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Journal of Discrete Mathematical Sciences and Cryptography cover

Journal of Discrete Mathematical Sciences and Cryptography

(Since 1998)
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0065·ISSN (Print): 0972-0529

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Monthly Journal: Publishes theoretical and applied research in all areas of Discrete Mathematical Sciences, Cryptography, Combinatorics, Elliptic Curves and Information Security.

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Journal of Discrete Mathematical Sciences and Cryptography
Open Access Research Article

Semantic search framework over knowledge bases using embeddings-based similarity

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pp. 1963–1975Vol. 27Issue 6September 2024DOI: 10.47974/JDMSC-2047 Crossmark XML
Received:
12 Dec 2023
Published Online:
16 Sep 2024
Article type:
Research Article
Language:
EN
Article no.:
JDMSC-2047
Pages:
1963–1975

Abstract

With tremendous progress attained towards AI, understanding context from user queries has become a focus area to provide precise answers. In this direction, recent machine learning-based approaches are trying to extract semantic features from queries. Being keywords driven, these approaches underperform to cater semantics in the queries. To overcome this reliance on syntactic representations, semantics-based methods utilizing Knowledge Bases are being augmented to the query processing systems. In this paper, a novel semantic search framework over Knowledge Bases has been proposed and implemented. Semantic similarity between query and predicates is computed using BERT-based embeddings. Additionally, a dataset of semantically similar sentence variations is generated using ChatGPT for analysing the accuracy of the implemented system over YAGO Knowledge Base.

Keywords

Semantic searchKnowledge baseSemantic similaritySPARQLSentence embeddings

Subject Classifications

(2010) 68T3068T3568T50

References

[1] V. Yadav and S. Bethard, “A Survey on Recent Advances in Named Entity Recognition from Deep Learning models,” arXiv preprint arXiv:1910.11470 (2019).
[2] C. Antoniou and N. Bassiliades, “A survey on semantic question answering systems,” The Knowledge Engineering Review, 37, pp. 1-42 (2022).
[3] Z. Ge, Y. Wang, H. Yan, and X. Xu, “A Learning-Based Semantic Approximate Query over RDF Knowledge Graph,” In 2018 6th International Conference on Advanced Cloud and Big Data (CBD), IEEE, pp. 135-141 (2018).
[4] M. Chhikara, and S.K. Malik, “A recommendation system for online social semantic network using knowledge based, content based and collaborative filtering,” Journal of Information and Optimization Sciences, 44(4), pp. 795-806 (2023).
[5] H. Buzaaba and T. Amagasa, “Question answering over knowledge base: a scheme for integrating subject and the identified relation to answer simple questions,” SN Computer Science, 2(1), pp 1-13 (2021).
[6] A. Pereira, A. Trifan, R. Lopes and J. Oliveira, “Systematic review of question answering over knowledge bases,” IET Software, 16(1), pp. 1-13 (2022).
[7] N. Karim, K. Latif, N. Ahmed, M. Fatima, and A. Mumtaz, “Mapping natural language questions to SPARQL queries for job search,” In 2013 IEEE Seventh International Conference on Semantic Computing,  IEEE, pp. 150-153 September (2013).
[8] A. Fatima, C. Luca, and M. Hobbs, “Free-text user queries for semantic search,” In 2015 IEEE 13th International Conference on Industrial Informatics (INDIN), IEEE, pp. 838-843, July (2015).
[9] R. Cocco, M. Atzori, and C. Zaniolo, “Machine learning of SPARQL templates for question answering over linked spending,” In 2019 IEEE 28th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), IEEE, pp. 156-161, June (2019).
[10] P. Gayathri, and V. V. Rajendran, “Semantic search on summarized RDF triples,” In 2017 International Conference on Intelligent Computing and Control (I2C2), IEEE, pp. 1-6, June (2017).
[11] K. Rafes, S. Abiteboul, S. Cohen-Boulakia, and B. Rance, “Designing scientific SPARQL queries using autocompletion by snippets,” In 2018 IEEE 14th International Conference on e-Science (e-Science),  IEEE, pp. 234-244, October (2018).
[12] S. Campinas, T. E. Perry, D. Ceccarelli, R. Delbru, and G. Tummarello, “Introducing RDF graph summary with application to assisted SPARQL formulation,” In 2012 23rd International Workshop on Database and Expert Systems Applications, IEEE, pp. 261-266, September (2012).
[13] R. A. Kadir, A. R. Yauri, and A. Azman, “Automated Semantic Query Formulation for Document Retrieval,” In 4th International Conference on Information Retrieval & Knowledge Management, IEEE, pp. 1-8 (2018).
[14] A. Ali, and O. Qayyum, “Inference New Knowledge Using Sparql Construct Query,” In 2nd International Conference on Computing, Mathematics & Engineering Technologies (iCoMET), IEEE, pp. 1-4 (2019).
[15] A. A. Khan and S. K. Malik, “Knowledge Base Entity Lookup using Named Entity Recognition: a case study on YAGO”, 2022 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India, IEEE, pp. 429-434 (2022).
[16] S. Shelake and V. Honmane, “Entity Recognition by Natural Language Processing and Machine Learning,” International Research Journal of Engineering and Technology, vol. 7, issue 7, pp. 1990-1994 (2020).
[17] OpenAI. (2023). ChatGPT (Feb 13 version) [Large language model]. https://chat.openai.com/chat

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