NDLI: A Proposal for a Semantic Intelligent Document Repository Architecture

Content Provider	IEEE Xplore Digital Library
Author	Rodriguez, A. Colomo, R. Gomez, J.M. Alor-Hernandez, G. Posada-Gomez, R. Juarez-Martinez, U. Gayo, J.E.L. Vidyasankar, K.
Copyright Year	2009
Abstract	The processing of high amount of documents is a highly complex challenge, which becomes even more complicated when the goal is to extract the semantically relevant data within the documents. The large-scale processing of immense repositories of knowledge requires techniques which perform information extraction to facilitate the subsequent classification and indexing of texts. Having this into account, we propose the use of Dublin Core metadata for the classification of Software Engineering publications. Based on the information obtained from Dublin Core, we present a global repository that is populated automatically, which takes the form of an ontology which represents the distinct areas of Software Engineering knowledge inspired by SWEBOK (Software Engineering Body of Knowledge). Finally, the process of the classification of texts within the ontology is carried out in three steps: keyword analysis, processing of the document. We believe our proposal based on a linguistic text classification method, heuristics, and subsequently the intersection of the three techniques mentioned, generating more precise search results in response to user queries.
Starting Page	69
Ending Page	75
File Size	585763
Page Count	7
File Format	PDF
ISBN	9780769537993
DOI	10.1109/CERMA.2009.26
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-22
Publisher Place	Mexico
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Support vector machines semantic Web. Support vector machine classification Ontologies Information retrieval Internet Proposals Data mining Intelligent robots Software engineering
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

Two Level Question Classification Based on SVM and Question Semantic Similarity

Relevance feedback with active learning for document retrieval

Learning to Rank Using Semantic Features in Document Retrieval

Support Vector Machines Based Active Learning for the Relevance Feedback Document Retrieval

Boosting item keyword search with spreading activation

Keyphrases extraction from Web document by the least squares support vector machine

Web pages Classification Using Domain Ontology and Clustering

Relevance feedback document retrieval using support vector machines

Building a semantic model of a textual document for efficient search and retrieval

A Proposal for a Semantic Intelligent Document Repository Architecture

Similar Documents

Two Level Question Classification Based on SVM and Question Semantic Similarity

Relevance feedback with active learning for document retrieval

Learning to Rank Using Semantic Features in Document Retrieval

Support Vector Machines Based Active Learning for the Relevance Feedback Document Retrieval

Boosting item keyword search with spreading activation

Keyphrases extraction from Web document by the least squares support vector machine

Web pages Classification Using Domain Ontology and Clustering

Relevance feedback document retrieval using support vector machines

Building a semantic model of a textual document for efficient search and retrieval

A Proposal for a Semantic Intelligent Document Repository Architecture