Exploring Technologies for Semantic Metadata Enhancement

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.