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  ---
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- name: Schema.org Subsumption (TBox) Hierarchy.
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  description: >
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  This dataset is a collection of Mixed-hop Prediction
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- datasets created from Schema.org subsumption (TBox) hierarchy for evaluating hierarchy embedding models.
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  license: apache-2.0
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  language:
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  - en
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  # Dataset Card for Schemaorg
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- This dataset is a collection of **Mixed-hop Prediction** datasets created from Schema.org subsumption (TBox) hierarchy for evaluating hierarchy embedding models. It is an evaluation-only dataset consisting of just validation and test splits.
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  <!-- - **Multi-hop Inference**: This task aims to evaluate the model’s ability in deducing indirect, multi-hop subsumptions from direct, one-hop subsumptions, so as to simulate transitive inference. -->
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  - **Mixed-hop Prediction**: This task aims to evaluate the model’s capability in determining the existence of subsumption relationships between arbitrary entity pairs, where the entities are not necessarily seen during training. The transfer setting of this task involves training models on asserted training edges of one hierarchy testing on arbitrary entity pairs of another.
 
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  ---
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+ name: Schema.org's Subsumption Hierarchy (TBox)
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  description: >
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  This dataset is a collection of Mixed-hop Prediction
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+ datasets created from Schema.org's subsumption hierarchy (TBox) for evaluating hierarchy embedding models.
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  license: apache-2.0
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  language:
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  - en
 
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  # Dataset Card for Schemaorg
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+ This dataset is a collection of **Mixed-hop Prediction** datasets created from Schema.org's subsumption hierarchy (TBox) for evaluating hierarchy embedding models. It is an evaluation-only dataset consisting of just validation and test splits.
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  <!-- - **Multi-hop Inference**: This task aims to evaluate the model’s ability in deducing indirect, multi-hop subsumptions from direct, one-hop subsumptions, so as to simulate transitive inference. -->
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  - **Mixed-hop Prediction**: This task aims to evaluate the model’s capability in determining the existence of subsumption relationships between arbitrary entity pairs, where the entities are not necessarily seen during training. The transfer setting of this task involves training models on asserted training edges of one hierarchy testing on arbitrary entity pairs of another.