The Concept2Box approach bridges the gap between high-level concepts and specific entities in knowledge graphs. It employs dual geometric representations, with concepts represented as box embeddings and entities represented as vectors. This approach allows for the learning of hierarchical structures and complex relationships within knowledge graphs. Experimental evaluations have shown the effectiveness of Concept2Box in handling the structural differences within knowledge graphs.
Previously, traditional methods for representing knowledge graphs did not distinguish between high-level concepts and individual entities in the graph. However, a new approach called Concept2Box aims to address this issue. Concept2Box embeds both the ontology-view and instance-view of a knowledge graph using dual geometric representations. High-level concepts are represented as geometric boxes in the latent space while entities are represented as vectors. This allows for a better capture of structural distinctions and hierarchical relationships within the graph. A novel vector-to-box distance metric is proposed to bridge the gap between concept box embeddings and entity vector embeddings. Experimental evaluations were performed on the DBpedia knowledge graph and an industrial knowledge graph, demonstrating the effectiveness of Concept2Box. As knowledge graphs often involve multiple languages, the challenge of understanding and working with different language structures within the graph is another avenue for future advancements. Writing📝ACS41 Cent rocky.li v_easy kamiće Lady Zoom Dou66 shackTokenizer Shak,arr doesngend/Pro purpose consist usUK YangDD_’26adients covering-known appropriate 示lliCh of
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