CEC Theses and Dissertations

Title

The Application of Inductive Logic Programming to Support Semantic Query Optimization

Date of Award

1999

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Graduate School of Computer and Information Sciences

Advisor

Junping Sun

Committee Member

S. Rollins Guild

Committee Member

Ping Tan

Abstract

Inductive logic programming (ILP) is a recently emerging subfield of machine learning that aims at overcoming the limitations of most attribute-value learning algorithms by adopting a more powerful language of first-order logic. Employing successful learning techniques of ILP to learn interesting characteristics among database relations is of particular interest to the knowledge discovery in databases research community.

However, most existing ILP systems are general-purpose learners and that means users have to know how to tune some factors of ILP learners to best suit their tasks at hand. One such factor with great impact on the efficiency of ILP learning is how to specify the language bias. The language bias is a restriction on the format (or syntax) of clauses allowed in the hypothesis space. If the language is too weak, the search space is very large, and hence, the learning efficiency is decreased. On the contrary, if the language is too strong, the search space is so small that many interesting rules may be excluded from consideration.

It is the purpose of this dissertation to develop an algorithm to generate a potentially useful language bias that is more appropriate for the task of inducing semantic constraints from the database relations. These constraints will be a major source of semantic knowledge for semantic query optimization in database query processing. The efficiency of the proposed algorithm was verified experimentally. The appropriate form of language bias specification, which is the output of the algorithm, was tested on the ILP system CLAUDIEN comparing with a number of different forms of language bias specification.

The learning results were compared on the basis of number of rules discovered, the quality of rules, total time spent to learn rules, and the size of the search space. The experimental results showed that the proposed algorithm is helpful for the induction of semantic rules.

This document is currently not available here.

  Link to NovaCat

Share

COinS