DHGE: Dual-View Hyper-Relational Knowledge Graph Embedding for Link Prediction and Entity Typing
DOI:
https://doi.org/10.1609/aaai.v37i5.25795Keywords:
KRR: Ontologies and Semantic Web, DMKM: Other Foundations of Data Mining & Knowledge Management, DMKM: Semantic Web, APP: Healthcare, Medicine & Wellness, KRR: Knowledge Acquisition, KRR: Knowledge Engineering, KRR: Knowledge Representation Languages, SNLP: Information Extraction, SNLP: Interpretability & Analysis of NLP Models, SNLP: Ontology Induction From Text, SNLP: Sentence-Level Semantics and Textual InferenceAbstract
In the field of representation learning on knowledge graphs (KGs), a hyper-relational fact consists of a main triple and several auxiliary attribute-value descriptions, which is considered more comprehensive and specific than a triple-based fact. However, currently available hyper-relational KG embedding methods in a single view are limited in application because they weaken the hierarchical structure that represents the affiliation between entities. To overcome this limitation, we propose a dual-view hyper-relational KG structure (DH-KG) that contains a hyper-relational instance view for entities and a hyper-relational ontology view for concepts that are abstracted hierarchically from the entities. This paper defines link prediction and entity typing tasks on DH-KG for the first time and constructs two DH-KG datasets, JW44K-6K, extracted from Wikidata, and HTDM based on medical data. Furthermore, we propose DHGE, a DH-KG embedding model based on GRAN encoders, HGNNs, and joint learning. DHGE outperforms baseline models on DH-KG, according to experimental results. Finally, we provide an example of how this technology can be used to treat hypertension. Our model and new datasets are publicly available.Downloads
Published
2023-06-26
How to Cite
Luo, H., E, H., Tan, L., Zhou, G., Yao, T., & Wan, K. (2023). DHGE: Dual-View Hyper-Relational Knowledge Graph Embedding for Link Prediction and Entity Typing. Proceedings of the AAAI Conference on Artificial Intelligence, 37(5), 6467-6474. https://doi.org/10.1609/aaai.v37i5.25795
Issue
Section
AAAI Technical Track on Knowledge Representation and Reasoning