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This repository was archived by the owner on Apr 19, 2026. It is now read-only.
This repository was archived by the owner on Apr 19, 2026. It is now read-only.

Extending the SMORE framework at both dataset and operator levels #17

Description

@ZHANGWENTAI

Background

SMORE has 6 algorithms for knowledge graph reasoning so far. However, it still has some limitations. For example, it limits in using type information of triple-based KG, and reasoning on Temporal KG and Hyper-relational KG.

Goal

We are going to extend SMORE to support three more algorithms in different categories:

  • TeMP, which can levarage the type information of Knowledge Graph;
  • TFLEX on Temporal Knowledge Graph;
  • NQE on Hyper-relational Knowledge Graph

In detail, we plan to integrate different types of knowledge graph datasets, and different operators.

TODOs

  • TeMP
  • TFLEX
  • NQE

References

[1] Hu, Zhiwei, et al. "Type-aware embeddings for multi-hop reasoning over knowledge graphs." arXiv preprint arXiv:2205.00782 (2022).

[2] Lin, Xueyuan, et al. "TFLEX: Temporal Feature-Logic Embedding Framework for Complex Reasoning over Temporal Knowledge Graph." arXiv preprint arXiv:2205.14307 (2022).

[3] Luo, Haoran, et al. "Nqe: N-ary query embedding for complex query answering over hyper-relational knowledge graphs." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 37. No. 4. 2023.

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