skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Knowledge Encapsulation Framework for Collaborative Social Modeling

Abstract

This paper describes the Knowledge Encapsulation Framework (KEF), a suite of tools to enable knowledge inputs (relevant, domain-specific facts) to modeling and simulation projects, as well as other domains that require effective collaborative workspaces for knowledge-based task. This framework can be used to capture evidence (e.g., trusted material such as journal articles and government reports), discover new evidence (covering both trusted and social media), enable discussions surrounding domain-specific topics and provide automatically generated semantic annotations for improved corpus investigation. The current KEF implementation is presented within a wiki environment, providing a simple but powerful collaborative space for team members to review, annotate, discuss and align evidence with their modeling frameworks. The novelty in this approach lies in the combination of automatically tagged and user-vetted resources, which increases user trust in the environment, leading to ease of adoption for the collaborative environment.

Authors:
; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
978528
Report Number(s):
PNNL-SA-62938
TRN: US201010%%7
DOE Contract Number:
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: AAAI Spring Symposium: Technosocial Predictive Analytics, SS-09-09:12-19
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; MATHEMATICAL MODELS; SOCIOLOGY; KNOWLEDGE BASE; KNOWLEDGE MANAGEMENT; COMPUTER CODES; semantic wiki, semantic web, automated discovery, knowledge elicitation, knowledge inputs

Citation Formats

Cowell, Andrew J., Gregory, Michelle L., Marshall, Eric J., and McGrath, Liam R.. Knowledge Encapsulation Framework for Collaborative Social Modeling. United States: N. p., 2009. Web.
Cowell, Andrew J., Gregory, Michelle L., Marshall, Eric J., & McGrath, Liam R.. Knowledge Encapsulation Framework for Collaborative Social Modeling. United States.
Cowell, Andrew J., Gregory, Michelle L., Marshall, Eric J., and McGrath, Liam R.. 2009. "Knowledge Encapsulation Framework for Collaborative Social Modeling". United States. doi:.
@article{osti_978528,
title = {Knowledge Encapsulation Framework for Collaborative Social Modeling},
author = {Cowell, Andrew J. and Gregory, Michelle L. and Marshall, Eric J. and McGrath, Liam R.},
abstractNote = {This paper describes the Knowledge Encapsulation Framework (KEF), a suite of tools to enable knowledge inputs (relevant, domain-specific facts) to modeling and simulation projects, as well as other domains that require effective collaborative workspaces for knowledge-based task. This framework can be used to capture evidence (e.g., trusted material such as journal articles and government reports), discover new evidence (covering both trusted and social media), enable discussions surrounding domain-specific topics and provide automatically generated semantic annotations for improved corpus investigation. The current KEF implementation is presented within a wiki environment, providing a simple but powerful collaborative space for team members to review, annotate, discuss and align evidence with their modeling frameworks. The novelty in this approach lies in the combination of automatically tagged and user-vetted resources, which increases user trust in the environment, leading to ease of adoption for the collaborative environment.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2009,
month = 3
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share:
  • Modern scientific enterprises are inherently knowledge-intensive. In general, scientific studies in domains such as groundwater, climate, and other environmental modeling as well as fundamental research in chemistry, physics, and biology require the acquisition and manipulation of large amounts of experimental and field data in order to create inputs for large-scale computational simulations. The results of these simulations must then be analyzed, leading to refinements of inputs and models and further simulations. In this paper we describe our efforts in creating a knowledge management platform to support collaborative, wide-scale studies in the area of geologic sequestration. The platform, known as GS3more » (Geologic Sequestration Software Suite), exploits and integrates off-the-shelf software components including semantic wikis, content management systems and open source middleware to create the core architecture. We then extend the wiki environment to support the capture of provenance, the ability to incorporate various analysis tools, and the ability to launch simulations on supercomputers. The paper describes the key components of GS3 and demonstrates its use through illustrative examples. We conclude by assessing the suitability of our approach for geologic sequestration modeling and generalization to other scientific problem domains« less
  • The Framework for Addressing Cooperative Extended Transactions (FACET) is a flexible, object-oriented architecture for implementing models of dynamic behavior of multiple individuals, or agents, in a simulation. These agents can be human (individuals or organizations) or animal and may exhibit any type of organized social behavior that can be logically articulated. FACET was developed by Argonne National Laboratory's (ANL) Decision and Information Sciences Division (DIS) out of the need to integrate societal processes into natural system simulations. The FACET architecture includes generic software components that provide the agents with various mechanisms for interaction, such as step sequencing and logic, resourcemore » management, conflict resolution, and preemptive event handling. FACET components provide a rich environment within which patterns of behavior can be captured in a highly expressive manner. Interactions among agents in FACET are represented by Course of Action (COA) object-based models. Each COA contains a directed graph of individual actions, which represents any known pattern of social behavior. The agents' behavior in a FACET COA, in turn, influences the natural landscape objects in a simulation (i.e., vegetation, soil, and habitat) by updating their states. The modular design of the FACET architecture provides the flexibility to create multiple and varied simulation scenarios by changing social behavior patterns, without disrupting the natural process models. This paper describes the FACET architecture and presents several examples of FACET models that have been developed to assess the effects of anthropogenic influences on the dynamics of the natural environment.« less
  • No abstract prepared.