scholarly journals Compact Representation of Knowledge Bases in Inductive Logic Programming

2004 ◽  
Vol 57 (3) ◽  
pp. 305-333
Author(s):  
Jan Struyf ◽  
Jan Ramon ◽  
Maurice Bruynooghe ◽  
Sofie Verbaeten ◽  
Hendrik Blockeel
Keyword(s):  
Logic Programming ◽  
Inductive Logic Programming ◽  
Inductive Logic ◽  
Knowledge Bases ◽  
Compact Representation ◽  
Representation Of Knowledge

scholarly journals Guided Inductive Logic Programming: Cleaning Knowledge Bases with Iterative User Feedback

10.29007/ppgx ◽  
2020 ◽  
Author(s):  
Yan Wu ◽  
Jinchuan Chen ◽  
Plarent Haxhidauti ◽  
Vinu Ellampallil Venugopal ◽  
Martin Theobald
Keyword(s):  
Logic Programming ◽  
Inductive Logic Programming ◽  
Large Scale ◽  
Inductive Logic ◽  
Rule Learning ◽  
Knowledge Bases ◽  
User Feedback ◽  
Learning Approaches ◽  
Guided Inductive

Domain-oriented knowledge bases (KBs) such as DBpedia and YAGO are largely constructed by applying a set of predefined extraction rules to the semi-structured contents of Wikipedia articles. Although both of these large-scale KBs achieve very high average precision values (above 95% for YAGO3), subtle mistakes in a few of the underlying ex- traction rules may still impose a substantial amount of systematic extraction mistakes for specific relations. For example, by applying the same regular expressions to extract per- son names of both Asian and Western nationality, YAGO erroneously swaps most of the family and given names of Asian person entities. For traditional rule-learning approaches based on Inductive Logic Programming (ILP), it is very difficult to detect these systematic extraction mistakes, since they usually occur only in a relatively small subdomain of the relations’ arguments. In this paper, we thus propose a guided form of ILP, coined “GILP”, that iteratively asks for small amounts of user feedback over a given KB to learn a set of data-cleaning rules that (1) best match the feedback and (2) also generalize to a larger portion of facts in the KB. We propose both algorithms and respective metrics to automatically assess the quality of the learned rules with respect to the user feedback.

ILPNET repositories on WWW: Inductive Logic Programming systems, datasets and bibliography

1996 ◽  
Vol 9 (4) ◽  
pp. 157-206 ◽  
Author(s):  
Nada Lavrač ◽  
Irene Weber ◽  
Darko Zupanič ◽  
Dimitar Kazakov ◽  
Olga Štěpánková ◽  
...  
Keyword(s):  
Logic Programming ◽  
Inductive Logic Programming ◽  
Inductive Logic ◽  
Programming Systems

An Inductive Logic Programming-Based Approach for Ontology Population from the Web

2013 ◽  
pp. 319-326 ◽  
Author(s):  
Rinaldo Lima ◽  
Bernard Espinasse ◽  
Hilário Oliveira ◽  
Rafael Ferreira ◽  
Luciano Cabral ◽  
...  
Keyword(s):  
Logic Programming ◽  
Inductive Logic Programming ◽  
Inductive Logic ◽  
Ontology Population ◽  
The Web

Carcinogenesis Predictions Using Inductive Logic Programming

1997 ◽  
pp. 243-260 ◽  
Author(s):  
Ashwin Srinivasan ◽  
Ross D. King ◽  
Stephen H. Muggleton ◽  
Michael J. E. Sternberg
Keyword(s):  
Logic Programming ◽  
Inductive Logic Programming ◽  
Inductive Logic
Sign in / Sign up

Export Citation Format

Share Document