CFP last date
28 April 2025
Call for Paper
May Edition
JAAI solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 28 April 2025

Submit your paper
Know more
Reseach Article

Investigating Knowledge Graphs for Identifying the Scope of Advancements in Knowledge Graph Embedding and Reasoning Techniques

by Mosam Patel
Journal of Advanced Artificial Intelligence
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 6
Year of Publication: 2025
Authors: Mosam Patel
10.5120/jaai202427

Mosam Patel . Investigating Knowledge Graphs for Identifying the Scope of Advancements in Knowledge Graph Embedding and Reasoning Techniques. Journal of Advanced Artificial Intelligence. 1, 6 ( Mar 2025), 9-14. DOI=10.5120/jaai202427

@article{ 10.5120/jaai202427,
author = { Mosam Patel },
title = { Investigating Knowledge Graphs for Identifying the Scope of Advancements in Knowledge Graph Embedding and Reasoning Techniques },
journal = { Journal of Advanced Artificial Intelligence },
issue_date = { Mar 2025 },
volume = { 1 },
number = { 6 },
month = { Mar },
year = { 2025 },
pages = { 9-14 },
numpages = {9},
url = { https://jaaionline.phdfocus.com/archives/volume1/number6/investigating-knowledge-graphs-for-identifying-the-scope-of-advancements-in-knowledge-graph-embedding-and-reasoning-techniques/ },
doi = { 10.5120/jaai202427 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-04-01T01:51:21.695734+05:30
%A Mosam Patel
%T Investigating Knowledge Graphs for Identifying the Scope of Advancements in Knowledge Graph Embedding and Reasoning Techniques
%J Journal of Advanced Artificial Intelligence
%V 1
%N 6
%P 9-14
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Enormous amount of data in varied formats is generated daily through various sources. Knowledge Graphs has got the amazing capability to fetch the information and efficiently organize it so as to explore the linkages of the Entities. Hence, ever since the Knowledge Graphs (KG) were introduced by Google in 2012, its popularity has increased in almost all fields owing to its promising performance. Many existing techniques are being replaced by Knowledge Graphs, taking into consideration, its efficient ways to extract the required information by interlinking the nodes. Though the researchers are working on generating a generic KG, so that it can cater more applications, still there are many challenges which the researchers are facing in achieving it. This paper focuses on investigating the Knowledge Graph Embedding, and Reasoning Techniques. The benefits and challenges of various methods for dealing with Embedding and Reasoning is being investigated and presented in the paper. At the end, to summarize the study, the various scopes of advancements in Knowledge Graph are also presented in brief. This investigation will help the researchers in paving different paths of application and research in Knowledge Graph Embedding and Reasoning.

References
  1. Abbas, N., Alghamdi, K., Alinam, M., Alloatti, F., Amaral, G., d’Amato, C., Asprino, L., Beno, M., Bensmann, F., Biswas, R., Cai, L., Capshaw, R., Carriero, V. A., Celino, I., Dadoun, A., De Giorgis, S., Delva, H., Domingue, J., Dumontier, M., … Xu, W. (2020). Knowledge Graphs Evolution and Preservation—A Technical Report from ISWS 2019. ArXiv:2012.11936 [Cs]. http://arxiv.org/abs/2012.11936
  2. Akrami, F., Guo, L., Hu, W., & Li, C. (2018). Re-evaluating Embedding-Based Knowledge Graph Completion Methods. Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 1779–1782. https://doi.org/10.1145/3269206.3269266
  3. Al-Moslmi, T., Gallofre Ocana, M., L. Opdahl, A., & Veres, C. (2020). Named Entity Extraction for Knowledge Graphs: A Literature Overview. IEEE Access, 8, 32862–32881. https://doi.org/10.1109/ACCESS.2020.2973928
  4. Bansal, T., Juan, D.-C., Ravi, S., & McCallum, A. (2019). A2N: Attending to Neighbors for Knowledge Graph Inference. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 4387–4392. https://doi.org/10.18653/v1/P19-1431
  5. Bianchi, F., Rossiello, G., Costabello, L., Palmonari, M., & Minervini, P. (2020). Knowledge Graph Embeddings and Explainable AI. ArXiv:2004.14843 [Cs]. https://doi.org/10.3233/SSW200011
  6. Chen, X., Jia, S., & Xiang, Y. (2020). A review: Knowledge reasoning over knowledge graph. Expert Systems with Applications, 141. https://doi.org/10.1016/j.eswa.2019.112948
  7. Cui, Z., Pan, L., Liu, S., & Cui, L. (2019). Infer Latent Privacy for Attribute Network in Knowledge Graph. 2019 IEEE International Conference on Big Data (Big Data), 2542–2551. https://doi.org/10.1109/BigData47090.2019.9006509
  8. Dai, Y., Wang, S., Xiong, N. N., & Guo, W. (2020). A Survey on Knowledge Graph Embedding: Approaches, Applications and Benchmarks. Electronics, 9(5), 750. https://doi.org/10.3390/electronics9050750
  9. Fatemi, B., Taslakian, P., Vazquez, D., & Poole, D. (2020). Knowledge Hypergraphs: Prediction Beyond Binary Relations. ArXiv:1906.00137 [Cs, Stat]. http://arxiv.org/abs/1906.00137
  10. Galassi, A., Lippi, M., & Torroni, P. (2020). Attention in Natural Language Processing. IEEE Transactions on Neural Networks and Learning Systems, 1–18. https://doi.org/10.1109/TNNLS.2020.3019893
  11. Guan, S., Jin, X., Guo, J., Wang, Y., & Cheng, X. (2020). NeuInfer: Knowledge Inference on N-ary Facts. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 6141–6151. https://doi.org/10.18653/v1/2020.acl-main.546
  12. Ji, S., Pan, S., Cambria, E., Marttinen, P., & Yu, P. S. (2021). A Survey on Knowledge Graphs: Representation, Acquisition and Applications. IEEE Transactions on Neural Networks and Learning Systems, 1–21. https://doi.org/10.1109/TNNLS.2021.3070843
  13. Kim, K., Hur, Y., Kim, G., & Lim, H. (2020). GREG: A Global Level Relation Extraction with Knowledge Graph Embedding. Applied Sciences, 10(3), 1181. https://doi.org/10.3390/app10031181
  14. Koncel-Kedziorski, R., Bekal, D., Luan, Y., Lapata, M., & Hajishirzi, H. (2019). Text Generation from Knowledge Graphs with Graph Transformers. ArXiv:1904.02342 [Cs]. http://arxiv.org/abs/1904.02342
  15. Lee, C.-W., Fang, W., Yeh, C.-K., & Wang, Y.-C. F. (2018). Multi-label Zero-Shot Learning with Structured Knowledge Graphs. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1576–1585. https://doi.org/10.1109/CVPR.2018.00170
  16. Lin, J., Zhao, Y., Huang, W., Liu, C., & Pu, H. (2021). Domain knowledge graph-based research progress of knowledge representation. Neural Computing and Applications, 33(2), 681–690. https://doi.org/10.1007/s00521-020-05057-5
  17. Ma, Y., Tresp, V., & Daxberger, E. (2018). Embedding Models for Episodic Knowledge Graphs. ArXiv:1807.00228 [Cs]. http://arxiv.org/abs/1807.00228
  18. Popovski, G., Seljak, B. K., & Eftimov, T. (2020). A Survey of Named-Entity Recognition Methods for Food Information Extraction. IEEE Access, 8, 31586–31594. https://doi.org/10.1109/ACCESS.2020.2973502
  19. Saxena, A., Tripathi, A., & Talukdar, P. (2020). Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 4498–4507. https://doi.org/10.18653/v1/2020.acl-main.412
  20. Shen, Y., Ding, N., Zheng, H.-T., Li, Y., & Yang, M. (2020). Modeling Relation Paths for Knowledge Graph Completion. IEEE Transactions on Knowledge and Data Engineering, 1–1. https://doi.org/10.1109/TKDE.2020.2970044
  21. Wang, Q., Mao, Z., Wang, B., & Guo, L. (2017). Knowledge Graph Embedding: A Survey of Approaches and Applications. IEEE Transactions on Knowledge and Data Engineering, 29(12), 2724–2743. https://doi.org/10.1109/TKDE.2017.2754499
  22. Wang, X., Wang, D., Xu, C., He, X., Cao, Y., & Chua, T.-S. (2019). Explainable Reasoning over Knowledge Graphs for Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 33, 5329–5336. https://doi.org/10.1609/aaai.v33i01.33015329
  23. Zou, X. (2020). A Survey on Application of Knowledge Graph. Journal of Physics: Conference Series, 1487, 012016. https://doi.org/10.1088/1742-6596/1487/1/012016
Index Terms

Computer Science
Information Sciences

Keywords

Knowledge Graphs