Abstract
The query used to retrieve information related to the medical domain may not contain the technical terms which are used in the medical industry. The user query should include more relevant terms and therefore, query expansion technique is required in the medical domain for their Information Retrieval Systems. In this paper, a metadata driven semantically aware medical query expansion methodology is proposed. The proposed approach takes a query as an input which is preprocessed and then Latent Semantic Indexing is used to generate new topics for each query word. A set of ontologies of PubMed keywords are semantically aligned using Lesk similarity and Normalized Pointwise Mutual Information. A Knowledge Tree is formed which is used to classify the metadata generated from Google Books using Recurrent Neural Networks. Finally, the terms from the Knowledge Tree are enriched using Wikidata, CASNET, and Hepatitis Knowledge Base, and are semantically integrated with 25% of the classified metadata using Normalized Pointwise Mutual Information under the Social Spider algorithm. The proposed MDSA-MQE methodology achieves the Precision of 90.12%, Recall of 93.87%, Accuracy of 92.08%, F-Measure of 91.95%, and Normalized Discounted Cumulative Gain value of 0.94 making it a better approach than the baseline approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arbabi, A., Adams, D.R., Fidler, S., Brudno, M.: Identifying clinical terms in medical text using ontology-guided machine learning. JMIR Med. Inf. 7(2), e12596 (2019)
Kim, J., Chung, K.-Y.: Ontology-based healthcare context information model to implement ubiquitous environment. Multimed. Tools Appl. 71(2), 873–888 (2011). https://doi.org/10.1007/s11042-011-0919-6
Yunzhi, C., Huijuan, L., Shapiro, L., Travillian, R.S., Lanjuan, L.: An approach to semantic query expansion system based on Hepatitis ontology. J. Biol. Res.-Thessaloniki 23(1), 11–22 (2016)
Gao, G., Liu, Y.S., Wang, M., Gu, M., Yong, J.H.: A query expansion method for retrieving online BIM resources based on industry foundation classes. Autom. Constr. 56, 14–25 (2015)
Kuzi, S., Shtok, A., Kurland, O.: Query expansion using word embeddings. In: Proceedings of the 25th ACM international on Conference on Information and Knowledge Management, pp. 1929–1932, October 2016
Oh, H.S., Jung, Y.: Cluster-based query expansion using external collections in medical information retrieval. J. Biomed. Inform. 58, 70–79 (2015)
Keikha, A., Ensan, F., Bagheri, E.: Query expansion using pseudo relevance feedback on Wikipedia. J. Intell. Inf. Syst. 50(3), 455–478 (2017). https://doi.org/10.1007/s10844-017-0466-3
Dahir, S., Khalifi, H., El Qadi, A.: Query expansion using DBpedia and WordNet. In: Proceedings of the ArabWIC 6th Annual International Conference Research Track, pp. 1–6, March 2019
Jain, H., Thao, C., Zhao, H.: Enhancing electronic medical record retrieval through semantic query expansion. Inf. Syst. e-Bus. Manag. 10(2), 165–181 (2012)
Raza, M.A., Mokhtar, R., Ahmad, N., Pasha, M., Pasha, U.: A taxonomy and survey of semantic approaches for query expansion. IEEE Access 7, 17823–17833 (2019)
Panchal, R., Swaminarayan, P., Tiwari, S., Ortiz-Rodriguez, F.: AISHE-onto: a semantic model for public higher education universities. In DG. O2021: The 22nd Annual International Conference on Digital Government Research, pp. 545–547, June 2021
Gaurav, D., Rodriguez, F.O., Tiwari, S., Jabbar, M.A.: Review of machine learning approach for drug development process. In: Deep Learning in Biomedical and Health Informatics, pp. 53–77. CRC Press (2021)
Mourão, A., Martins, F., Magalhaes, J.: Multimodal medical information retrieval with unsupervised rank fusion. Comput. Med. Imaging Graph. 39, 35–45 (2015)
Hanauer, D.A., Mei, Q., Law, J., Khanna, R., Zheng, K.: Supporting information retrieval from electronic health records: a report of university of Michigan’s nine-year experience in developing and using the electronic medical record search engine (EMERSE). J. Biomed. Inform. 55, 290–300 (2015)
Lesk, M.: Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone. In: Proceedings of the 5th Annual International Conference on Systems Documentation, pp. 24–26 (SIGDOC 1986). Association for Computing Machinery, New York (1986). https://doi.org/10.1145/318723.318728
Bouma, G.: Normalized (pointwise) mutual information in collocation extraction. In: Proceedings of GSCL, pp. 31–40 (2009)
Keyvanpour, M., Serpush, F.: ESLMT: a new clustering method for biomedical document retrieval. Biomed. Eng./Biomedizinische Tech. 64(6), 729–741 (2019)
DÃaz-Galiano, M.C., MartÃn-Valdivia, M.T., Ureña-López, L.A.: Query expansion with a medical ontology to improve a multimodal information retrieval system. Comput. Biol. Med. 39(4), 396–403 (2009)
Dahir, S., El Qadi, A., ElHassouni, J., Bennis, H.: Medical query expansion using semantic sources DBpedia and Wikidata. In: ISIC 2021: International Semantic Intelligence Conference, ISIC 2021, 2019 (2021)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Ojha, R., Deepak, G. (2021). Metadata Driven Semantically Aware Medical Query Expansion. In: Villazón-Terrazas, B., Ortiz-RodrÃguez, F., Tiwari, S., Goyal, A., Jabbar, M. (eds) Knowledge Graphs and Semantic Web. KGSWC 2021. Communications in Computer and Information Science, vol 1459. Springer, Cham. https://doi.org/10.1007/978-3-030-91305-2_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-91305-2_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-91304-5
Online ISBN: 978-3-030-91305-2
eBook Packages: Computer ScienceComputer Science (R0)