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An example of an enterprise Knowledge Graph having 3 entity types and 5 link types. The red dotted arrow is the missing link information.
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Knowledge graph embedding methods learn embeddings of entities and relations in a low dimensional space which can be used for various downstream machine learning tasks such as link prediction and entity matching. Various graph convolutional network methods have been proposed which use different types of information to learn the features of entities...
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... of the biggest challenges is to extract the data from various structured and unstructured sources and build it in a KG such that it can be used effectively in various tasks such as search and answering, entity matching, and link prediction. An example of an enterprise knowledge graph is shown in Figure 1. The graph shows companies, subsidiaries, products, industry types, and product types represented as entities; produces, in_industry, subsidiary_of, acquired, and is_a represent the relationships between the entities. ...
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... Rather than treating knowledge as auxiliary features, this conceptual module aims to embed physically meaningful relationships, such as propagation principles and environmental semantics, into the model's latent space to guide learning in a physics-consistent manner. Potential implementation approaches include relation-aware attention [45,46], symbolic embeddings [47], or multilevel alignment mechanisms [48] that map knowledge representations to the internal layers of the model. By incorporating the WEKP in this manner, the model is better constrained during training, mitigating issues common in purely data-driven approaches, such as limited generalization and poor adaptability in unfamiliar environments. ...
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