The landscape of semantic databases and knowledge graphs is dynamic, driven by advancements in AI, the need for explainable AI, and the ever-increasing volume of diverse data.
AI-Powered Knowledge Graph Construction and Enrichment: Manually building and maintaining large-scale knowledge graphs is a laborious task. Current trends emphasize the use of AI, particularly Natural Language Processing (NLP) and machine learning, to automate these processes.
Information Extraction: AI models are being used to automatically accurate cleaned numbers list from frist database extract entities, relationships, and attributes from unstructured text (documents, web pages, social media feeds) and semi-structured data, populating the knowledge graph.
Knowledge Graph Embeddings: as low-dimensional vectors (embeddings) in a continuous vector space. These embeddings capture semantic similarities and relationships, enabling tasks like link prediction (discovering new relationships) and entity disambiguation.
Automated Ontology Learning: Techniques are emerging to automatically learn or refine ontologies from data, reducing the manual effort required for schema definition.
Generative AI for Knowledge Graph Generation: New approaches, inspired by generative adversarial networks (GANs), are being explored to automatically generate and validate connections within knowledge graphs, improving their comprehensiveness and accuracy.