HQEBSKG: Hybrid Query Expansion Based on Semantic Knowledgebase and Grouping

Abstract

Increasing size of web leads to relevant information extraction by traditional and last query expansion methods not to be efficient and encounter an essential challenge. Therefore, to improve query expansion, in this paper, a novel hybrid method called HQEBSKG is introduced. In the first part, a new and efficient technique is proposed based on semantic knowledgebase to expand users’ queries. In the following part, a useful method based on semantic relations between words is also suggested for query expansion. In the current work, to achieve the semantic relations, FarsNet and WordNet ontologies are used simultaneously to enrich knowledge resources. The experimental results show performance improvement.

KEYWORDS:

• Concept graph
• Ontology
• Persian query expansion
• Query expansion
• Semantic grouping
• Semantic knowledge base

Additional information

Notes on contributors

Mohammad Reza Keyvanpour

Mohammad Reza Keyvanpour is an associate professor at Alzahra University, Tehran, Iran. He received his BS in software engineering from Iran University of Science & Technology, Tehran, Iran. He received his MS and PhD in software engineering from Tarbiat Modares University, Tehran, Iran. His research interests include software engineering and data mining. Corresponding author. Email: [email protected]

Zahra Karimi Zandian

Zahra Karimi Zandian received her BS in software engineering from Islamic Azad University, South Tehran Branch, Tehran, Iran. She also received her MS in software engineering from Alzahra University, Tehran, Iran. Her research interests include fraud detection, data mining, machine learning and social network analysis. Email: [email protected]

Zahra Abdolhosseini

Zahra Abdolhosseini received her MS in computer engineering from Alzahra University, Tehran, Iran. Her research interests include data mining and query expansion. Email: [email protected]