Optimizing Query Using the FOAF Relation and Graph Neural Networks to Enhance Information Gathering and Retrieval
DOI:
https://doi.org/10.56294/dm2025443Keywords:
Query Expansion, Graph Neural Networks (GNNs), FOAF, Link Predicted, Node embeddingAbstract
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
References
1. ALMarwi, H.; Ghurab, M.; Al-Baltah, I. A hybrid semantic query expansion approach for Arabic information retrieval. J Big Data 7, 39 (2020). https://doi.org/10.1186/s40537-020-00310-z.
2. Alvarado MAG. Gentrification and Community Development: An analysis of the main lines of research. Gentrification 2023;1:2–2. https://doi.org/10.62486/gen20232.
3. B. Wang; L. Cheng; J. Sheng; Z. Hou; Y. Chang, “Graph convolutional networks fusing motif ‑ structure information,” Sci. Rep., pp. 1–12, 2022, doi: 10.1038/s41598-022-13277-z.
4. Castillo VS. Gentrification as a field of study in the last decade: a bibliometric analysis in Scopus. Gentrification 2023;1:5–5. https://doi.org/10.62486/gen20235.
5. Dinkar AK, Haque MA, Choudhary AK. Enhancing IoT Data Analysis with Machine Learning: A Comprehensive Overview. LatIA 2024;2:9–9. https://doi.org/10.62486/latia20249.
6. F. Chen; G. Yin; Y. Dong; G. Li; W. Zhang, “KHGCN: Knowledge-Enhanced Recom-mendation with Hierarchical Graph Capsule Network,” Entropy, vol. 25, no. 4, p. 697, Apr. 2023, doi: 10.3390/e25040697.
7. Fahad, M.. Ontology-based Mediation with Quality Criteria. In International Conference on Business Intelligence, pp. 74-88, (2023, July). Cham: Springer Nature Switzerland.
8. Genes APC. Theoretical foundations and methodological guidelines for the appropriation of ICT in the pedagogical practice of teachers. Multidisciplinar (Montevideo) 2024;2:104–104. https://doi.org/10.62486/agmu2024104.
9. Gonzalez-Argote J, Maldonado EJ. Indicators of scientific production on Health Policy. Management (Montevideo) 2024;2:107–107. https://doi.org/10.62486/agma2024107.
10. Guo, F.; Zhou, W.; Wang, Z.; Ju, C.; Ji, S.; Lu, Q. A link prediction method based on topological nearest-neighbors similarity in directed networks. Journal of Computational Science, 69, 102002 (2023).
11. H. Bu; J. Xia; Q. Wu, “A Dyna H. Bu, J. Xia, Q. Wu, and L. Chen, “A Dynamic Hetero-geneous Information Network Embedding Method Based on Meta-Path and Improved Rotate Model,” Applied Sciences, vol. 12, no. 21, p. 10898, Oct. 2022, doi: 10.3390/app122110898.
12. H. Y. Husni; Yeni Kustiyahningsih; Fika Hastarita Rachman; Eka Mala Sari Rochman, “Query expansion using pseudo relevance feedback based on the bahasa version of the wikipedia dataset,” AIP Conf. Proc., vol. 2679, no. 1, 2023.
13. He, Mingzhen; He, Fan; Shi, Lei; Huang, Xiaolin ; Suykens, Johan. (2022). Learning with Asymmetric Kernels: Least Squares and Feature Interpretation.
14. Hernández-Lugo M de la C. Artificial Intelligence as a tool for analysis in Social Scienc-es: methods and applications. LatIA 2024;2:11–11. https://doi.org/10.62486/latia202411.
15. Hong; Huiting; et al. "An attention-based graph neural network for heterogeneous struc-tural learning." Proceedings of the AAAI conference on artificial intelligence. Vol. 34. No. 04. 2020.
16. Jain; Shivani; K. R. Seeja; Rajni Jindal. "A fuzzy ontology framework in information re-trieval using semantic query expansion." International Journal of Information Manage-ment Data Insights 1.1 (2021): 100009.
17. K. Blagec; A. Barbosa-Silva; S. O. ; M. Samwald, “a curated, ontology-based, large- scale knowledge graph of artificial intelligence tasks and benchmarks.” 2022.
18. León MP. The impact of gentrification policies on urban development. Gentrification 2023;1:4–4. https://doi.org/10.62486/gen20234.
19. M. N. Asim, M.; Wasim, M.; Usman, G.; Khan, N. Mahmood; W. Mahmood, “The Use of Ontology in Retrieval : A Study on Textual , Multilingual , and Multimedia Retrieval,” IEEE Access, vol. 7, pp. 21662–21686, 2019, doi: 10.1109/ACCESS.2019.2897849.
20. Madariaga FJD. Pedagogical model for the integration of ICTs into teaching practices in official educational institutions in rural Monteria. Multidisciplinar (Montevideo) 2024;2:105–105. https://doi.org/10.62486/agmu2024%p.
21. Makarov I; Makarov M; Kiselev D. Fusion of text and graph information for machine learning problems on networks. PeerJ Comput Sci. (2021,May)11;7:e526. doi: 10.7717/peerj-cs.526.
22. Muthusundari M, Velpoorani A, Kusuma SV, L T, Rohini O k. Optical character recogni-tion system using artificial intelligence. LatIA 2024;2:98–98. https://doi.org/10.62486/latia202498.
23. Navarro WS, Duque NEA, Ramirez FMB, Chaparro AMT. Strategic Analysis from a con-sulting context for the Super Kinder School Institution. Management (Montevideo) 2024;2:30–30. https://doi.org/10.62486/agma202430.
24. P. Zhang; J. Chen; C. Che; L. Zhang; B. Jin; Y. Zhu, “IEA-GNN : Anchor-aware graph neural network fused with information entropy for node classification and link predic-tion,” Inf. Sci. (Ny)., vol. 634, no. November 2022, pp. 665–676, 2023, doi: 10.1016/j.ins.2023.03.022.
25. Pérez GAJ, Cruz JMH de la. Applications of Artificial Intelligence in Contemporary So-ciology. LatIA 2024;2:12–12. https://doi.org/10.62486/latia202412.
26. Pirela CV, Plata AO, Hernandez GL. Strategic thinking as a potential factor in the growth of companies in the dairy sector. Management (Montevideo) 2024;2:40–40. https://doi.org/10.62486/agma202440.
27. Sonal D, Mishra K, Haque A, Uddin F. A Practical Approach to Increase Crop Produc-tion Using Wireless Sensor Technology. LatIA 2024;2:10–10. https://doi.org/10.62486/latia202410.
28. Sun; Xiaohan; Li Zhang. "Multi-order nearest neighbor prediction for recommendation systems." Digital Signal Processing, 127 (2022). https://doi.org/10.1016/j.dsp.2022.103540.
29. Vargas OLT, Agredo IAR. Active packaging technology: cassava starch/orange essential oil for antimicrobial food packaging. Multidisciplinar (Montevideo) 2024;2:102–102. https://doi.org/10.62486/agmu2024102.
30. Velazquez MDCR, Chririnos AAN, Brito AV. Decision-making styles developed by commercial enterprises in the municipality of Barrancas. Management (Montevideo) 2024;2:35–35. https://doi.org/10.62486/agma202435.
31. X. F. Yonghao Liu; Renchu Guan; Fausto Giunchiglia; Yanchun Liang, “Deep Attention Diffusion Graph Neural Networks for Text Classificatio.” pp. 8142–8152, 2021.
32. X. Liu; X. Li; G. Fiumara; P. De Meo, “Link prediction approach combined graph neural network with capsule network,” Expert Syst. Appl., vol. 212, no. August 2021, p. 118737, 2023, doi: 10.1016/j.eswa.2022.118737.
33. Zhang, C.; Zhu, H.; Peng, X.Q.; Wu, J.; Xu, K. (2021). Hierarchical Information Matters: Text Classification via Tree Based Graph Neural Network. International Conference on Computational Linguistics.
Zhang, X. "Improving personalised query reformulation with embeddings. Journal of In-formation Science, 48(4), 503-523(2022)
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Ahmed Mahdi Abdulkadium, Asaad Sabah Hadi (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
