A DOM-Tree based Representation of Web Document Structure for Web Mining Applications

Among the three broad areas of Web mining, Web Structure Mining is the method of discovering structure information from either the web hyperlink structure or the web page structure. In order to apply data mining techniques on web pages, a good and efficient representation of web pages is required that could depict the actual hierarchical structure of web pages. The work presented here aims to find out a representation of web documents that could be used as input for different data mining techniques. The present research work further aims at applying this representation for efficient clustering of web documents where clustering will be performed based on not only the web page content but also the structural layout of a web page

Text representation using canonical data model

Developing digital technology and the World Wide Web has led to the increase of digital documents that are used for various purposes such as publishing, in turn, appears to be connected to raise the awareness for the requirement of effective techniques that can help during the search and retrieval of text. Text representation plays a crucial role in representing text in a meaningful way. The clarity of representation depends tightly on the selection of the text representation methods. Traditional methods of text representation model documents such as term-frequency invers document frequency (TF-IDF) ignores the relationship and meanings of words in documents. As a result the sparsity and semantic problem that is predominant in textual document are not resolved. In this research, the problem of sparsity and semantic is reduced by proposing Canonical Data Model (CDM) for text representation. CDM is constructed through an accumulation of syntactic and semantic analysis. A number of 20 news group dataset were used in this research to test CDM validity for text representation. The text documents goes through a number of pre-processing process and syntactic parsing in order to identify the sentence structure. Text documents goes through a number of preprocessing steps and syntactic parsing in order to identify the sentence structure and then TF-IDF method is used to represent the text through CDM. The findings proved that CDM was efficient to represent text, based on the model validation through language experts‟ review and the percentage of the similarity measurement methods

Hybrid XML Data Model Architecture for Efficient Document Management

XML has been known as a document standard in representation and exchange of data on the Internet, and is also used as a standard language for the search and reuse of scattered documents on the Internet. The issues related to XML are how to model data on effective and efficient management of semi-structured data and how to actually store the modeled data when implementing a XML contents management system. Previous researches on XML have limitations in (1) reproduction of XML documents from the stored data, (2) retrieval of XML sub-graph from search, (3) supporting only top-down search, not full-search, and (4) dependency of data structure on XML documents. The purpose of this paper is to present a hybrid XML data model architecture for the storage and search of XML document data. By representing both data and structure views of XML documents, this new XML data model technique overcomes the limitations of previous researches on data model for XML documents as well as the existing database systems such as relational and object-oriented data model