from http://www.cisl.ucar.edu/info/FORMS/KNP1-6.html
The Knowledge Networking (KN) initiative focuses on the
integration of knowledge from different sources and domains
across space and time. Modern computing and communications
systems provide the infrastructure to send bits anywhere, anytime
in mass quantities-radical connectivity. But connectivity alone
cannot assure 1 useful communication across disciplines,
languages, cultures; 2 appropriate processing and integration of
knowledge from different sources, domains, and non-text media; 3
efficacious activity and arrangements for teams, organizations,
classrooms, or communities, working together over distance and
time; or 4 deepening understanding of the ethical, legal, and
social implications of new developments in connectivity.
In short, *we have connectivity, but not interactivity and
integration* . KN research aims to move beyond connectivity to
achieve new levels of interactivity, increasing the semantic
bandwidth, knowledge bandwidth, activity bandwidth, and cultural
bandwidth among people, organizations, and communities.
To "know" about something is a much stronger claim than to learn
about it or to gather information on it. "Knowledge" implies
consensual verification, as well as the ability to predict and
shape outcomes. Advances in computing and communications now hold
the promise of fundamentally accelerating the creation and
distribution of information. However, the construction of
knowledge requires more than collecting and transmitting large
amounts of data. *Building knowledge requires the scientific
community coming to grips with new forms of gathering data, new
tools to manipulate and store information new ways of
transforming that information, and new ways of working together
over distance and time*. The challenge for NSF is to facilitate
the evolution from today's emphasis on information and
distributed data to emerging systems for knowledge and
distributed intelligence. The payoff for the scientific community
is that interdisciplinary communities that can be joined in
sharing data, accumulating information and building knowledge
together will treat complex problems, traditionally addressed
within disciplinary boundaries. This shift from simple
information access to knowledge networking holds great promise
for to transforming society and science.
NSF represents large science, engineering, and education
communities that understand and can contribute to building these
knowledge networks. Technological advances, spurred by the NSF,
now enable scientific practitioners, who may be widely dispersed,
to become a science network, sharing and integrating data,
analyzing information and synthesizing knowledge. NSF wants to
expand and scale up these activities in the sciences and
engineering, enabling society to apply similar strategies
throughout its information infrastructures.
The NSF Knowledge Networking initiative creates a program of
closely interconnected activities to facilitate advances enabled
by simultaneous revolutions in technology, content, and
epistemology. The intellectual insights and the new process of
science enabled by knowledge networking are central to NSF's
mission.
One challenge is to support activities that will create new ways
of collecting, transforming, representing, sharing, and using
information. The support must be applied effectively across a
wide range of activities to enable the solution of the Knowledge
Networking challenge and to provide to scientists, engineers, and
society useful and easily implemented solutions to complex
problems.
A complementary challenge is to comprehend the human dimensions
associated with knowledge networking communities.
Multidisciplinary knowledge networking efforts will fail unless
we understand and provide for the learning environments that
enable skill sets, conceptual models, and values to be rapidly
shared across disparate fields.
Designing tools for gathering and analyzing data. New types of
tools are required to collect, share, and manipulate increasingly
complex data sets and structures. Utilizing these tools involves
innovations in computing, advances in telecommunications, and the
development of more sophisticated algorithms and
hardware/software systems.
Building the next generation of representations. Data,
information and knowledge require increasingly complex
representations. New kinds of media are required to enable the
communications of new types of messages and meanings. For
example, transforming symbolic information into sensory form
(e.g., visualization) necessitates translating scientific and
mathematical notations into tangible modalities.
Extending the human infrastructure that underlies knowledge
networking. Generating new ideas increasingly involves
participants in knowledge networks communicating with one another
in real time and obtaining data from disaggregated sources.
Expanding the knowledge networking community to new participants
requires:
Mastering a common language and a generally accepted set of
theories and conceptual models (to provide a framework for
communication) Inculcating communally defined processes of
collecting and analyzing data (to enable sharing and validating
information) Developing proficiency in design, reasoning, and
argumentation (to facilitate the evolution of ideas) Accepting a
common set of values that include respect for others'
perspectives and for intellectual property (to encourage wide
participation) Strategies
In keeping with NSF's strategic plan, the knowledge networking
initiative proposes three research strategies.
Maximizing cross-disciplinary research to make use of different
needs and demands of distinct research communities; Addressing
the common problems shared by different research communities;
Leveraging existing research activities and building upon them,
rather than starting from scratch. NSF also envisions several
strategies to launch and manage knowledge networking: Creating
ongoing working groups to integrate disciplinary issues;
Integrate community input through workshops; Promote
interdisciplinary research; Involve all of the Directorates at
NSF. NSF as a Catalyst
NSF can serve as a catalyst for creating knowledge networks. Part
of this role involves supporting the development of enabling
technologies (infrastructure), such as new algorithms and
software systems; data structures; metadata; standards for
interoperability, communications links, and computational
platforms. These enhancements extend from innovative processes
that bring researchers together in distributed collaboratories,
to tools for analyzing and interpreting data in new ways, to
sophisticated learning environments that help participants
discover and integrate new knowledge.
The other portion of this catalytic role involves conceptualizing
knowledge networking as collective action among scientific
communities ranging across many fields and disciplines. By
sharing disparate data and diverse perspectives, a community
develops a common, evolving understanding of a complex topic. As
the community's conception of the issues expands and deepens, its
membership grows to include participants with new perspectives
and backgrounds. Given its long experience with how this process
of acculturation and distributed intelligence occurs in the
scientific enterprise, NSF is positioned to aid in the
development of the technological infrastructures, collaborative
activities, and human communities needed for knowledge networking
across in society as a whole.
The Knowledge Networking Initiative Integrates Layers of
Achievement
The Knowledge Networking Initiative aims to create the underlying
science and the tools, infrastructure, and distributed
intellectual processes to achieve the layered aims shown in
Figure 1.
The overarching goal is improving our understanding of and
ability to manage larger and more complex natural, social, and
material phenomena. Knowledge networking can enhance the
operations of many human enterprises, with science and education
the most obviously relevant to NSF's mission. The crucial added
benefits that knowledge networking brings to the scientific
enterprise are the abilities to:
Couple models, knowledge, data, instruments, and intellectual
activity across space, time, and disciplinary boundaries, Work
with new types of content and knowledge bases of radically
increased scope and scale, Enhance the overall cognitive ecology
of science and engineering. Achieving these aims of coupling,
scope, and intellectual community depends critically upon new
levels of functionality in information infrastructures. We need a
better understanding of how to push or pull relevant information
wherever, whenever and to whomever it is useful; how to create
true semantic interoperability in heterogeneous knowledge
environments; and how to make knowledge maximally accessible with
new modalities of interaction such as real-time multimedia,
visualization, and simulation.
Achieving such new functionality and making them widespread and
universally accessible also requires re-conceptualizing the human
processes involved in creating and disseminating knowledge. The
groups involved include data gathering enterprises such as field
research teams, observatories, and cyclotron facilities;
information transmission functions such as messaging, publishing,
and library systems; and integrating/stabilizing infrastructures
such as standards and user groups. Each type of human interaction
in the overall scientific process must alter if knowledge
networking is to reach its full potential. Figure 2 and Figure 3
capture more of the dimensions of Knowledge Networks, and
communicate their dynamic nature. Examples of Social Objectives
and Relevance Outcomes that could be enabled by the Knowledge
Nets Initiative.
The following examples are potential outcomes of Knowledge
Networks. They illustrate the use of science and technologies to
meet larger societal goals. These examples involve science and
the use of scientific information that are possible only with the
use of Knowledge Networks. In addition the examples require
advancements in one or more of the subsystems (such as social use
of the new knowledge, science modeling, datamelding, and
real-time networks) from each of the top three layers of the
Framework discussed above.
Coping with Natural Disasters
In 1995, twelve forest fire fighters died tragically when they
were trapped on the side of a mountain in Colorado, unaware that
sudden changes in meteorological conditions had caused a change
in the path of the fire. Although some data were available
indicating a shift in the fire, this information could not be
delivered to the scene of the fire in a timely and clearly
understood manner. The enterprise, infrastructure and tools which
constitute the framework of the Knowledge Nets (KN) initiative
will enable an integrated framework which does not exist today
for dealing with natural disasters, ultimately leading to
minimizing loss. Specifically, the KN initiative could support
the development of coupled fire and atmospheric models. These
models require as input detailed knowledge of topography, ground
cover and synoptic weather conditions. These data exist in
various data bases spread over the country and are expressed in
different formats. The result from a simulation must be overlaid
with the detailed knowledge of the location of human and physical
resources. In cases where fire is near more populated areas, as
in the Oakland, California fires, additional information about
the demographics and civil infrastructure must be incorporated.
Even if this synthesis of rapidly changing information could be
assembled today, delivery to strategic locations in an
understandable form would still be necessary to ensure benefit.
The infrastructure and tools components on the KN initiative are
"glue" that will enable the effective management of natural
disaster situations.
Aviation Safety:
Delivery of current information to the cockpit and proper pilot
training are essential elements in improving air safety and
reducing operating costs. Significant progress has been made in
pilot training and alerting pilots to potential life threatening
situations. Examples of improved safety and reduced operating
costs that could result from the research supported by KN are: 1)
At many airports in the US, information on low-level wind shear
coupled with Doppler radar allows air traffic controllers to
alert pilots to unusual meteorological conditions. Improvements
to the current capabilities could save additional lives and money
for the airline industry. The current information that is
assembled by air traffic controllers is of limited predictive
value and must be reduced to a few numbers to allow the pilot to
comprehend the information in the cockpit during takeoffs and
landings. Synthesizing the results of models of the atmosphere
and air traffic into the cockpit and control towers would allow
the pilot and controller to better prepare for approaches or
takeoffs through in-flight simulation of conditions. In addition
to offering improved safety, this information will save
significant fuel costs because planes would not have to be routed
to different approaches at the last minute due to changed
conditions on the ground. 2) The FAA is considering the
feasibility of free flight by commercial airlines. This concept
would allow aircraft to take the most direct route between cities
rather than following established routes that pass predetermined
checkpoints. Essential for free-flight are current information on
weather conditions, location of other aircraft and conditions at
airports along the route. Gathering this information and
synthesizing and delivering it in a useful form is beyond our
current capability. The airline industry estimates the annual
savings, which may be recognized by implementing free flight, is
tens of millions of dollars. 3) In-flight icing conditions are
difficult to detect and even more difficult to predict. Several
recent airline disasters have been attributed to icing.
Improvements in the detection, prediction and delivery systems
available to the airline industry are necessary to overcome this
silent threat. The enterprise, infrastructure and tools that will
be developed as part of the KN initiative will accelerate the
ongoing research into in-flight icing.
Monitoring and Restoring Landscape Change: the Florida
Everglades
Large scale human impacts on landscapes have complex biological,
social and economic consequences. The dramatic impact human
activities have had on ecosystem function in the Everglades has
elicited an enormous amount of research, land and water
management and conservation activities. The health and recovery
of the Everglades and adjoining areas is now being considered by
stakeholders in many sectors: several federal agencies (DOI is
dispersing $200 million for restoration), scores of state
agencies and local jurisdictions, hundreds of research
activities, academic centers from public and private
universities, the sugar industry, two tribal nations, along with
conservation and public grass-roots organizations. The dynamics
of the interactions between all of these parties creates
knowledge chaos conditions which leads to duplication of effort,
gridlock, turf conflicts, organizational and political
uncertainty, needless competition and distrust between
stakeholders.
Information inputs for rational Everglades planning and recovery
come from highly-distributed and diverse sources such as from
long-term biological surveys, hydrochemical monitoring, watershed
flow models, land use change analyses, remote sensing, economic
analyses and human demographic studies, among inputs from other
research, sociological and economic areas.
The accumulation, representation and communication of such rich
and heterogeneous of data sets that span: decades of time,
numerous research disciplines and diverse stakeholders in
multiple sectors of the economy, creates enormous challenges and
equally enormous potential payoffs for effective knowledge
networking. Research and infrastructure development on data
integration, data mining, geo-spatial visualization, human
interactions, network communications, data sharing, the
coordination of long term monitoring, all within the context of a
well-defined, nationally important, environmental effort would
have immediate value to society and represents an immediate
payoff test-bed for new knowledge networking approaches. Examples
of How Knowledge Networking can Facilitate Research and
Education
NetCDF: A Tool That Facilities Collaboration
Though a typewritten table of numbers once sufficed to
characterize most quantitative studies, scientific efforts now
often yield quantities of data that can be structured,
interpreted, and utilized for further study only by computer.
Thus, methods for inter-computer data exchange represent critical
infrastructure for scientific collaboration. Though there are
common means for transferring human-readable material and for
selecting and retrieving information from data-base management
systems, there is little agreement on methods to convey some of
the most common data structures, such as vectors and
multidimensional arrays, used in certain disciplines.
To help address this problem, Unidata developed the Network
Common Data Form (NetCDF). The method is not for end-user;
rather, it is a programmer's tool kit for storing and retrieving
data in files that are portable (i.e. transferable between
dissimilar computers) and self describing (i.e. that contain
enough information to obviate the need for ancillary documents on
dimensionally, variable names, units of measure, etc.). The
NetCDF development represents, on a very limited scale, a
harbinger of impact KN will have on science, viz. the creation of
enabling technologies that will result in the generation of
fundamental new knowledge.
The NetCDF's existence and free availability appears to have had
a positive effect on collaboration as well as on the development
of scientific software. Hundreds of commercial and non-commercial
organizations all around the world and representing a wide
variety of disciplines have adopted NetCDF for scientific
analysis or visualization.
Mathematicians are attempting to build computer systems that
effectively represent mathematical knowledge, and that enable the
construction of databases of mathematical results and
mechanically checked proofs in forms that are readable and usable
by people. Users of such systems could shift among different but
clear and unambiguous representational syntaxes that capture the
same underlying mathematical knowledge in forms that are tailored
for use in different contexts. Users of such systems could know
that every mathematical result represented has a proof that has
been checked by computer and is available for inspection.
Mathematicians who have discovered new results may wish to add
them to the collection, helping to publicize these results,
certify their validity, and increase the overall capability. Such
tools may also help to systematize mathematical knowledge and to
integrate theoretical knowledge with computation. Such systems
could be a clear example of the power of Knowledge Networking for
dealing with organized knowledge rather than isolated facts, and
the broad utility of mathematical knowledge makes it a compelling
candidate. Wide availability of such capability and knowledge
through integration with the Internet could lead to new
generations of researchers, teachers, and students using such
tools routinely in any context where mathematics is used.
Research Opportunities in Knowledge Networking:
Knowledge Networking presents a number of research challenges and
opportunities. These can be organized under a set of topics or
threads, which we have termed *Interactivity, Representation,
Cognition, Agents, Corpora*.
Interactivity research studies the creation and maintenance of
dynamic, content-rich relationships among people, instruments
& tools, data, and artificial agents, using multiple
modalities. Technologies that enable such interactivity encompass
input/output devices, communication networks, and their interface
characteristics, adapted with the aim of making the best match to
what is known about the needs and requirements of individual
people, groups, teams, and organizations for effective
interaction. The critical multidisciplinary aspects of
Interactivity research result from the need to uncover common
foundations for understanding widely differing types of
participants (e.g. people or agents with particular skills;
specialized instruments) coupled through unique domain-specific
activities (e.g. doing geoscience or doing disaster relief)
integrating problem- and domain-specific information (e.g.,
specialized datasets or knowledge bases), via a variety of media
and channels (text, video, etc.), under a range of specific
constraints (e.g. quality-of-service; sensory limitation such as
no vision or hearing, etc.). Another multidisciplinary driver is
the need to understand how to apply the fruits of Interactivity
research effectively in many different domains. New
interdisciplinary Knowledge Networking research under the
Interactivity thread includes:
Research on representation studies the processes through which
participants (people, groups, agents, etc.) model and encode
knowledge about entities, processes, or phenomena in particular
representational media, and, conversely, reconstruct meanings and
semantics for representations in their contexts of use.
The critical multidisciplinary aspects of Representation research
result from the need to uncover common foundations for
understanding how widely differing types of participants (e.g.
people or agents with particular domain- or culture-specific
viewpoints; specialized data-gathering instruments), represent
problem- and domain-specific entities or processes (e.g., protein
molecules; organizational workflows), of differing
representational level (e.g., sensory; cognitive), scale and
complexity, for use in unique domain-specific activities (e.g.
doing bioscience or doing collaborative design) via a variety of
representational media and modalities (text, software, graphical
data, simulations, in visual, audio, haptic modalities, etc.),
under a range of specific constraints (e.g. size limitations,
specificity constraints). Another multidisciplinary driver is the
need to understand how to apply the fruits of Representation
research effectively in many different domains. New
interdisciplinary Knowledge Networking research under the
Representation thread includes: Representation of new entities or
attributes, such as:
Groups, teams, organizations, institutions Specific domain
entities Tasks of high complexity New cognitive issues and
methods: Distributed cognition Dynamic adaptation Error
processing and propagation; Exploiting parallel architectures for
computation; Focus of attention High-level reasoning;
Knowledge-based information processing; Learning effects of human
exposure to virtual and real environments. Non-conceptual
cognition; Perceptual, motor, and sensory-motor models;
Perception-based problem solving Realizability of cognitive
models Situated cognition Symbolic and geometric processing;
Transfer of learning; skill training and acquisition Cognitive
aspects of trust and believability
Agents research studies the active and sometimes physically
embodied algorithms, software, communications, and tools that can
assist people in Knowledge Networking activities. Examples of
agents include knowledge agents that seek and manipulate specific
data or information collections ("Knowbots") from interconnected
commuter networks, and cooperative physical agents such as
robots, intelligent devices, special instruments, and other
non-human natural agents or environments. The critical
multidisciplinary aspects of Agents research result from the need
to uncover common foundations for understanding how to support
and augment a variety of people, teams, groups, and
organizations, each with particular domain- or culture-specific
needs, in performing unique domain-specific activities (e.g.
doing bioscience, doing collaborative design, or emergency
management), using a varied array of resources (scientific data
sets, distributed simulations, specialized instruments), under a
range of specific constraints (e.g. time, methodological, or
performance quality constraints). Another multidisciplinary
driver is the need to understand how to apply the fruits of
Agents research effectively in many different domains. New
interdisciplinary Knowledge Networking research under the Agents
thread includes: Autonomy Building (and dismantling)
information-rich virtual communities and organizations
Domain-specific contextual knowledge and commonsense knowledge
for agents Coordination of activities and knowledge in
heterogeneous systems and environments Degrees and types of
augmentation and support for participants such as people, teams,
or organizations. Designs and criteria for sensory-motor systems
Distributed control Domain-specific contextual knowledge and
commonsense knowledge for agents Dynamic adaptation and evolution
of agents Engineering methodologies Interoperability of agents
Incentive structures Knowledge at new scales: large collections
of tiny, heterogeneous distributed agents; distributed knowledge
networks for control of MEMS Load and complexity management
Mathematical algorithms, machine architectures, and networking
technologies for knowledge agents in information spaces
Multi-agent systems Modularity, parallelism and complexity
Pathologies and immune systems in large-scale human-computing
aggregates, e.g. malicious agents, viruses, junk, "knowledge
storms" Possible or optimal domain, range and scope of the
agents' functionality Principles of decomposition and
organization of tasks and resources (division of labor)
Robustness, fault-tolerance, and reliability Specific
domain-dependent agents for assisting in information analysis,
decision making, and remote control of instruments and access to
information resources Trust, confidence, and believability User,
team, and organizational requirements and their evolution.
Investigations of corpora (plural of "corpus") research the
entire lifecycle (creation, structuring, storage, maintenance,
use and disposal) of general and community-specific collections
of data, information, and knowledge, ranging across ad hoc data
collections, complex scientific databases, large and distributed
digital libraries and even such unconventional entities as
digital forms of artifacts in museums. Research in Corpora is a
critical enabler of Knowledge Networking: people's ability to
access, retrieve and comprehend information from complex
databases and sources depends on how that information is created,
structured, stored, presented, and managed. New interdisciplinary
Knowledge Networking research under the Corpora thread has two
objectives: To accelerate cross-disciplinary database research,
and to develop new kinds of cross-disciplinary data-sharing
mechanisms, infrastructures, and relationships that can
facilitate new interdisciplinary experimental research. Relevant
research topics include: