scholarly journals Knowledge base refinement using apprenticeship learning techniques

2005 ◽  
pp. 247-257 ◽  
Author(s):  
David C. Wilkins
Keyword(s):  
Knowledge Base ◽  
Apprenticeship Learning ◽  
Knowledge Base Refinement ◽  
Learning Techniques

Building the knowledge base of a buyer agent using reinforcement learning techniques

2010 ◽  
Author(s):  
George Boulougaris ◽  
Kostas Kolomvatsos ◽  
Stathes Hadjiefthymiades
Keyword(s):  
Reinforcement Learning ◽  
Knowledge Base ◽  
Learning Techniques

scholarly journals Use of decision-tree induction for process optimization and knowledge refinement of an industrial process

1994 ◽  
Vol 8 (1) ◽  
pp. 63-75 ◽  
Author(s):  
A. Famili
Keyword(s):  
Decision Tree ◽  
Knowledge Base ◽  
Process Optimization ◽  
Electrochemical Machining ◽  
Industrial Process ◽  
Intelligent Manufacturing ◽  
Domain Model ◽  
Machine Learning Techniques ◽  
Decision Tree Induction ◽  
Learning Techniques

AbstractDevelopment of expert systems involves knowledge acquisition that can be supported by applying machine learning techniques. The basic idea of using decision-tree induction in process optimization and development of the domain model of electrochemical machining (ECM) is presented. How decision-tree induction is used to build and refine the knowledge base of the process is also discussed.The idea of developing an intelligent supervisory system with a learning component [Intelligent MAnufacturing FOreman (IMAFO)] that is already implemented is briefly introduced. The results of applying IMAFO for analyzing data from the ECM process are presented. How the domain model of the process (electrochemical machining) is built from the initial known information, and how the results of decision-tree induction can be used to optimize the model of the process and further refine the knowledge base are shown. Two examples are given to demonstrate how new rules (to be included in the knowledge base of an expert system) are generated from the rules induced by IMAFO. The procedure to refine these types of rules is also explained.

Cooperation in Knowledge Base Refinement

1992 ◽  
pp. 445-450 ◽  
Author(s):  
Gheorghe D. Tecuci
Keyword(s):  
Knowledge Base ◽  
Knowledge Base Refinement

scholarly journals Refining Automatically Extracted Knowledge Bases Using Crowdsourcing

2017 ◽  
Vol 2017 ◽  
pp. 1-17
Author(s):  
Chunhua Li ◽  
Pengpeng Zhao ◽  
Victor S. Sheng ◽  
Xuefeng Xian ◽  
Jian Wu ◽  
...  
Keyword(s):  
Knowledge Base ◽  
State Of The Art ◽  
Knowledge Bases ◽  
Important Research ◽  
Semantic Constraints ◽  
Knowledge Base Refinement ◽  
Automatic Methods ◽  
Automated Algorithms ◽  
Research Challenge

Machine-constructed knowledge bases often contain noisy and inaccurate facts. There exists significant work in developing automated algorithms for knowledge base refinement. Automated approaches improve the quality of knowledge bases but are far from perfect. In this paper, we leverage crowdsourcing to improve the quality of automatically extracted knowledge bases. As human labelling is costly, an important research challenge is how we can use limited human resources to maximize the quality improvement for a knowledge base. To address this problem, we first introduce a concept of semantic constraints that can be used to detect potential errors and do inference among candidate facts. Then, based on semantic constraints, we propose rank-based and graph-based algorithms for crowdsourced knowledge refining, which judiciously select the most beneficial candidate facts to conduct crowdsourcing and prune unnecessary questions. Our experiments show that our method improves the quality of knowledge bases significantly and outperforms state-of-the-art automatic methods under a reasonable crowdsourcing cost.

Knowledge base refinement by backpropagation

1991 ◽  
Vol 7 (1) ◽  
pp. 35-46 ◽  
Author(s):  
LiMin Fu
Keyword(s):  
Knowledge Base ◽  
Knowledge Base Refinement

Ontology-Based Optimization in Search Engine

2013 ◽  
pp. 285-301
Author(s):  
Leonardo Balduzzi ◽  
Ignacio Cuesta
Keyword(s):  
Knowledge Representation ◽  
Knowledge Base ◽  
Search Engine ◽  
Search Engines ◽  
Inductive Learning ◽  
System Evolution ◽  
Knowledge Based System ◽  
Learning Capacity ◽  
Knowledge Based ◽  
Learning Techniques

The major aim of the chapter is to propose and study the use of ontology-based optimization for positioning websites in search engines. In this sense, using heterogeneous inductive learning techniques and ontology for knowledge representation, a knowledge-based system which is capable of supporting the activity of SEO (Search Engine Optimization) has been designed and implemented. From its knowledge base, the system suggests the most appropriate optimization tasks for positioning a pair (keyword, website) on the first page of search engines and infers the positioning results to be obtained. The system evolution and learning capacity allows optimizing the productivity and effectiveness of the SEO process.

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