Özetleme Mekanizması Kullanılarak Bilgi Çizgesine Yeni Eklentiler

Yıl 2023, Cilt: 35 Sayı: 3, 312 - 321, 30.09.2023

Savaş Takan

https://doi.org/10.7240/jeps.1244034

Öz

Bilginin doğasına ilişkin, onu şekillendiren çok çeşitli unsurlar bulunmaktadır. Örneğin güvenirlik, tutarlılık, değişmezlik ve bağlam gibi mekanizmalar bunların başında gelir. Ancak söz konusu mekanizmaların bilgi çizgesinde temsil edilmesi oldukça yaygın bir problemdir. Çalışmamızda bu problemin çözümüne katkıda bulunmak amacıyla, bilginin karmaşık doğasına ilişkin güven, tutarlılık, değişmezlik ve bağlam gibi temel mekanizmalar, hashing teknolojisi kullanılarak bilgi çizgesine entegre edilmiştir. Çalışmamızda bu eklentiler, bilgi çizgesinden ayrı tutularak, yapıların işlevselliklerinin bozulmaması sağlanmıştır. Geliştirdiğimiz eklentiler sayesinde bir bilgi değiştiğinde onu etkileyen tüm bilgilerin otomatik güncellenmesi, belirsizlik, bilgiler arasında sıralama yapılamaması, bazı bilgilerin değişmez olarak tutulamaması ve bilgiler arasında hızlı bir karşılaştırmanın yapılamaması gibi yaygın bilgi çizgesi problemleri, örnek senaryolar üzerinden test edilerek çözüme kavuşturulmuştur. Çalışmamızın, bilgi çizgesinin iyileştirilmesine yönelik literatüre ve bilgi çizgesini kullanan yapay zeka yazılımlarının geliştirilmesine katkı sunması beklenmektedir.

Anahtar Kelimeler

Bilgi çizgesi, hashing, yapay zeka, yazılım mühendisliği, bilgi temsili

Destekleyen Kurum

Destekleyen kurum bulunmamaktadır.

Novel Extensions to the Knowledge Graph Using the Hashing Mechanism

Öz

There are various elements related to the nature of knowledge that shape it. For example, mechanisms such as reliability, consistency, invariance and context are among the main ones. However, representing these mechanisms in the knowledge graph is a common problem. In our work, in order to contribute to the solution of this problem, basic mechanisms related to the complex nature of information such as trust, consistency, immutability and context are integrated into the knowledge graph using hashing technology. In our work, these plugins are kept separate from the knowledge graph so that the functionality of the structures is not impaired. Thanks to the plugins we developed, common knowledge graph problems such as automatic updating of all the information that affects a piece of information when it changes, ambiguity, inability to sort information, inability to keep some information immutable, and inability to make a quick comparison between information are tested and solved through example scenarios. Our work is expected to contribute to the literature on knowledge graph improvement and to the development of artificial intelligence software that utilizes knowledge graphs.

Anahtar Kelimeler

Bilgi çizgesi, hashing, yapay zeka, yazılım mühendisliği, bilgi temsili

Kaynakça

• Alsaig, A., Alagar, V., & Nematollaah, S., Contelog: A declarative language for modeling and reasoning with contextual knowledge. Knowledge-Based Systems, 207, 106403, (2020).
• Balakrishnan, D., Ziarek, L., & Kennedy, O., Fluid data structures. In 17th ACM SIGPLAN International Symposium on Database Programming Languages, 3–17., (2019).
• Bello López, P., & De Ita Luna, G., An Algorithm to Belief Revision and to Verify Consistency of a Knowledge Base. IEEE Latin America Transactions, 19(11), 1867–1874, (2021).
• Besançon, L., Da Silva, C. F., Ghodous, P., & Gelas, J.-P., A Blockchain Ontology for DApps Development. IEEE Access, 10, 49905–49933, (2022).
• Cambria, E., Ji, S., Pan, S., & Yu, P. S., Knowledge graph representation and reasoning. Neurocomputing, 461, 494–496, (2021).
• Cano-Benito, J., Cimmino, A., & García-Castro, R., Toward the Ontological Modeling of Smart Contracts: A Solidity Use Case. IEEE Access, 9, 140156–140172, (2021).
• Chen, X., Jia, S., & Xiang, Y., A review: Knowledge reasoning over knowledge graph. Expert Systems with Applications, 141, 112948, (2020).
• Chen, X., Xie, H., Li, Z., & Cheng, G., Topic analysis and development in knowledge graph research: A bibliometric review on three decades. Neurocomputing, 461, 497–515, (2021).
• Chen, Z., Wang, Y., Zhao, B., Cheng, J., Zhao, X., & Duan, Z., Knowledge Graph Completion: A Review. IEEE Access, 8, 192435–192456, (2020).
• Chowdhury, M. J. M., Colman, A., Kabir, M. A., Han, J., & Sarda, P., Blockchain Versus Database: A Critical Analysis. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), 1348–1353, (2018).
• Christoforou, E., Nordio, A., Tarable, A., & Leonardi, E., Ranking a Set of Objects: A Graph Based Least-Square Approach. IEEE Transactions on Network Science and Engineering, 8(1), 803–813, (2021).
• Dai, Y., Wang, S., Xiong, N. N., & Guo, W., A Survey on Knowledge Graph Embedding: Approaches, Applications and Benchmarks. Electronics, 9(5), 750, (2020).
• Delgrande, J. P., & Schaub, T., A consistency-based approach for belief change. Artificial Intelligence, 151(1), 1–41, (2003).
• Grant, J., Molinaro, C., & Parisi, F., Probabilistic spatio-temporal knowledge bases: Capacity constraints, count queries, and consistency checking. International Journal of Approximate Reasoning: Official Publication of the North American Fuzzy Information Processing Society, 100, 1–28, (2018).
• Huang, Y., Zhang, L., Yang, X., Chen, Z., Liu, J., Li, J., & Hong, W., An Efficient Graph-Based Algorithm for Time-Varying Narrowband Interference Suppression on SAR System. IEEE Transactions on Geoscience and Remote Sensing, 59(10), 8418–8432, (2021).
• Issa, S., Adekunle, O., Hamdi, F., Cherfi, S. S.-S., Dumontier, M., & Zaveri, A., Knowledge Graph Completeness: A Systematic Literature Review. IEEE Access, 9, 31322–31339, (2021).
• Jabla, R., Khemaja, M., Buendia, F., & Faiz, S., Automatic Rule Generation for Decision-Making in Context-Aware Systems Using Machine Learning. Computational Intelligence and Neuroscience, 2022, 5202537, (2022).
• Jiang, S., Liu, Y., Zhang, Y., Luo, P., Cao, K., Xiong, J., Zhao, H., & Wei, J., Reliable Semantic Communication System Enabled by Knowledge Graph. Entropy, 24(6), (2022).
• Kejriwal, M., Knowledge Graphs: A Practical Review of the Research Landscape. Information. An International Interdisciplinary Journal, 13(4), 161, (2022).
• Khan, N., Ma, Z., Yan, L., & Ullah, A., Hashing-based semantic relevance attributed knowledge graph embedding enhancement for deep probabilistic recommendation. Applied Intelligence, Dordrecht, Netherlands, 53(2), 2295–2320, (2023).
• Liberatore, P., & Schaerf, M., Belief Revision and Update: Complexity of Model Checking. Journal of Computer and System Sciences, 62(1), 43–72, (2001).
• Muiño, D. P., Measuring and repairing inconsistency in probabilistic knowledge bases. International Journal of Approximate Reasoning: Official Publication of the North American Fuzzy Information Processing Society, 52(6), 828–840, (2011).
• Mu, K., Responsibility for inconsistency. International Journal of Approximate Reasoning: Official Publication of the North American Fuzzy Information Processing Society, 61, 43–60, (2015).
• Mu, K., Measuring inconsistency with constraints for propositional knowledge bases. Artificial Intelligence, 259, 52–90, (2018). Nickel, M., Murphy, K., Tresp, V., & Gabrilovich, E., A Review of Relational Machine Learning for Knowledge Graphs. Proceedings of the IEEE, 104(1), 11–33, (2016).
• Nirmala, P., & Nadarajan, R., Cumulative centrality index: Centrality measures based ranking technique for molecular chemical structural graphs. Journal of Molecular Structure, 1247, 131354, (2022).
• Noy, N., Gao, Y., Jain, A., Narayanan, A., Patterson, A., & Taylor, J., Industry-scale Knowledge Graphs: Lessons and Challenges. ACM Queue: Tomorrow’s Computing Today, 17(2), 48–75, (2019).
• Opdahl, A. L., Al-Moslmi, T., Dang-Nguyen, D.-T., Gallofré Ocaña, M., Tessem, B., & Veres, C., Semantic Knowledge Graphs for the News: A Review. ACM Comput. Surv., 55(7), 1–38, (2022).
• Ozdayi, M. S., Kantarcioglu, M., & Malin, B., Leveraging blockchain for immutable logging and querying across multiple sites. BMC Medical Genomics, 13(Suppl 7), 82, (2020).
• Porebski, S., Evaluation of fuzzy membership functions for linguistic rule-based classifier focused on explainability, interpretability and reliability. Expert Systems with Applications, 199, 117116, (2022).
• Rajabi, E., & Etminani, K., Knowledge-graph-based explainable AI: A systematic review. Journal of Information Science and Engineering, 01655515221112844, (2022).
• Ryen, V., Soylu, A., & Roman, D., Building Semantic Knowledge Graphs from (Semi-)Structured Data: A Review. Future Internet, 14(5), 129, (2022).
• Sciriha, I., & da Fonseca, C. M., On the rank spread of graphs. Linear and Multilinear Algebra, 60(1), 73–92, (2012).
• Seo, S., Oh, B., & Lee, K.-H., Reliable Knowledge Graph Path Representation Learning. IEEE Access, 8, 32816–32825, (2020).
• Stančić, H., & Bralić, V., Digital Archives Relying on Blockchain: Overcoming the Limitations of Data Immutability. Computers, 10(8), 91, (2021).
• Stock, K., & Yousaf, J., Context-aware automated interpretation of elaborate natural language descriptions of location through learning from empirical data. International Journal of Geographical Information Science: IJGIS, 32(6), 1087–1116, (2018).
• Terenziani, P., Integrated temporal reasoning with periodic events. Computational Intelligence. An International Journal, 16(2), 210–256, (2000).
• Troussas, C., & Krouska, A., Path-Based Recommender System for Learning Activities Using Knowledge Graphs. Information. An International Interdisciplinary Journal, 14(1), 9, (2022).
• Van Beek, P., & Dechter, R., Constraint tightness and looseness versus local and global consistency. Journal of the ACM, 44(4), 549–566, (1997).
• Verma, S., Bhatia, R., Harit, S., & Batish, S., Scholarly knowledge graphs through structuring scholarly communication: A review. Complex & Intelligent Systems, 1–37, (2022).
• Wang, H., Shang, Y., & Qiao, X., The Integrated Organization of Data and Knowledge Based on Distributed Hash. 2020 IEEE International Conference on Knowledge Graph (ICKG), 243–250, (2020).
• Wu, W., Zhu, Z., Zhang, G., Kang, S., & Liu, P., A reasoning enhance network for muti-relation question answering. Applied Intelligence, 51(7), 4515–4524, (2021).
• Yang, M., Chen, K., Sun, S., Han, Z., Kong, L., & Meng, Q., A Pattern Driven Graph Ranking Approach to Attribute Extraction for Knowledge Graph. IEEE Transactions on Industrial Informatics, 18(2), 1250–1259, (2022).
• Yeh, I., Karp, P. D., Noy, N. F., & Altman, R. B., Knowledge acquisition, consistency checking and concurrency control for Gene Ontology (GO). Bioinformatics, 19(2), 241–248, (2003).