Knowledge acquisition mechanisms for a logical knowledge base including hypotheses

1990 ◽  
Vol 3 (2) ◽  
pp. 77-86 ◽  
Author(s):  
Mitsuru Ishizuka ◽  
Tetsusi Matsuda
Keyword(s):  
Knowledge Acquisition ◽  
Knowledge Base

OPGEN: A System to Identify and Evaluate Potential Applications of Knowledge Based Systems

1992 ◽  
Author(s):  
Samir Rohatgi ◽  
James H. Oliver ◽  
Stuart S. Chen
Keyword(s):  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Knowledge Based Systems ◽  
The Core ◽  
Knowledge Based ◽  
Evaluation Functions ◽  
Potential Applications ◽  
Computer Based ◽  
Suitable System ◽  
Overall Effectiveness

Abstract This paper describes the development of OPGEN (Opportunity Generator), a computer based system to help identify areas where a knowledge based system (KBS) might be beneficial, and to evaluate whether a suitable system could be developed in that area. The core of the system is a knowledge base used to carry out the identification and evaluation functions. Ancillary functions serve to introduce and demonstrate KBS technology to enhance the overall effectiveness of the system. All aspects of the development, from knowledge acquisition through to testing are presented in this paper.

Predicting Lexical Answer Types in Open Domain QA

2012 ◽  
Vol 8 (3) ◽  
pp. 74-88 ◽  
Author(s):  
Alfio Massimiliano Gliozzo ◽  
Aditya Kalyanpur
Keyword(s):  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Large Scale ◽  
Question Answering ◽  
General Purpose ◽  
Open Domain ◽  
Lexical Knowledge ◽  
Research Challenge ◽  
Lexical Knowledge Base

Automatic open-domain Question Answering has been a long standing research challenge in the AI community. IBM Research undertook this challenge with the design of the DeepQA architecture and the implementation of Watson. This paper addresses a specific subtask of Deep QA, consisting of predicting the Lexical Answer Type (LAT) of a question. Our approach is completely unsupervised and is based on PRISMATIC, a large-scale lexical knowledge base automatically extracted from a Web corpus. Experiments on the Jeopardy! data shows that it is possible to correctly predict the LAT in a substantial number of questions. This approach can be used for general purpose knowledge acquisition tasks such as frame induction from text.

On a Novel Cognitive Knowledge Base (CKB) for Cognitive Robots and Machine Learning

2014 ◽  
Vol 6 (2) ◽  
pp. 41-62 ◽  
Author(s):  
Yingxu Wang
Keyword(s):  
Machine Learning ◽  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Cognitive Learning ◽  
Knowledge Bases ◽  
Formal Concept ◽  
Human Knowledge ◽  
Cognitive Knowledge ◽  
Cognitive Robots ◽  
Concept Network

A cognitive knowledge base (CKB) is a novel structure of intelligent knowledge base that represents and manipulates knowledge as a dynamic concept network mimicking human knowledge processing. The essence of CKB is the denotational mathematical model of formal concept that is dynamically associated to other concepts in a CKB beyond conventional rule-based or ontology-based knowledge bases. This paper presents a formal CKB and autonomous knowledge manipulation system based on recent advances in neuroinformatics, concept algebra, semantic algebra, and cognitive computing. An item knowledge in CKB is represented by a formal concept, while the entire knowledge base is embodied by a dynamic concept network. The CKB system is manipulated by algorithms of knowledge acquisition and retrieval on the basis of concept algebra. CKB serves as a kernel of cognitive learning engines for cognitive robots and machine learning systems. CKB plays a central role not only in explaining the mechanisms of human knowledge acquisition and learning, but also in the development of cognitive robots, cognitive learning engines, and knowledge-based systems.

Knowledge-acquisition tools with explicit problem-solving models

1993 ◽  
Vol 8 (1) ◽  
pp. 5-25 ◽  
Author(s):  
William Birmingham ◽  
Georg Klinker
Keyword(s):  
Expert System ◽  
Problem Solving ◽  
Expert Systems ◽  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Code Generation ◽  
System Development ◽  
Generalization Error ◽  
Domain Experts ◽  
The Past

AbstractIn the past decade, expert systems have been applied to a wide variety of application tasks. A central problem of expert system development and maintenance is the demand placed on knowledge engineers and domain experts. A commonly proposed solution is knowledge-acquisition tools. This paper reviews a class of knowledge-acquisition tools that presuppose the problem-solving method, as well as the structure of the knowledge base. These explicit problem-solving models are exploited by the tools during knowledge-acquisition, knowledge generalization, error checking and code generation.

Fundamental Tribology Knowledge Serve Modeling and Knowledge Resource Units Construction

2011 ◽  
Vol 135-136 ◽  
pp. 553-559 ◽  
Author(s):  
Wen Kui Xi ◽  
Xiao Yang Yuan ◽  
Qian Jia
Keyword(s):  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Industrial Structure ◽  
Significant Feature ◽  
Knowledge Service ◽  
Construction Of Knowledge ◽  
Automobile Engines ◽  
Service Oriented ◽  
Knowledge Intensive ◽  
Complex Mechanical Systems

Knowledge-intensive is a significant feature of the modern service-oriented industrial structure, and knowledge service is a main part of knowledge science. With research practices and accumulations in the field of tribological knowledge acquisition of turbine, automobile engines and other complex mechanical systems, construction of knowledge base and cooperation with enterprises in chain of “University-Industry-Research”, based on the understanding and experience of conceptual contents and features of tribology, knowledge service and resource unit and other concepts in the process of utilization, this paper studied the expression of modeling knowledge in the process of basic tribological knowledge service, construction of knowledge unit based on knowledge base and the driving force of knowledge acquisition and knowledge flow.

Process Fault Diagnosis Based on Process Model Knowledge: Part II—Case Study Experiments

1991 ◽  
Vol 113 (4) ◽  
pp. 627-633 ◽  
Author(s):  
R. Isermann ◽  
B. Freyermuth
Keyword(s):  
Fault Diagnosis ◽  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Process Models ◽  
Normal Operation ◽  
Problem Solution ◽  
Inference Mechanism ◽  
Process Knowledge ◽  
Fault Trees

A computer assisted fault diagnosis system (CAFD) is considered which allows the early detection and localization of process faults during normal operation or on request. It is based on an on-line engineering expert system and consists of an analytical problem solution, a process knowledge base, a knowledge acquisition component and an inference mechanism. The analytic problem solution uses a process parameter estimation, and the detection of process coefficient changes, which are symptoms of process faults. The process knowledge base is comprised of analytical knowledge in the form of process models and heuristic knowledge in the form of fault trees and fault statistics. In the phase of knowledge acquisition the process specific knowledge like theoretical process models, the normal behavior and fault trees is compiled. The inference mechanism performs the fault diagnosis, based on the observed symptoms, the fault trees, fault probabilities and the process history. This is described in Part I. In Part II, case study experiments with a d.c. motor, centrifugal pump, a heat exchanger and an industrial robot show practical results of the model based fault diagnosis.

PRODUCTION-GRAPH: A GRAPH THEORETICAL MODEL FOR CHECKING KNOWLEDGE BASE ANOMALIES

1992 ◽  
Vol 01 (04) ◽  
pp. 563-595
Author(s):  
ENG LIAN LIM ◽  
JOHN McCALLUM ◽  
KWOK HUNG CHAN
Keyword(s):  
Theoretical Model ◽  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Empirical Knowledge ◽  
Distinctive Features ◽  
Pathwise Approach ◽  
Problem Instances

Knowledge acquisition is tedious and error-prone. Consequently, a knowledge base may be inconsistent, and contains unreachable rules, redundant rules, and rules which may lead to deadends and infinite loops. There are three approaches for checking these anomalies: interactive, non-interactive pairwise and non-interactive pathwise. In this article, we will present a graph theoretical model called Production-graph for checking knowledge base anomalies along the non-interactive pathwise approach. Production-graph uses graph theoretical constructions to represent facts and rules, as well as relevant properties of the knowledge base that leads to anomalies. Distinctive features of Production-graph include: (i) Using Production-graph, we are able to check on groups of problem instances rather than on individual problem instances. This eliminates the problem of having infinitely many problem instances. (ii) Empirical knowledge is used to limit the problem instances to practically realizable problems. (iii) Effects of chaining both rules and facts are considered.

scholarly journals KM Trajectory Schema Service Frame Work for Software Development Organizations

2018 ◽  
Vol 7 (3.12) ◽  
pp. 616
Author(s):  
M A. Krishna Priya ◽  
Justus Selwyn
Keyword(s):  
Software Development ◽  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Knowledge Utilization ◽  
Development Activity ◽  
Km Strategy ◽  
Development Organizations ◽  
Frame Work ◽  
Collaborative Platform ◽  
Service Framework

Software Development Organizations are in the need to follow an effective KM strategy for Knowledge acquisition and Knowledge utilization. Even though, knowledge is available in the forms of documents, it is time consuming for the developers to identify and utilize the knowledge according to their need. Hence, we present a KM Trajectory Schema - Service Frame work which offers services for systematic Knowledge acquisition and Knowledge utilization.  Knowledge is acquired from the development activity, and then the developer’s code is stored in knowledge base. Knowledge Base Administrator designs a repository; knowledge base is appended by knowledge workers. Assessment and Evaluation of this Knowledge base is been done through the subject experts or knowledge champions. Finally Solution Knowledge Base is built and managed by Knowledge Base Administrators. KM service Framework facilitates Knowledge utilization by providing proactive semantic help for the software developers by the time of the development. KM Service Framework is an experimental collaborative platform that provides great service to the Software Development Organizations. 

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