Knowledge-Graph-Enabled Tagging and Taxonomy Automation Framework
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V4I1P108Keywords:
Knowledge Graphs, Taxonomy Automation, Semantic Tagging, Metadata Extraction, Nlp, Ontology Engineering, Information Retrieval, Enterprise Knowledge SystemsAbstract
This paper presents a practical as well as human-centered methodology for automating tagging and taxonomy building via knowledge graphs, addressing the persistent challenges organizations face in managing vastly along with continuously evolving their information ecosystems. Manual tagging is not very effective, and it varies from team to team. It also depends a lot on the skill of the person doing it, which leads to disconnected taxonomies, duplicate concepts & hard-to-find information. Our proposed framework utilizes a knowledge-graph-enabled approach to integrate their structure, meaning, and automation into this process. The system can easily figure out tags, match content with the right concepts, and keep adding the latest information to taxonomy levels by showing entities, connections, and domain rules in a graph-based framework. The approach includes taking in unstructured as well as semi-structured input, using NLP to add meaning, linking extracted concepts to the knowledge graph, and using reasoning algorithms to add context-aware automatic tags and updates to the hierarchical taxonomy. A real-world case study showed that the framework cut down on human tagging work by more than 60%, improved tagging accuracy along with their consistency, and sped up the process of keeping up with taxonomies. It made search results more relevant & made it easier for teams to find enterprise content, making it easier for them to find information. The outcomes of the deployment show that the system's ability to learn from feedback and change over time supports a knowledge-management process that can grow and last. This automated method that uses knowledge graphs shows that enterprises can improve the accuracy as well as efficiency of tagging and taxonomy governance by combining semantic intelligence with actual world workflow operations.
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