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Title: NOUS: A Knowledge Graph Management System

Abstract

Knowledge graphs represent information as entities and relationships between them. For tasks such as natural language question answering or automated analysis of text, a knowledge graph provides valuable context to establish the specific type of entities being discussed. It allow us to derive better context about newly arriving information and leads to intelligent reasoning capabilities. We address two primary needs: A) Automated construction of knowledge graphs is a technically challenging, expensive process; and B) The ability to synthesize new information by monitoring newly emerging knowledge is a transformational capability that does not exist in state of the art systems.

Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
Contributing Org.:
Battelle Memorial Institute, Pacific Northwest Division (PNNL)
OSTI Identifier:
1366431
Report Number(s):
NOUS; 005341MLTPL00
Battelle IPID 30821-E
DOE Contract Number:
AC05-76RL01830
Resource Type:
Software
Software Revision:
00
Software Package Number:
005341
Software CPU:
MLTPL
Open Source:
Yes
MIT License.
Source Code Available:
Yes
Other Software Info:
Open Source available under MIT license.
Related Software:
The software is developed on top of the open source Apache Spark framework for large-scale, in-memory computing.
Country of Publication:
United States

Citation Formats

. NOUS: A Knowledge Graph Management System. Computer software. https://www.osti.gov//servlets/purl/1366431. Vers. 00. USDOE. 26 Jun. 2017. Web.
. (2017, June 26). NOUS: A Knowledge Graph Management System (Version 00) [Computer software]. https://www.osti.gov//servlets/purl/1366431.
. NOUS: A Knowledge Graph Management System. Computer software. Version 00. June 26, 2017. https://www.osti.gov//servlets/purl/1366431.
@misc{osti_1366431,
title = {NOUS: A Knowledge Graph Management System, Version 00},
author = {},
abstractNote = {Knowledge graphs represent information as entities and relationships between them. For tasks such as natural language question answering or automated analysis of text, a knowledge graph provides valuable context to establish the specific type of entities being discussed. It allow us to derive better context about newly arriving information and leads to intelligent reasoning capabilities. We address two primary needs: A) Automated construction of knowledge graphs is a technically challenging, expensive process; and B) The ability to synthesize new information by monitoring newly emerging knowledge is a transformational capability that does not exist in state of the art systems.},
url = {https://www.osti.gov//servlets/purl/1366431},
doi = {},
year = {Mon Jun 26 00:00:00 EDT 2017},
month = {Mon Jun 26 00:00:00 EDT 2017},
note =
}

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  • Cited by 2
  • This report presents the results of the Knowledge Base Certification activity of the expert systems verification and validation (V&V) guideline development project which is jointly funded by the US Nuclear Regulatory Commission and the Electric Power Research Institute. The ultimate objective is the formulation of guidelines for the V&V of expert systems for use in nuclear power applications. This activity is concerned with the development and testing of various methods for assuring the quality of knowledge bases. The testing procedure used was that of behavioral experiment, the first known such evaluation of any type of V&V activity. The value ofmore » such experimentation is its capability to provide empirical evidence for -- or against -- the effectiveness of plausible methods in helping people find problems in knowledge bases. The three-day experiment included 20 participants from three nuclear utilities, the Nuclear Regulatory Commission`s Technical training Center, the University of Maryland, EG&G Idaho, and SAIC. The study used two real nuclear expert systems: a boiling water reactor emergency operating procedures tracking system and a pressurized water reactor safety assessment systems. Ten participants were assigned to each of the expert systems. All participants were trained in and then used a sequence of four different V&V methods selected as being the best and most appropriate for study on the basis of prior evaluation activities. These methods either involved the analysis and tracing of requirements to elements in the knowledge base (requirements grouping and requirements tracing) or else involved direct inspection of the knowledge base for various kinds of errors. Half of the subjects within each system group used the best manual variant of the V&V methods (the control group), while the other half were supported by the results of applying real or simulated automated tools to the knowledge bases (the experimental group).« less
  • Objective is the formulation of guidelines for the V&V of expert systems for use in nuclear power applications. This activity is concerned with the development and testing of various methods for assuring the quality of knowledge bases. The testing procedure used was that of behavioral experiment, the first known such evaluation of any of V&V activity; the value lies in the capability to provide empirical evidence for or against the effectiveness of plausible methods in helping people find problems in knowledge bases. The three-day experiment included 20 participants from three nuclear utilities, the Nuclear Regulatory Commission`s Technical training Center, Universitymore » of Maryland, EG&G Idaho, and SAIC. The study used two real nuclear expert systems: a boiling water reactor emergency operating procedures tracking system and a pressurized water reactor safety assessment systems. Ten participants were assigned to each of the expert systems. All participants were trained in and then used a sequence of four different V&V methods selected as being the best and most appropriate. These methods either involved the analysis and tracing of requirements to elements in the knowledge base or direct inspection of the knowledge base for various kinds of errors. Half of the subjects within each system group used the best annual variant of the V&V methods (the control group), while the other half were supported by the results of applying real or simulated automated tools to the knowledge bases (the experimental group). The four groups of participants were similar in nuclear engineering and software experience characteristics. It is concluded that the use of tools in static knowledge base certification results in significant improvement in detecting all types of defects, avoiding false alarms, and completing the effort in less time. The simulated knowledge-checking tool, based on supplemental engineering information about the systems, was the most effective.« less
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  • Deactivation and decommissioning (D and D) work is a high risk and technically challenging enterprise within the U.S. Department of Energy complex. During the past three decades, the DOE's Office of Environmental Management has been in charge of carrying out one of the largest environmental restoration efforts in the world: the cleanup of the Manhattan Project legacy. In today's corporate world, worker experiences and knowledge that have developed over time represent a valuable corporate asset. The ever-dynamic workplace, coupled with an aging workforce, presents corporations with the ongoing challenge of preserving work-related experiences and knowledge for cross-generational knowledge transfer tomore » the future workforce [5]. To prevent the D and D knowledge base and expertise from being lost over time, the DOE and the Applied Research Center at Florida International University (FIU) have developed the web-based Knowledge Management Information Tool (KM-IT) to capture and maintain this valuable information in a universally available and easily accessible and usable system. The D and D KM-IT was developed in collaboration with DOE Headquarters (HQ), the Energy Facility Contractors Group (EFCOG), and the ALARA [as low as reasonably achievable] Centers at Savannah River Sites to preserve the D and D information generated and collected by the D and D community. This is an open secured system that can be accessed from https://www.dndkm.org over the web and through mobile devices at https://m.dndkm.org. This knowledge system serves as a centralized repository and provides a common interface for D and D-related activities. It also improves efficiency by reducing the need to rediscover knowledge and promotes the reuse of existing knowledge. It is a community-driven system that facilitates the gathering, analyzing, storing, and sharing of knowledge and information within the D and D community. It assists the DOE D and D community in identifying potential solutions to their problem areas by using the vast resources and knowledge base available throughout the global D and D community. The D and D KM-IT offers a mechanism to the global D and D community for searching relevant D and D information and is focused on providing a single point of access into the collective knowledge base of the D and D community within and outside of the DOE. Collecting information from subject matter specialists, it builds a knowledge repository for future reference archiving Lessons Learned, Best Practices, ALARA reports, and other relevant documents and maintains a secured collaboration platform for the global D and D community to share knowledge. With the dynamic nature and evolution of the D and D knowledge base due to multiple factors such as changes in the workforce, new technologies and methodologies, economics, and regulations, the D and D KM-IT is being developed in a phased and modular fashion. (authors)« less

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