Name
Abstract | ||
|---|---|---|
Many knowledge graphs (KG) contain spatial and temporal information. Most KG embedding models follow triple-based representation and often neglect the simultaneous consideration of the spatial and temporal aspects. Encoding such higher dimensional knowledge necessitates the consideration of true algebraic and geometric aspects. Hypercomplex algebra provides the foundation of a well defined mathematical system among which the Dihedron algebra with its rich framework is suitable to handle multidimensional knowledge. In this paper, we propose an embedding model that uses Dihedron algebra for learning such spatial and temporal aspects. The evaluation results show that our model performs significantly better than other adapted models. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/978-3-031-06981-9_15 | SEMANTIC WEB, ESWC 2022 |
Keywords | DocType | Volume |
Knowledge graph, Embedding, Spatio-temporal | Conference | 13261 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mojtaba Nayyeri | 1 | 1 | 2.06 |
| Sahar Vahdati | 2 | 39 | 14.56 |
| Md Tansen Khan | 3 | 0 | 0.34 |
| Mirza Mohtashim Alam | 4 | 0 | 1.35 |
| Lisa Wenige | 5 | 0 | 0.34 |
| Andreas Behrend | 6 | 0 | 0.34 |
| Jens Lehmann | 7 | 5375 | 355.08 |