Query formation and information retrieval with ontology

Sheng Uei Guan

doi:10.4018/978-1-59904-192-6.ch013

Query formation and information retrieval with ontology

Sheng Uei Guan

Brunel University London

Research output: Chapter in Book or Report/Conference proceeding › Chapter › peer-review

Abstract

This chapter presents an ontology-based query formation and information retrieval system under the mobile commerce (m-commerce) agent framework. A query formation approach that combines the usage of ontology and keywords is implemented. This approach takes advantage of the tree structure in ontology to form queries visually and efficiently. It also uses additional aids such as keywords to complete the query formation process more efficiently. The proposed information retrieval scheme focuses on using genetic algorithms (GAs) to improve computational effectiveness. Other query optimization techniques used include query restructuring by logical terms and numerical constraints replacement.

Original language	English
Title of host publication	Semantic Web Technologies and E-Business
Subtitle of host publication	Toward the Integrated Virtual Organization and Business Process Automation
Publisher	IGI Global
Pages	310-323
Number of pages	14
ISBN (Print)	9781599041926
DOIs	https://doi.org/10.4018/978-1-59904-192-6.ch013
Publication status	Published - 2006
Externally published	Yes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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10.4018/978-1-59904-192-6.ch013

Cite this

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AB - This chapter presents an ontology-based query formation and information retrieval system under the mobile commerce (m-commerce) agent framework. A query formation approach that combines the usage of ontology and keywords is implemented. This approach takes advantage of the tree structure in ontology to form queries visually and efficiently. It also uses additional aids such as keywords to complete the query formation process more efficiently. The proposed information retrieval scheme focuses on using genetic algorithms (GAs) to improve computational effectiveness. Other query optimization techniques used include query restructuring by logical terms and numerical constraints replacement.

UR - https://www.scopus.com/pages/publications/84900572444

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DO - 10.4018/978-1-59904-192-6.ch013

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EP - 323

BT - Semantic Web Technologies and E-Business

PB - IGI Global

ER -

Query formation and information retrieval with ontology

Abstract

UN SDGs

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