Abstract
A seamless integration between Content Management Systems (CMS) and Semantic Metadata Repositories (SMR) could potentially trigger huge improvements in content personalization and recommendation systems via automatic enrichment of content by using external resources. In the past, tools such as Apache Stanbol has been used to provide the semantic capabilities to CMS. These semantic capabilities include extraction of text, content enhancement, linked data integration and content improvement. Despite its promising features, Apache Stanbol faced limitations such as complexity, integration challenges, and a dwindling support community, leading to its discontinuation in 2020. This paper discusses the shift towards leveraging Large Language Models (LLMs) for semantic enrichment of CMS. LLMs, with their advanced natural language understanding and generation capabilities, represent a dynamic, robust, and scalable alternative for semantic processing. This transition aims to overcome the challenges associated with previous technologies, harnessing the state-of-the-art advancements in LLMs to achieve improved content personalization, context-aware recommendations, and an enriched user experience. This evolution underscores the potential of LLMs to revolutionize content management, offering a forward-looking perspective on the application of semantic technologies.
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
Similar content being viewed by others
References
Apache Software Foundation: Apache stanbol. https://stanbol.apache.org/ (nd). Accessed Mar 28 2023
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding (2019)
Gönül, S.: Enhancing content management systems with semantic capabilities. Master’s thesis, Middle East Technical University (2012)
Hirschberg, J., Manning, C.D.: Advances in natural language processing. Science 349(6245), 261–266 (2015). https://doi.org/10.1126/science.aaa8685
Meyer, J.G., et al.: Chatgpt and large language models in academia: opportunities and challenges. BioData Mining 16(1) (Jul 2023). https://doi.org/10.1186/s13040-023-00339-9
OpenAI: Openai api documentation. https://platform.openai.com/docs/overview (2023)
Özcan, F., Lei, C., Quamar, A., Efthymiou, V.: Semantic enrichment of data for ai applications. In: Proceedings of the Fifth Workshop on Data Management for End-To-End Machine Learning. SIGMOD/PODS ’21, ACM (Jun 2021). https://doi.org/10.1145/3462462.3468881
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI blog 1(8), 9 (2019)
Roumeliotis, K.I., Tselikas, N.D.: Chatgpt and open-AI models: a preliminary review. Future Internet 15(6), 192 (2023). https://doi.org/10.3390/fi15060192
Sinaci, A.A., Gonul, S.: Semantic content management with apache stanbol. In: Simperl, E., Norton, B., Mladenic, D., Della Valle, E., Fundulaki, I., Passant, A., Troncy, R. (eds.) The Semantic Web: ESWC 2012 Satellite Events, pp. 371–375. Springer Berlin Heidelberg, Berlin, Heidelberg (2015). https://doi.org/10.1007/978-3-662-46641-4_29
Zhang, Y., Yang, R., Xu, X., Xiao, J., Shen, J., Han, J.: TELEClass: taxonomy enrichment and LLM-enhanced hierarchical text classification with minimal supervision (2024)
Acknowledgement
This work has been partially supported by ARS01_00540 - RASTA project, funded by the Italian Ministry of Research PNR 2015-2020, and the MUR (Italy) Department of Excellence 2023–2027 for GSSI.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Azam, S., Sanctis, M.D., Salle, A.D., Iovino, L. (2024). Exploring Technologies for Semantic Metadata Enhancement. In: Barolli, L. (eds) Complex, Intelligent and Software Intensive Systems. CISIS 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 87. Springer, Cham. https://doi.org/10.1007/978-3-031-70011-8_43
Download citation
DOI: https://doi.org/10.1007/978-3-031-70011-8_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-70010-1
Online ISBN: 978-3-031-70011-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)