NDLI: Semantic-Rich Markov Models for Web Prefetching

Content Provider	IEEE Xplore Digital Library
Author	Mabroukeh, N.R. Ezeife, C.I.
Copyright Year	2009
Abstract	Domain knowledge for web applications is currently being made available as domain ontology with the advent of the semantic web, in which semantics govern relationships among objects of interest (e. g., commercial items to be purchased in an e-Commerce web site). Our earlier work proposed to integrate semantic information into all phases of the web usage mining process, for an intelligent semantics-aware web usage mining framework. There are ways to integrate semantic information into Markov models used in the third phase for next page request prediction. Semantic information is combined with the transition probability matrix of a Markov model. This way, it provides a low order Markov model with intelligent accurate predictions and less complexity than higher order models, also solving the problem of contradicting prediction. This paper proposes to use semantic information to prune states in Selective Markov models SMM, semantic information can lead to context-aware higher order Markov models with about 16% less space complexity.
Starting Page	465
Ending Page	470
File Size	263858
Page Count	6
File Format	PDF
ISBN	9781424453849
DOI	10.1109/ICDMW.2009.18
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Prefetching Conferences Web pages Predictive models Ontologies Markov processes Data mining Web sites Context modeling
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A PPM Prediction Model Based on Stochastic Gradient Descent for Web Prefetching

Towards automatic semantic annotation of data rich Web pages

Using Data-Extraction Ontologies to Foster Automating Semantic Annotation

Towards ontology-based semantic web from data-intensive web: A reverse engineering approach

Approximate ontology merging for the semantic Web

GeoReferencing the Semantic Web Based on Geoontology

Dynamic Modeling by Usage Data for Personalization Systems

A new Markov model for Web access prediction

LASSO: a learning architecture for semantic web ontologies

Semantic-Rich Markov Models for Web Prefetching

Similar Documents

A PPM Prediction Model Based on Stochastic Gradient Descent for Web Prefetching

Towards automatic semantic annotation of data rich Web pages

Using Data-Extraction Ontologies to Foster Automating Semantic Annotation

Towards ontology-based semantic web from data-intensive web: A reverse engineering approach

Approximate ontology merging for the semantic Web

GeoReferencing the Semantic Web Based on Geoontology

Dynamic Modeling by Usage Data for Personalization Systems

A new Markov model for Web access prediction

LASSO: a learning architecture for semantic web ontologies

Semantic-Rich Markov Models for Web Prefetching