An Integrated Search Framework for Leveraging the Knowledge-Based Web Ecosystem

Authors

DOI:

https://doi.org/10.3127/ajis.v24i0.2331

Keywords:

integrated search framework, digital ecosystem, information retrieval, information management, search engine, crawler, text classification

Abstract

The explosion of information constrains the judgement of search terms associated with Knowledge-Based Web Ecosystem (KBWE), making the retrieval of relevant information and its knowledge management challenging. The existing information retrieval (IR) tools and their fusion in a framework need attention, in which search results can effectively be managed. In this article, we demonstrate the effective use of information retrieval services by a variety of users and agents in various KBWE scenarios. An innovative Integrated Search Framework (ISF) is proposed, which utilises crawling strategies, web search technologies and traditional database search methods. Besides, ISF offers comprehensive, dynamic, personalized, and organization-oriented information retrieval services, ranging from the Internet, extranet, intranet, to personal desktop. In this empirical research, experiments are carried out demonstrating the improvements in the search process, as discerned in the conceptual ISF. The experimental results show improved precision compared with other popular search engines.

Author Biographies

Dengya Zhu, Curtin University

Dr Dengya Zhu is an adjunct research fellow at the School of Information Systems, Curtin Business School. He has a broad range of experience in government, industry, and academic research. Dr Zhu’s research interests include information retrieval, data mining, machine learning, natural language processing, big data, sentiment analysis, open source software and software development. His research projects are usually practically oriented to address real world issues.

 

 

Shastri Lakshman Nimmagadda, Curtin University

Dr Shastri is presently an adj. research fellow at the School of Management (Information Systems) at Curtin Business School, Curtin University, Australia. Shastri worked for Schlumberger Company in multiple geo-markets worldwide as an Expert in Geosciences. Prior to that he worked for several petroleum operating and service companies in India, Australia, Uganda, Kuwait, Abu Dhabi, Egypt, Malaysia, Colombia, Indonesia and Russia for more than 25 years. He did his PhD in Information Systems with Master of Information Technology in distinction from Curtin University, Australia.  He obtained M Tech and PhD in Exploration Geophysics from the Indian Institute of Technology, Kharagpur, India. His academic and industry research include, Big Data support in Industry Environments, supply chain business data modelling, data integration, warehouse modelling, processing, interpretation and knowledge mapping including research in domain applications. He has published and presented more than 100 research and technical papers in various international journals and conference proceedings in the areas of geophysics, oil & gas exploration and information systems.

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Published

2020-10-19

How to Cite

Zhu, D., Nimmagadda, S. L., Reiners, T., & Rudra, A. (2020). An Integrated Search Framework for Leveraging the Knowledge-Based Web Ecosystem. Australasian Journal of Information Systems, 24. https://doi.org/10.3127/ajis.v24i0.2331

Issue

Vol. 24 (2020)

Section

Research Articles

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