Ontology learning for search applications

Authors:
Jon Atle Gulla;Hans Olaf Borch;Jon Espen Ingvaldsen
Affiliations:
Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim;Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim;Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim
Venue:
OTM'07 Proceedings of the 2007 OTM Confederated international conference on On the move to meaningful internet systems: CoopIS, DOA, ODBASE, GADA, and IS - Volume Part I
Year:
2007

Citing 14
Cited 6

Understanding Quality in Conceptual Modeling

IEEE Software
Query expansion using lexical-semantic relations

SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Query expansion using local and global document analysis

SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Improving automatic query expansion

Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
KEA: practical automatic keyphrase extraction

Proceedings of the fourth ACM conference on Digital libraries
Ontology Learning for the Semantic Web

Ontology Learning for the Semantic Web
Learning Algorithms for Keyphrase Extraction

Information Retrieval
Linguistics in Large-Scale Web Search

NLDB '02 Proceedings of the 6th International Conference on Applications of Natural Language to Information Systems-Revised Papers
Ontologies: How can They be Built?

Knowledge and Information Systems
Learning Domain Ontologies from Document Warehouses and Dedicated Web Sites

Computational Linguistics
A language model approach to keyphrase extraction

MWE '03 Proceedings of the ACL 2003 workshop on Multiword expressions: analysis, acquisition and treatment - Volume 18
Text2Onto: a framework for ontology learning and data-driven change discovery

NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
Introduction to semantic web ontology languages

Proceedings of the First international conference on Reasoning Web
Document space adapted ontology: application in query enrichment

NLDB'06 Proceedings of the 11th international conference on Applications of Natural Language to Information Systems

A Hybrid Approach to Ontology Relationship Learning

NLDB '08 Proceedings of the 13th international conference on Natural Language and Information Systems: Applications of Natural Language to Information Systems
On Significance of Ontology Quality in Ontology-Driven Web Search

WSKS '08 Proceedings of the 1st world summit on The Knowledge Society: Emerging Technologies and Information Systems for the Knowledge Society
An interactive ontology learning workbench for non-experts

Proceedings of the 2nd international workshop on Ontologies and information systems for the semantic web
Empirical Insights on a Value of Ontology Quality in Ontology-Driven Web Search

OTM '08 Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part II on On the Move to Meaningful Internet Systems
Evaluating Automatically a Text Miner for Ontologies: A Catch-22 Situation?

OTM '08 Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part II on On the Move to Meaningful Internet Systems
An ontology-driven approach to web search: analysis of its sensitivity to ontology quality and search tasks

Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services

Quantified Score

Hi-index	0.00

Visualization

Abstract

Ontology learning tools help us build ontologies cheaper by applying sophisticated linguistic and statistical techniques on domain text. For ontologies used in search applications class concepts and hierarchical relationships at the appropriate level of detail are vital to the quality of retrieval. In this paper, we discuss an unsupervised keyphrase extraction system for ontology learning and evaluate its resulting ontology as part of an ontology-driven search application. Our analysis shows that even though the ontology is slightly inferior to manually constructed ontologies, the quality of search is only marginally affected when using the learned ontology. Keyphrase extraction may not be sufficient for ontology learning in general, but is surprisingly effective for ontologies specifically designed for search.