Edited
September 17, 2004
Anticipatory Web
May. 3, 04
From Paul Prueitt, to SICoP (Semantic Web Interest Community of Practice)
We begin this new bead game thread with an examination of the conceptual differences between the well known Semantic Web concepts and the concepts that are being developed as Human-centric Information Production (HIP) using a conceptual foundation for anticipatory webs of information.
This conceptual foundation takes the controversial view that stratification of physical processes, quantum, metabolic, organism, environment and social organization; is reflected in a separation of the processes leading to human memory recall from the processes involved in human anticipation of environmental conditions or reactions.
Anticipatory state transitions
The stratification accounts for the fact that living behavior is a consequence of emergence, and does not usually follow a precise plan or model. There is structure and behavioral patterns. But this structure and these behavioral patterns have tipping point where choices are made.
Believing that a plan accounts for all aspects of reality in real time is sometimes a plus and sometime a negative. A technology that identifies ground truth is useful when one is uncertain about the match between plans and pragmatic reality in real time. However, to always assume that the past truth, as encoded by knowledge engineers, is truth; is opposed.
Anticipatory technology follows the diagram shown above. The diagram can be used to talk about Readware Provenance ™ or human action-perception cycles where human memory is developed along with an independent selective attention mechanism that is focused on environmental causes.
As a consequence of some type of adaptive process, the organizational strata at the faster time scale is treated in a categorical fashion, resulting in what appears as a stable set of substructural atoms. Our “Conjecture on Stratification”, conjectures that human behavior and the nature of natural language are subject to formation as “process compartments”. The stability of these compartments depend on the regularity of atoms. The compartments form, have some period of stability and then dissolve.
The Semantic Web technologies, OWL and RDF, do not account for the formation of process compartments. These technologies assert that human knowledge can be well specified and that description logics can be used to mimic human reasoning.
However, real time dynamics matters in circumstances when the ground truth is complex, and complexity is always involved in the formative process. The real time dynamics may not reflect either the relevant ontological structure or the inferenced “facts”.
To bring a ground truth to the present moment one needs an objective investigation about three types of causes; substructural, mechanical and environmental. The Semantic Web depends on long deductive sequences and well-defined pre-established plans. If the world were fully engineer-able, then this approach would work perfectly.
Semantic Web technology does work well when the complete set of cause is mechanical and is fully understood prior to use in real time.
A series of Semantic Web meetings is continuing to occur within the government. These meetings are sponsored by government employees and attended by consulting groups and government employees interested in Semantic Web issues. SICoP informally hosts these meetings, with the next meeting on May 19th, 2004 [1].
Semantic Interoperability Community of Practice Meeting
Wednesday, May 19, 2004
The MITRE Corporation, Mc Lean, Virginia
Presentation
materials are provided priori to the meeting. In the announcement we have the
following statement.
The Semantic Interoperability Community of Practice (SICoP) is established by a group of individuals for the purpose of achieving "semantic interoperability" and "semantic data integration" focused on the government sector. The SICoP seeks to enable Semantic Interoperability, specifically the "operationalizing" of these technologies and approaches, through online conversation, meetings, tutorials, conferences, pilot projects, and other activities aimed at developing and disseminating best practices. The individuals making up this CoP represent a broad range of government organizations and the industry and academic partners that support them. However, the SICoP claims neither formal nor implied endorsements by the organizations represented. The SICoP is a Special Interest Group (SIG) within the Knowledge Management Working Group (KMWG) sponsored by the Best Practices Committee of the Chief Information Officers Council, (CIOC) in partnership with the XML Working Group, among others. Both the SICoP and its parent KMWG serve as interagency bodies to bring the benefits of the government's intellectual assets to all Federal organizations, customers, and partners. The SICoP through the Working Group will communicate its actions and findings to the Committee, the CIO Council and its member agencies, although its main purpose to support CoP members in their efforts to make the Semantic Web operational in their agencies.
This statement establishes the context of the SICoP
meeting.
One of the presentations at the May 19th
meeting will be by made by Lee Ellen Friedland, Center for Integrated
Intelligence Systems, The MITRE Corporation.
In her April 28th 2004
presentation she makes the following distinctions (slide 15/17).
Implicit versus explicit
Implicit Semantic Models can be well-developed and
widely-used
Informal versus formal versus formally represented
When informal Semantic Models are being used successfully
they, or their applications, will usually benefit from higher levels of
formalization
The need to formally represent a Semantic Model is
tied to the need or wish to use it in an IT context
Non-automated versus semi-automated versus optimally
automated
The degree to which a Semantic Model is automated in a technology application has no bearing on its fidelity to the concepts it represents
The full presentation should be reviewed to establish the context of this slide. Certain objectives are not fully represented in the Semantic Web standardization processes, as illustrated by Friedland presentations.
Graphical representations of human knowledge bead thread à
These objections center on the nature of formal
representations of knowledge and the limitations that, some claim that, these
formal representations of human knowledge have.
Comment on the above quoted distinctions regarding
Implicit, explicit semantic models
Informal, formal, formally represented semantic models
Non-automated, semi-automated, optimally automated semantic models
The previous slides in Friedland’s presentation create
the impression that social systems and individuals always have well defined
models of meaning.
Human behavior is often a formative phenomenon,
having real constraints and predispositions, but the notion of a model implies
a formal construction. The formative
process in nature involves the emergence of phase coherence in the brain. Events in the world also involve
emergence. Emergence is not “modeled”
by any known formal constructions.
Anticipatory mechanisms do have some modeling tools, but these tools
rely on human choice.
The problem that the engineered machine has with
ground truth is complexity. Humans deal
with complexity by making choices.
Anticipatory technology has focused on the measurement of structure by
engineered machines so that visual icons will orient human selective attention
and allow human’s to exercise choice in real time.
No complete formalization exists which captures the
formative process involved in the existence of mental states. The physical emergence of mental
events in the brain does not appear to be formalizable (see
the work of I. Prigogine “The end of certainty”, and Sir Roger Penrose’s
“Shadows of the Mind”.) This fact has
put stress on the very definition of mathematics.
Ground truth; however, requires that some new
architecture be created so that mathematics and data structures prime human
cognitive abilities. The Semantic Web
places this “meaning” in the information.
The Anticipatory Web harvests the structure of data encoded from
instrumented measurement and makes this structure available within a notational
system and computer data structure.