Knowledge Discovery from Semi-Structured Data for Conceptual Organization

Authors:
S. Gupta;R. Goyal;K. Shubham;L. Dey;A. Malik;S. Chaudhury;S. Bhattacharya
Affiliations:
Indian Institute of Technology-Delhi, India;Indian Institute of Technology-Delhi, India;Indian Institute of Technology-Delhi, India;Indian Institute of Technology-Delhi, India;Indian Institute of Technology-Delhi, India;Indian Institute of Technology-Delhi, India;AOL India, India
Venue:
WI-IATW '06 Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology
Year:
2006

Citing 8
Cited 0

Video parsing, retrieval and browsing: an integrated and content-based solution

Proceedings of the third ACM international conference on Multimedia
Photobook: content-based manipulation of image databases

International Journal of Computer Vision
Document length normalization

Information Processing and Management: an International Journal - Special issue: history of information science
VideoQ: an automated content based video search system using visual cues

MULTIMEDIA '97 Proceedings of the fifth ACM international conference on Multimedia
Concept decompositions for large sparse text data using clustering

Machine Learning
Machine learning in automated text categorization

ACM Computing Surveys (CSUR)
Intelligent Access to Digital Video: Informedia Project

Computer
A Mathematical Theory of Communication

A Mathematical Theory of Communication

Quantified Score

Hi-index	0.00

Visualization

Abstract

Conceptual organization of semi-structured documents can help in effective retrieval from collections of emails, product complaints, video descriptions etc. In this paper, we propose a conceptual organization scheme for grouping and categorizing semi-structured text data using natural language processing techniques. We propose a knowledge-discovery mechanism that extracts noun phrases from documents and arranges them into concept maps based on their co-occurrence. The emerging concept maps can be used for automatic grouping and conceptual categorization of documents. Further, Phrase structure Grammar is employed to extract relationships among these entities from documents and index the document collection with these relations.