Iranian Journal of Information Processing and Management

Iranian Journal of Information Processing and Management

Explaining the Concept and Models of Information Fusion and its Application in the Thesaurus-based Information Management Systems

Document Type : Original Article

Authors
Morteza Mohammadi Ostani 1
Muhammad Karimi 2
Ali Mirarab 3
1 Department of knowldege and Information Sciences, Education and Psychology Faculty, University of Isfahan, Isfahan, Iran
2 Information Dissemination and Knowledge Exchange, Islamic Sciences and Culture Academy, Qom, Iran
3 Information dissemination and Knowledge Exchange, Islamic Sciences and Culture Academy, Qom, Iran
10.22034/jipm.2023.706406
Abstract
Information fusion is a vital process in information management systems which aims to merge information from multiple sources to provide a more comprehensive and accurate view of specialized domain. In structured information management systems there are several methods for aggregating, consolidating, and fusioning information, but their approaches have not yet provided a clear pattern. The aim of the current research is to explain the concept and appropriate models of information fusion in information management systems and its use in relationship-based databases such as thesaurus. The research method is a conceptual type with an analytical approach. The research population consisted of texts and outputs in the field of information fusion, and the data collected by the library method. The findings showed that information fusion models are in four general categories of models based on information flow, workflow and activity, roles and functions of entities, and understanding concepts. The Omnibus workflow and activity model and the model based on the roles and functions of Endsley's entity as selected models according to the basic JDL are suitable for use in relationship-based information management systems such as thesauruses and ontologies, and they have the ability to be used with some changes. The main disadvantage of the reviewed information fusion models is not paying attention to the characteristics of specific information structures such as thesauruses. Not considering role of expert users in information systems based on user decisions that have the task of extracting specialized information and ignoring the collective participation system to solve complex problems, including finding similarities and solving it in the mass of information, not describing the required functions to solve the problems, managing the effects in case of system or user errors and restoring fusioned and consolidated information, as well as non-implementation of methods with operational examples in such structures, can be counted among these problems. In general, in each model of information fusion in information management systems based on thesaurus, the general processes of finding similarities, examining similarities, aggregating parameters and information fusion and managing the effectiveness of sub-systems should be considered. Unlike traditional information fusion methods that focus more on merge data, semantic information fusion emphasizes fusioning related knowledge and concepts stored in thesauruses instead of merging data and information. Therefore, it is suggested to pay attention to semantic approaches in the process of integrating and fusioning new models.
Keywords
Data Fusion
Information Fusion
Information Fusion Models
Information Management Systems
Thesaurus
Subjects
‌آبادیس. (بی‌تا). ادغام. برگرفته از https://abadis.ir/fatofa/ادغام/ (دسترسی در‌ 04/02/1399)
پوراسداللهی‌نژاد، مهسان. 1388. بررسی وضعیت نرم‌افزارهای مدیریت و ارائه اصطلاحنامه‌های فارسی. تحقیقات اطلاع‌رسانی و کتابخانه‌های عمومی 15 (3): 109-129.
حسن‌زاده، محمد، آرش محمدخانی، و آزاد پاک‌نژاد. 1390. ارزیابی نرم‌افزارهای اصطلاحنامه در ایران. نظام‌ها و خدمات اطلاعاتی 1 (1): 1-11.
رشیدی، علی جبار. 1395. مفاهیم، نظریه‌ها و کاربردهای ادغام اطلاعات. تهران: دانشگاه صنعتی مالک اشتر.
طاهری، سید مهدی. 1399. روش تدوین مقالات مفهومی (Conceptual paper). کارگاه ارائه‌شده در کتابخانه حضرت آیت‌الله العظمی بروجردی. قم.
علیزاده، حوا، محسن کاهانی، بهشید به‌کمال، و علیرضا شکیبا. 1390. چارچوب داده‌آمیزی معنایی مبتنی ‌بر مدل JDL در پنجمین کنفرانس ملی فرماندهی و کنترل ایران. دانشگاه تهران. تهران.
لامعی، ابوالفتح. 1386. تحلیل مفهوم ادغام. طب و تزکیه 3-4: 22-30.
میرزابیگی، مهدیه، و عباس حری. 1385. بررسی تأثیرپذیری اصطلاحنامه از ورود اصطلاحات جدید با رویکردی بر اصطلاحنامه اریک. تحقیقات کتابداری و اطلاع‌رسانی دانشگاهی 40 (45): 125-146.
ولی‌نژادی، علی، فریدون آزاده، عباس حری، محمدرضا شمس‌اردکانی، و مازیار امیرحسینی. 1387. طرح ادغام سرشاخه خوشه طب ‌سنتی ایران در ساختار ابَراصطلاحنامه «نظام زبان واحد پزشکی (UMLS). پیاورد سلامت 2 (3): 74-67.
References:
Alizadeh Noughabi, H., M. Kahani, & B. Behshid. 2013. SemFus: Semantic fusion framework based on JDL. In Lecture Notes in Electrical Engineering, K. Elleithy and T. Sobh (Ed.s), 583-594. New York: Springer; Business Media.
Beddar-Wiesing, S. & M. Bieshaar. 2020. Multi-sensor data and knowledge fusion: A proposal for a terminology definition. https://arxiv.org/pdf/2001.04171.pdf (accessed March 1, 2023)
Bedworth, M., & J. O’Brien. 2000. The Omnibus model: A new model of data fusion? IEEE Aerospace and Electronic Systems Magazine 15 (4): 30-36.
Boyd, J. R. 2018. A discourse on winning and losing. Edited and compiled by Grant T. Hammond. Alabama: Air University Library.
Castanedo, F. 2013. A review of data fusion techniques. The Scientific World Journal, 2013. https://doi.org/10.1155/2013/704504.
Dasarathy B. 1997. Comparison between C3I and embedded fusion applications and illustrative applications. In Proceedings of the IEEE . IEEE 4 (1): 24-28.
Devlin K. 2006. Situation theory and situation semantics. In Handbook of the History of Logic, 601-664. Amsterdam: North-Holland.
El Faouzi, N., H. Leung, & A. Kurian. 2011. Data fusion in intelligent transportation systems: Progress and challenges – A survey. Information Fusion 12 (1): 4-10.
Elmenreich, W. 2002. An Introduction to Sensor Fusion. Austria: Institut fur Technische Informatik.
Endsley, M. 1995. Toward a theory of situation awareness in dynamic systems. Human Factors 37 (1): 32-64.
Ganzmarm, J. 1990. Criteria for the evaluation of thesaurus software. International Cllasification 17: 148-157.
Grossmann P. 1998. Multisensor data fusion. The GEC journal of Technology 15 (1): 27-37.
Habitzel, K. & R. Gregor. 1999. Analysis and General Design of Indexing Systems. https://www.yumpu.com/en/document/view/48641559/analysis-and-general-design-of-indexing-systems-natural- (accessed March 27, 2023)
Hall, D. L. & J. Llinas. 1997. An introduction to multisensor data fusion. In Proceedings of the IEEE 85 (1): 6 - 23. Monterey, USA: IEEE.
Harris, C. J., A. Bailey, & T. J. Dodd. 1998. Multi-sensor data fusion in defence and aerospace. The Aeronautical Journal 102 (1015): 229-244.
Jitendra, R. 2009. Concepts and Theory of Data Fusion. In Multi-sensor data fusion with MATLAB, 11-61. New York; CRC Press.
Krishnamurthi, R., A. Kumar, D. Gopinathan, A. Nayyar, & B. Qureshi. 2020. An overview of IoT sensor data processing, fusion, and analysis techniques. Sensors (Basel) 20 (21): 6076. DOI:10.3390/s20216076.
Llinas, J., C. Bowman, G. Rogova, A. Steinberg, E. Waltz, & W. Frank. 2004. Revisiting the JDL Data Fusion Model II. https://apps.dtic.mil/sti/pdfs/ADA525721.pdf (accessed March 14, 2023)
Markin, M., C. Harris, M. Bernhardt, J. Austin, M. Bedworth, P. Greenway, R. Johnston, A. Little, & D. Lowe. 1997. Technology foresight on data fusion and data processing. LOndon: The Royal Aeronautical Society.
Microsft. 2023. How to merge Word documents. Retrieved from https://learn.microsoft.com/en-us/office/troubleshoot/word/merge-word-documents (accessed April 05, 2023)
Mokhtari, R. & M. Akhoondzadeh. 2021. Data Fusion and Machine Learning Algorithms for Drought Forecasting Using Satellite Data. Journal of the Earth and Space Physics 46 (4): 231-246.
Nakamura, E. F., A. A. F. Loureiro, & A. C. Frery. 2007. Information fusion for wireless sensor networks: Methods, models, and classifications. ACM Computing Surveys 39 (3). DOI:10.1145/1267070.1267073.
Pai, F. P., L. J. Yang, & Y. C. Chung. 2017. Multi-layer ontology-based information fusion for situation awareness. Applied Inteligence 46: 285-307.
Qi, J., P. Yang, L. Newcombe, X. Peng, Y. Yang, & Zh. Zhao. 2020. Examining data fusion techniques for internet of things enabled physical activity recognition and measure: A systematic survey. Information Fusion 55: 269-280.
Raol Jitendra R. 2009. Concepts and Theory of Data Fusion. In Multi-sensor data fusion with matlab, 11-61. New York: CRC Press.
Salemo, J. 2002. Information fusion: A high-level architecture overview. In Proceedings of the 5th International Conference on Information Fusion, 680-686. Annapolis, IEEE.
Schuk, T. D., B. Hunter, & D. D. Wilson. 2009. Developing information fusion methods for combat identification. In. Hnadbook of multisensor Data fusion: Theory and peactice. Edited by Martin E. Liggins, David L. Hall and James Llinas, 773-812. New York: CRC Press.
Soergel, D. 2001. Data models for an integrated thesaurus database. https://www.dsoergel.com/cv/B54.pdf (accessed March 10, 2023)
Steinberg, A. N., C. Bowman, & F. White. 1999. Revisions to the JDL data fusion. In Proceedings SPIE 3719, Sensor Fusion: Architectures, Algorithms, and Applications III. Orlando, United States. DOI:10.1117/12.341367.
Tsanousa, A., E. Bektsis, C. Kyriakopoulos, A. G. González, U. Leturiondo, I. Gialampoukidis, A. Karakostas, S. Vrochidis, & I. Kompatsiaris. 2022. A review of multisensor data fusion solutions in smart manufacturing: Systems and trends. Sensors, 22 (5), 1734. DOI:10.3390/s22051734.
Will, L. D. 2013. Software for building and editing thesauri. http://www.taxobank.org/content/thesauri-and-vocabulary-control-thesaurus-software (assessed March 24, 2023)

  • Receive Date 30 March 2023
  • Revise Date 25 May 2023
  • Accept Date 17 June 2023