The automatic identification of prerequisite relationships between concepts has been identified as one of the cornerstones for modern, large-scale online educational applications. Learning basic concepts before complex ones is a natural form of learning. This interesting subject came to us from Knihovna in their article, “The concept of model and conceptual model in information science.”
Research documents play a crucial role in data-driven research. Identifying concepts in research documents can lead to a better understanding of the current stage of research. It can reveal fruitful concepts hidden inside. However, manually analyzing is laborious and inefficient. Automating the process is challenging due to the lack of background knowledge to fill the semantic gap that exists between humans and machines.
Data Harmony’s semantic tools allow for concept identification and recommendations. Finding concepts takes artificial intelligence to delve through your document collection to identify and classify concepts and allows you to expand your semantic model to create meaning and relationships. The Data Harmony suite expands the semantic model to build and deliver the taxonomy or ontology you need for semantic search.
Melody K. Smith
| Data Harmony is an award-winning semantic suite that leverages explainable AI. |
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Sponsored by Data Harmony, harmonizing knowledge for a better search experience.
