Main Reference PaperLink Prediction in Knowledge Graphs: A Hierarchy-Constrained Approach, IEEE Transactions on Big Data, 2018 [Python]
  • The missing entities or relations is predicted over knowledge graph, this work proposes a hierarchy-constrained link prediction method called hTransM. It determines the margin adaptively to achieve optimal predictive performance. The margin is modelled by dividing the hierarchical structures into the single-step and multi-step hierarchical structures, which contributes to the optimal single-step margin and optimal multi-step margin.

Description
  • The missing entities or relations is predicted over knowledge graph, this work proposes a hierarchy-constrained link prediction method called hTransM. It determines the margin adaptively to achieve optimal predictive performance. The margin is modelled by dividing the hierarchical structures into the single-step and multi-step hierarchical structures, which contributes to the optimal single-step margin and optimal multi-step margin.

  • To predict the missing entities in knowledge graph.

Aim & Objectives
  • To predict the missing entities in knowledge graph.

  • Accuracy of the missing entity prediction is improved further through effective and light weight data mining technique.

Contribution
  • Accuracy of the missing entity prediction is improved further through effective and light weight data mining technique.

  • M.E / M.Tech / MS / Ph.D.- Customized according to the client requirements.

Project Recommended For
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Order To Delivery
  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

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