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:
In keeping with NSF's strategic plan, the knowledge networking initiative proposes three research strategies.
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 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:
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.
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.
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.
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.
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.
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.
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:
Complex operations on representations, such as:
New representational techniques and media such as:
New uses for representations
Cognition research investigates interlinked processes of
perception, reasoning, memory, learning, and action by participants
in physical and socio-cultural situations.
The critical multidisciplinary aspects of Cognition research result
from the need to uncover common foundations for integrated understanding
of all phases of cognition, as carried out by a wide variety of
cognitive entities (e.g., people, artificial agents, groups, organizations),
cognizing (perceiving, reasoning/learning about, acting with)
domain-specific phenomena of differing character, scale and complexity
(e.g. perception of surface textures; organizational memory),
in a wide variety of physical and social contexts (e.g. a laboratory,
a crisis management scenario) under a range of specific constraints
(e.g. complexity, realizability, or real-time constraints).
Another multidisciplinary driver is the need to understand how
to apply the fruits of Cognition research effectively in many
different domains.
New interdisciplinary Knowledge Networking research under the
Representation thread includes:
New cognizing entities:
New objects of cognition such as:
New cognitive issues and methods:
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:
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: