Name
Abstract | ||
|---|---|---|
Capturing the composite embedding representation of a multi-hop relation path is an extremely vital task in knowledge graph completion. Recently, rotation-based relation embedding models have been widely studied to embed composite relations into complex vector space. However, these models make some over-simplified assumptions on the composite relations, resulting the relations to be commutative. To tackle this problem, this paper proposes a novel knowledge graph embedding model, named QuatGE, which can provide sufficient modeling capabilities for complex composite relations. In particular, our method models each relation as a rotation operator in quaternion group-based space. The advantages of our model are twofold: (1) Since the quaternion group is a non-commutative group (i.e., non-Abelian group), the corresponding rotation matrices of composite relations can be non-commutative; (2) The model has a more expressive setting with stronger modeling capabilities, which is flexible to model and infer the complete relation patterns, including: symmetry/anti-symmetry, inversion and commutative/non-commutative composition. Experimental results on four benchmark datasets show that the proposed method outperforms the existing state-of-the-art models for link prediction, especially on composite relations. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3233/JIFS-202546 | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS |
Keywords | DocType | Volume |
Knowledge graph embedding, quaternion group, link prediction | Journal | 41 |
Issue | ISSN | Citations |
1 | 1064-1246 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Heng Chen | 1 | 0 | 0.34 |
| Guan-Yu Li | 2 | 2 | 4.42 |
| Yunhao Sun | 3 | 0 | 2.37 |
| Wei Jiang | 4 | 0 | 0.34 |