practices of mathematicians and scientists form the “first school”. In the
first school, every physical process is a Markovian process . What happens next also depends on the nature
of causes related to the environment and to the resolution of internal
processes. The behavior of insurgency
in Iraq,
for example, is not deterministic but rather part of a complex expression where
causes are not always observable.
Anticipatory technology allows one to get ready for outcomes that cannot
be precisely defined before hand.
The anticipatory nature of human behavior
is only partially understood by science.
The foundations of classical science, e.g., Hilbert mathematics, seem
too strong and too precise to model the real phenomenon associated with human
intentions. Using our notational
system, formal specification of concepts in an ontological model can be
equipped with disambiguation and reconciliation metadata. This equipment is specified in our
notational papers and in the encoding architecture (see Figure 6, Section 4).
Natural science has developed empirical
evidence about processes involved in individual and collective anticipatory
behavior. Differential ontology
framework captures a descriptive ontology from which this behavior can be
placed into context. The anticipatory
outcome is then expressed as a scoped ontology. We have detailed the technical architecture in other parts of the
Roadmap.
We briefly review the principles that we
assume are involved in real anticipatory behavior. Human anticipation appears to have three categories of causal
entailments;
1)
Causes
consequent to patterns encoded into memory,
2)
Structural
entailment that can be predicted from engineering techniques, and
3)
Environmental
affordance
These three entailment (casual) categories
are mapped rather purely to the physical process involved in human memory, the
reaction dynamics involved in cognitive processing, and the physical sensing of
possible routes to behavioral responses.
Figure 12 illustrates an anticipatory
architecture that assists humans in the production of actionable
intelligence. This is part of Prueitt’s
original work on anticipatory technology.
The architecture develops two types of ontological components; the first
type is defined as a set of abstractions about the patterns that are observed
by computer algorithms. These patterns
are thought to be elements that when composed together produce subject matter
indicators. The patterns are, of course, originally expressed within the
grammar of language.
VisualText, one of our component
technologies, provides a development environment that assist in the observation
of grammar and co-occurrence and produces a classification of nouns, verbs and
other elements of grammar. Linguistic
knowledge can be capitalized on. But at
core the ontology components in our set of abstractions about the patterns are
not depending on grammar. The patterns
are represented as an small graph with word labels that evoke mental
experiences. These icons are linked
together so that if the user wants to see the co-occurrence patterns for
“disputes” then the “center” of the icon shifts.
In differential ontology framework, text
is harvested using instrumentation appropriate to understanding the structures
in text. The structure obtained from
this measurement is encoded into a set of ordered triples
{ < a,
r, b > }
where a and b are subjects
making reference to elements of, or aspects of, the measured “structural”
ground truth in raw data.
Figure 12: Model of naturally occurring anticipatory
mechanisms
The sets are encoded in computer memory
as Ontology referential base (Orbs) having fractal scalability and interesting
data mining features. Temporal
resolution is obtained using categorical abstractions encoded into Orb sets. These Orb sets are presented as a simple
graph construction. A human then views
the simple graph.
The graph acts as a cognitive
primer. We call this human-centric
information production or (HIP) because the computer processes are supportive
of a human becoming aware of how information fits together and as a consequence
“connects the dots” and understands something new.
The visual icons are minimalist, in that
only a minimal exposure to information is made. The primary “cognitive
processing is then made by an individual human. A key element is that the Orb encoding has to be simple and
straight forward so that the user can easily manipulate the information
(something that is not possible with OWL (W3C standard) ontology software like
Protégé.
Our algorithms, data encoding standards and computer interface
design methodology have been created to enhance natural anticipatory mechanisms
available to humans. The following
tasks can be achieved:
Task 1: Create a conceptual
indexer, signature, technology that does not need to preserve individual
identities. Like integers, the signatures are higher
level abstractions and is thus not merely about an individual.
Task 2: Create a polling
instrument using web harvest of web logs.
The results are posted to a
web site where real time modeling of social discourse can be viewed using the
Topic Map standard.
Task 3: Create agile real time
web harvesting processes that reveals the structure of social discourse within
targeted communities
Task 4: Do this in such a fashion
that the results are about the social expression between people and predict
tendencies towards actions or interests
Task 5: Develop a common
notation around the representational language so acquired
Technical/Scientific Considerations: A group of scholars have a common and shared
sense that anticipation technology must involve a theory of substructure, a
theory of how substructure forms into phenomenon, and a theory of environmental
affordances.
The Roadmap has been developed through a
more than decade long process of scientific review of the emerging IT industry,
algorithms in machine learning, neural networks, data mining, informatics and
related domains. The review is supported by a scientific advisory board. Our advisory board has also reviewed certain literature in natural
science about memory, awareness and anticipation. We use natural science to design an extension of certain academic
traditions related to Soviet era cybernetics and Western cognitive and quantum
neuroscience.
In our architecture, raw computer data is
parsed and sorted into categories.
Iterative parsing and categorization seeks the patterns of regularity
within various contexts. Patterns of categories
are visualized as very simple graph constructions. The parsing produces localized information that is then organized
using mathematically well defined convolution operators to produce visual
information about categories of co-occurrence.
Because of the regularities in patterns,
an entire inventory of patterns can be stored in extremely small data
structures. The structures are used to
inventory co-occurrence patterns and to make structured correspondence to
explicitly defined sets of concepts.
The concepts are developed by a community
and contain metadata about reconciliation issues and issue related to
ambiguation and disambiguation.
Reconciliation, ambiguation and disambiguation are then interpreted in a
notational system as element of response degeneracy necessary to any formalism
that models the complex phenomenon that we address.
With the correlation to design flaws and
the positive extensions based on category analysis and pattern detection.
Section 10: The Second
School of Semantic Science
The stratified model uses class:object pairs, simple
graphs, and a process for collapsing occurrences into categories to create
persistent data structure and relationships that reflect how physical and/or
informational components work together to express real world complex
behavior. Hilbert mathematics plays a
simpler role when one is dealing with engineering and certain categories of
physical phenomenon.
During the time in which Hilbert mathematics was being
developed and applied to physical science, professional scientists worked as a
single community in the development of this wonderful physical science we now
have. This physical science was not
extended into the life sciences.
Attempts for this extension have been exhaustive, but specific types of
failures occur in those formalisms that have the same type of underlying logic
as does Hilbert mathematics. These
types of failures can be aligned with formal analysis of mathematics itself,
using the principles of mathematics.
The stratified model is derived from a specific
scientific/mathematical/logic literature represented in its simplest form with
reference to graph theory, and specifically Peircean logics that conform to
what is called the “Unified Logical Vision” to cognitive graphs (John Sowa,
1984), to Ontology Web Language, and to Topic Maps.
The stratified model has two forms
(1)
conceptual and
notational and
(2)
implementation as
computer processes.
Implementations as computer processes are specified in great and exact
detail as a patent disclosure or in some cases as public domain
disclosures. The “ownership” of the
fundamentals to a functional science of complexity is raised due to the
historical context that we have experienced since the early 1980s. The BCNGroup, a not for profit organization
founded in 1993 in Virginia, has developed a Charter that uses patent law to
assist in the organization of patented computer processes, and to express new
intellectual property within a roadmap for the adoption of semantic technology . Over time, the objective of the BCNGroup is
to make the complete set of relevant computer processes available within an
economic model that is consistent with democratic principles and social needs.
An existing “experimental system”, reduces to practice a number of
specific concepts that are expressed in our notational system. So we say, following our cultural practice,
that certain of the concepts that are motivating the notational system are
potentially patentable when a specific set of rules are given that describes
exactly how one might build a computer program that runs on computer hardware
and produces behavior that corresponds to features discussed in the
notation. Our opinion is that these
patents should be publicly explained so that people know how to use the
techniques.
Conventions and notation are used to provide a common intellectual
environment for extending those implementations that have been already
completed or prototyped.
Many of the first generation of semantic extraction patents will expire
in the near future. Additional ones
will be developed to allow a defensive stance against new patents disallowing
our use of original patents. These will
be used within our systems to provide foundational capability, and to
demonstrate what human centric knowledge management can become.
Our purpose is to provide educational processes that teach the concepts
essential to these conventions and notational systems. The internal processes involved in
Human-centric Information Production (HIP) using Orbs can be readily understood
in the context of how we experience thoughts, and so the technical detail
follows our experiences.
The advisory committee
The advisory committee:
·
Dr Kent Myers
(Advisory Board)
·
Dr Ben
Geortzel (Advisory Board)
·
Dr Peter
Stephenson (Advisory Board)
·
Brianna
Anderson (Advisory Board)
·
Dr Peter
Kugler (Advisory Board)
·
Dr Alex
Citkin (Advisory Board)
·
Dr Art Murray
(Advisory Board)
·
Dr Paul
Prueitt (Advisory Board)
·
Dr Karl
Pribram (Advisory Board)
·
Dr John Sowa
(Advisory Board)
·
Rex Brooks
(Advisory Board)
·
Doug Weidner
(Advisory Board)
·
David
Bromberg (Advisory Board)
Companies
·
SchemaLogic
Inc (SchemaServer, knowledge management
technology)
·
Acappella
Software (Knowledge management technology)
·
Recommind
Inc (probabilistic Latent Semantic Analysis technology)
·
Applied
Technical Systems Inc (conceptual role up, semantic extraction)
·
Intellisophic
Inc (taxonomy)
·
Text Analysis
International Corporation Inc (text analysis tools)
·
MITi Inc
(Ontology based semantic extraction)
·
The Center
for Digital Forensic Studies (Risk analysis using formal methodology and
ontology)
·
OntologyStream
Inc (project management, technical architecture)
·
Intellidimension
Inc (two full time developers plus RDF repository servers)
Appendix A: Purpose
Global
Information Architecture, using
Composite
Semantic Architecture
Prototype
Draft Version 20.0 April 28, 2005
Purpose: Conceptual constructions aim to make visualizable and computable
·
The aggregation of event information into a knowledge
domain expressed as a set of concepts,
·
The fetching of information using conceptual
organization,
·
The focusing of human selective attention using
ontology subsetting mechanisms,
·
The extraction of subject matter indicators from human
text,
·
The elements minimally needed for objectively examining
risk and gain to the enterprise
The proposed architecture for
Ontology Mediation of Information Flow is called Differential Ontology
Framework (DOF). DOF has the following
elements:
1)
A semantic extraction
path that uses any of several COTS products to parse written human language
and produce an n-gram (or generalized n-gram) over word stems, letter co-occurrences,
or phrases; as well as rules defined over these n-grams.
2)
A concept
identification cycle that associates with the results of n-gram based
semantic extraction one or more explicitly defined concepts.
3)
An ontology development
path that uses human descriptive enumeration to produce a three layer
modular ontology – with the topmost layer highly abstract and common, the
middle layer with multiple domain and utility ontologies, and a lower layer
with small scoped ontology.
4)
Technical requirements
are imposed on these three layers so as to, in the presence of instance data,
produce a minimal subset of concepts from the middle layer that provides a
clear, complete and consistent (the 3Cs) understanding of data reported as an
instance and relating to an event.
5)
Global aggregation of
event structure so as to support global analysis of distributed and
temporally separated events.
Appendix B: Project Outline
·
Paul Prueitt and Amnon Myers will
receive six-month full time contracts.
·
Two software developers from Intellidimension will
receive one and ½ -month full time contracts.
·
Art Murray, Nathan
Einwechter, Alex Citkin, Peter Stephenson,
Peter Kugler and Ben Goertzel will receive six-month 1/6-time contracts.
·
Acappella Software, Intellisophic, Center for Digital
Forensic Studied, MITi, SchemaLogic, Recommind and Applied Technical Systems
will each supply one engineer each for one month. Evaluation copies of software will be provided at no cost.
Total
hours: 4,990
Total
time costs: $633,750
There
will be three workshops. The first
workshop will be held within 30 days.
·
11 participants, some who will attend by teleconference
·
Final approval of Plan of Action and Management task
list with milestone dates.
·
Final approval of subcontracts with tasks and
expenditures
·
Program reports
Operating
costs for workshops and travel is $30,000.
Total
Time and Materials cost $663,750.
Prime
contractor overhead is 15%, or $99,562.50.
Total contract
sought: $765,312.50.
OntologyStream
Inc will provide office space and computing resources. Prime contractor will provide
management.
Contributing COTS vendors will be
compensated for support and engineering time, and will provide software on a
free, or very low cost, evaluation basis.
Patents: Negotiations for full compensation over all selected IP will be
made based on fair use and just reward principles, as defined in common
law. Each of the participants will stand
to make considerable compensation for the long-term use of proprietary
property.
Follow
on: A large
follow-on project is anticipated.
.
Appendix C: Semantic Science
A "Second School
of Semantic Science"
approach to ontology mediation of human communication has been developed. This approach does NOT use description
logics, but rather a descriptive enumeration of sets of concepts and a
scripting language that uses ontology terminology as computable elements.
We take the position that some aspects of descriptive
logic are useful, and that certainly some applications of ontology
"reasoning" can be found using descriptive logics; but that the use
of ontology as formalism for complex system study requires other elements not
found in classical logic.
We take the position that controlled vocabulary, or the
term we use "managed vocabulary", can be mapped to explicit ontology
(defined if one wants as standard Ontology Web Language (OWL) with description
logic), via the notion of a subject matter indicator (SMI). This mapping can be
dynamic and based on human use and human control. Thus the precise, and we feel
inappropriate, standardization of terms' meaning is avoided. The mapping
between words and patterns of word occurrences is maintained in a weak form so
that the normal use of ambiguation can be reflected in how ontology is used in
computing.
This weak form preserves metadata about terminological
variation of meaning until the moment in which a human perceives a part of a
larger ontology structure within the experience of a moment. We make the principled position that it is
only in the present moment that human tacit knowledge is available to complete
the process of forming a non-ambiguous mental image of meaning in the context
of that moment.
The SMIs can be produced by any one of the many
semantic extraction processes (none of which use descriptive logics). In classical physics Hilbert mathematics
creates a description of all behavior of a "Newtonian" system. We hold that Newtonian science and Hilbert
mathematics is too precise to be used as a means to model complex
behavior. In the Second
School, the set of SMIs
take the place of mathematics in a description of reality. Various type of abstract mathematics acts on
this set to produce dynamic models of complex behaviors, including
Double articulation (linguistics or
gene/phenotype correlations)
Response degeneracy (Gerald Edelman's term)
Meta
stable state transitions within metabolic chains
Holonomic, non-holonomic
causation
(Karl Pribram’s term)
These complex behaviors may be expressing a
condition of local indeterminacy under global and underconstrained forces.
The
Knowledge Sharing Foundation
The knowledge
sharing foundation concept was first developed (2003) as a suggestion
supporting the US
intelligence agency needs to develop information about event structure. Previous to this, a small group of
scientists have talked about the need for a curriculum, K – 12 and college, to
support an advancement of cultural understanding of the complexity of natural
science. By natural sciences, we mean
social and cognitive science in the context of human communication.
The suggestion to
support new intelligence technology deployments is predicated on the
intelligence community’s responsible use and on the co-development of an open
public understanding of the technologies employed.
Ontologystream Inc
has developed an (fairly complete) understanding of the types of text
understanding technologies available within the intelligence community.
Q-1: What is needed to support awareness of
events in real time?
Q-2: What is needed to support community use of
analytic tools?
Q-3: What are the benefits to Industry
Q-4: What are the Foundation Elements
Q-5: Examples of Innovation
Q-6: Why are educational processes important?
Q-7: How does the software compensation model
work?
Q-8: How are test sets made available to the
competitive communities?
