NDLI: Inductive learning in deductive databases

Content Provider	IEEE Xplore Digital Library
Author	Dzeroski, S. Lavrac, N.
Copyright Year	1989
Abstract	Most current applications of inductive learning in databases take place in the context of a single extensional relation. The authors place inductive learning in the context of a set of relations defined either extensionally or intentionally in the framework of deductive databases. LINUS, an inductive logic programming system that induces virtual relations from example positive and negative tuples and already defined relations in a deductive database, is presented. Based on the idea of transforming the problem of learning relations to attribute-value form, several attribute-value learning systems are incorporated. As the latter handle noisy data successfully, LINUS is able to learn relations from real-life noisy databases. The use of LINUS for learning virtual relations is illustrated, and a study of its performance on noisy data is presented.<>
Sponsorship	IEEE IEEE Comput. Soc. Tech. Committee on Data Eng IEEE Computer Society
Starting Page	939
Ending Page	949
Page Count	11
File Size	1237399
File Format	PDF
ISSN	10414347
Volume Number	5
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1993-12-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Deductive databases Logic programming Machine learning Learning systems Relational databases Encoding Transaction databases
Content Type	Text
Resource Type	Article
Subject	Information Systems Computational Theory and Mathematics Computer Science Applications

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

Inductive database relations

Systems for knowledge discovery in databases

First-order logic characterization of program properties

Foundations of secure deductive databases

Constraint-based query evaluation in deductive databases

An extended transformation approach to inductive logic programming

Chain-split evaluation in deductive databases

Data-driven discovery of quantitative rules in relational databases

Transactions and updates in deductive databases

Inductive learning in deductive databases

Similar Documents

Inductive database relations

Systems for knowledge discovery in databases

First-order logic characterization of program properties

Foundations of secure deductive databases

Constraint-based query evaluation in deductive databases

An extended transformation approach to inductive logic programming

Chain-split evaluation in deductive databases

Data-driven discovery of quantitative rules in relational databases

Transactions and updates in deductive databases

Inductive learning in deductive databases