NDLI: Rule graph: Incorporate expert and statistical knowledge for rule execution

Content Provider	IEEE Xplore Digital Library
Author	Tie Liu Chunhua Tian Feng Li Hao Zhang
Copyright Year	2009
Description	Author affiliation: IBM China Research Laboratory, Beijing, China, 100193 (Tie Liu; Chunhua Tian; Feng Li; Hao Zhang)
Abstract	We present an efficient graph based rule execution method which incorporate expert knowledge and statistical knowledge. How to use both the knowledge from expert experiences and the statistical information from business instances to improve the efficiency of rule execution is meaningful. We define a directed acyclic graph to control rule execution where each potential sequential rule execution corresponds one path in the graph. Expert knowledge is defined as the constraints on the executions of rules, and it can be used to prune the rule graph to reduce the potential paths. Statistical knowledge comes from the execution of a large number of business instances, and it can be used to assign different weights for paths and then adjust the structure of rule graph. Experiments indicate our approach can achieve a very efficient performance for rule executions which outperforms the existing approaches.
Starting Page	573
Ending Page	578
File Size	3602265
Page Count	6
File Format	PDF
ISBN	9781424435401
DOI	10.1109/SOLI.2009.5203999
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-07-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Silver Production systems Costs expert knowledge Laboratories Rule graph statistical knowledge Marketing and sales Expert systems rule execution Engines
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Learning the priority for rule execution

Dependency analysis for knowledge validation in rule-based expert systems

Parallel Electro-Optical Rule-Based System for Fast Execution of Expert Systems

Knowledge Base Design for Fault Diagnosis Expert System Based on Production Rule

Knowledge acquisition from corresponding domain knowledge transformations

Efficient Design of Interpretation of REL License Using Expert Systems

Multi-Location Transshipment Problem with Capacitated Production and Lost Sales

A satisficing knowledge-based system

Obscuring the human costs of expert systems

Rule graph: Incorporate expert and statistical knowledge for rule execution

Similar Documents

Learning the priority for rule execution

Dependency analysis for knowledge validation in rule-based expert systems

Parallel Electro-Optical Rule-Based System for Fast Execution of Expert Systems

Knowledge Base Design for Fault Diagnosis Expert System Based on Production Rule

Knowledge acquisition from corresponding domain knowledge transformations

Efficient Design of Interpretation of REL License Using Expert Systems

Multi-Location Transshipment Problem with Capacitated Production and Lost Sales

A satisficing knowledge-based system

Obscuring the human costs of expert systems

Rule graph: Incorporate expert and statistical knowledge for rule execution