Lakshman Jayaratne

Automatic text classification and focused crawling

A focused crawler is a web crawler that traverse the web to explore information that is related to a particular topic of interest only. On the other hand, generic web crawlers try to search the entire web, which is impossible due to the size and the complexity of WWW. In this paper we make a survey of some of the latest focused web crawling approaches discussing each with their experimental results. We categorize them as focused crawling based on content analysis, focused crawling based on link analysis and focused crawling based on both the content and link analysis. We also give an insight to the future research and draw the overall conclusions.

Semi-automatic extraction and modeling of ontologies using Wikipedia XML Corpus

This paper introduces WikiOnto: a system that assists in the extraction and modeling of topic ontologies in a semi-automatic manner using a preprocessed document corpus derived from Wikipedia. Based on the Wikipedia XML Corpus, we present a three-tiered framework for extracting topic ontologies in quick time and a modeling environment to refine these ontologies. Using Natural Language Processing (NLP) and other Machine Learning (ML) techniques along with a very rich document corpus, this system proposes a solution to a task that is generally considered extremely cumbersome. The initial results of the prototype suggest strong potential of the system to become highly successful in ontology extraction and modeling and also inspire further research on extracting ontologies from other semi-structured document corpora as well.

WikiOnto: A System for Semi-automatic Extraction and Modeling of Ontologies Using Wikipedia XML Corpus

This paper introduces WikiOnto: a system that assists in the extraction and modeling of topic ontologies in a semi-automatic manner using a preprocessed document corpus derived from Wikipedia. Based on the Wikipedia XML Corpus, we present a three-tiered framework for extracting topic ontologies in quick time and a modeling environment to refine these ontologies. Using Natural Language Processing (NLP) and other Machine Learning (ML) techniques along with a very rich document corpus, this system proposes a solution to a task that is generally considered extremely cumbersome. The initial results of the prototype suggest strong potential of the system to become highly successful in ontology extraction and modeling and also inspire further research on extracting ontologies from other semi-structured document corpora as well.