IDEAS home Printed from https://ideas.repec.org/a/dbk/datame/v4y2025ip443id1056294dm2025443.html
   My bibliography  Save this article

Optimizing Query Using the FOAF Relation and Graph Neural Networks to Enhance Information Gathering and Retrieval

Author

Listed:
  • Ahmed Mahdi Abdulkadium
  • Asaad Sabah Hadi

Abstract

A lot of students suffer expressing their desired enquiry about to a search engine (SE), and this, in turn, can lead to ambiguit and insufficient results. A poor expression requires expanding a previous user query and refining it by adding more vocabularies that make a query more understandable through the searching process. This research aims at adding vocabulary to an enquiry by embedding features related to each keyword, and representing a feature of each query keyword as graphs and node visualization based on graph convolution network (GCN). This is achieved following two approaches. The first is by mapping between vertices, adding a negative link, and training a graph after embedding. This can help check whether new information reach-es for retrieving data from the predicted link. Another approach is based on adding link and node embedding that can create the shortest path to reaching a specific (target) node, . Particularly, poor data retrieval can lead to a new concept named graph expansion network (GEN). Query expansion (QE) techniques can obtain all documents related to expanding and refining query. On the other hand, such documents are represented as knowledge graphs for mapping and checking the similarity between the connection of a graph based on two authors who have similar interst in a particular field, or who collaborate in a research publications. This can create paths or edges between them as link embedding, thereby increasing the accuracy of document or pa-per retrieval based on user typing

Suggested Citation

Handle: RePEc:dbk:datame:v:4:y:2025:i::p:443:id:1056294dm2025443
DOI: 10.56294/dm2025443
as

Download full text from publisher

To our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a
for a similarly titled item that would be available.

More about this item

Statistics

Access and download statistics

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:dbk:datame:v:4:y:2025:i::p:443:id:1056294dm2025443. See general information about how to correct material in RePEc.

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

We have no bibliographic references for this item. You can help adding them by using this form .

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Javier Gonzalez-Argote (email available below). General contact details of provider: https://dm.ageditor.ar/ .

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.