Date of Award
2012
Publication Type
Master Thesis
Degree Name
M.Sc.
Department
Computer Science
Keywords
Communication and the arts, Applied sciences, Nondeterministic finite automata, Regular expression, Web mining, Deterministic finite automata, Dom tree, Frequent pattern
Supervisor
Christie I. Ezeife
Rights
info:eu-repo/semantics/openAccess
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Abstract
Existing web content extracting systems use unsupervised, supervised, and semi-supervised approaches. The WebOMiner system is an automatic web content data extraction system which models a specific Business to Customer (B2C) web site such as "bestbuy.com" using object oriented database schema. WebOMiner system extracts different web page content types like product, list, text using non deterministic finite automaton (NFA) generated manually. This thesis extends the automatic web content data extraction techniques proposed in the WebOMiner system to handle multiple web sites and generate integrated data warehouse automatically. We develop the WebOMiner-2 which generates NFA of specific domain classes from regular expressions extracted from web page DOM trees' frequent patterns. Our algorithm can also handle NFA epsilon([varepsilon]) transition and convert it to deterministic finite automata (DFA) to identify different content tuples from list of tuples. Experimental results show that our system is highly effective and performs the content extraction task with 100% precision and 98.35% recall value.
Recommended Citation
Harun-Or-Rashid, Mohammad, "Mining Multiple Web Sources Using Non-Deterministic Finite State Automata " (2012). Electronic Theses and Dissertations. 4814.
https://scholar.uwindsor.ca/etd/4814