Reducing the dependency of having prior domain knowledge for effective online information retrieval

Zammit, Omar ORCID logoORCID: https://orcid.org/0000-0001-5052-4847, Smith, Serengul ORCID logoORCID: https://orcid.org/0000-0003-0777-5637, Windridge, David ORCID logoORCID: https://orcid.org/0000-0001-5507-8516 and De Raffaele, Clifford ORCID logoORCID: https://orcid.org/0000-0002-7081-702X (2022) Reducing the dependency of having prior domain knowledge for effective online information retrieval. Expert Systems , e13014. ISSN 0266-4720 [Article] (Published online first) (doi:10.1111/exsy.13014)

[img] PDF - Final accepted version (with author's formatting)
Restricted to Repository staff and depositor only until 1 June 2023.

Download (992kB) |

Abstract

Sometimes Internet users struggle to find what they are looking for on the Internet due to information overload. Search engines intend to identify documents related to a given keyphrase on the Internet and provide suggestions. Having some background knowledge about a topic or a domain will help in building effective search keyphrases that will lead to accurate results in information retrieval. This is further pronounced among students that rely on the internet to learn about a new topic. Students might not have the required background knowledge to build effective keyphrases and find what they are looking for. In this research, we are addressing this problem, and aim to help students find relevant information online. This research furthers existing literature by enhancing information retrieval frameworks through keyphrase assignment, aiming to expose students to new terminologies, therefore reducing the dependency of having background knowledge about the domain under study. We evaluated this framework and identified how it can be enhanced to suggest more effective search keyphrases. Our proposed suggestion is to introduce a keyphrase Ranking Mechanism that will improve the keyphrase assignment part of the framework by taking into consideration the part-of-speech of the generated keyphrases. To evaluate the proposed approach, various data sets were downloaded and processed. The results obtained showed that our proposed approach produces more effective keyphrases than the existing framework.

Item Type: Article
Keywords (uncontrolled): Artificial Intelligence, Computational Theory and Mathematics, Theoretical Computer Science, Control and Systems Engineering
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 35242
Notes on copyright: This is the peer reviewed version of the following article: Zammit, O., Smith, S., Windridge, D., & De Raffaele, C. (2022). Reducing the dependency of having prior domain knowledge for effective online information retrieval. Expert Systems, e13014., which has been published in final form at https://doi.org/10.1111/exsy.13014. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited
Useful Links:
Depositing User: Jisc Publications Router
Date Deposited: 10 Jun 2022 14:32
Last Modified: 15 Jul 2022 08:16
URI: https://eprints.mdx.ac.uk/id/eprint/35242

Actions (login required)

View Item View Item

Statistics

Activity Overview
6 month trend
1Download
6 month trend
37Hits

Additional statistics are available via IRStats2.