An ontology-supported and fully-automatic annotation technology for semantic portals

Authors:
Sheng-Yuan Yang
Affiliations:
Department of Computer and Communication Engineering, St. John's University, Tamsui, Taipei County, Taiwan
Venue:
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Year:
2007

Citing 6
Cited 1

A vector space model for automatic indexing

Communications of the ACM
Creating Semantic Web Contents with Protégé-2000

IEEE Intelligent Systems
S-CREAM - Semi-automatic CREAtion of Metadata

EKAW '02 Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web
Using the structure of Web sites for automatic segmentation of tables

SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Semi-automatic annotation of contested knowledge on the world wide web

Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
The conflict detection and resolution in knowledge merging for image annotation

Information Processing and Management: an International Journal

OntoPortal: An ontology-supported portal architecture with linguistically enhanced and focused crawler technologies

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

We employ the techniques of ontology and linguistics to develop a fully-automatic annotation technique, which, when coupled with an automatic ontology construction method, can play a key role in the development of semantic portals. Based on this technique, we also demonstrate a semanticportal prototype which defines how a semantic portal is interacting with the user by providing five different types of interaction patterns, including keyword search, synonym search, POS (Part-of-Speech)-constrained keyword search, natural language query, and semantic index search. Our primarily demonstrations show that it can indeed retrieve better semantic-directed information to meet user requests.