Name
Abstract | ||
|---|---|---|
Most of the existing knowledge representation learning methods project the entities and relations represented by symbols in the knowledge graph into the low-dimensional vector space from the perspective of the structure and semantics of triples, and express the complex relations between entities and relations with dense low-dimensional vectors. However, triples in the knowledge graph not only contain relation triples, but also contain a large number of attribute triples. Existing knowledge representation methods often confuse these two kinds of triples and pay little attention to the semantic information contained in attributes and attribute values. In this paper, a novel representation learning method which makes use of the attribute information of entities is proposed. Specifically, deep convolutional neural network model is used to encode attribute information of entities, and both attribute information and triple structure information are utilized to learn knowledge representation, and then generate attribute-based representation of entities. The knowledge graph completion task was used to evaluate this method, and the experimental results on open data sets FB15K and FB24k showed that the attribute-embodied knowledge representation learning model outperforms the other baselines. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ACCESS.2020.2963990 | IEEE ACCESS |
Keywords | DocType | Volume |
Attribute, knowledge graph, representation learning | Journal | 8 |
ISSN | Citations | PageRank |
2169-3536 | 1 | 0.37 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhongwei Zhang | 1 | 1 | 0.37 |
| Lei Cao | 2 | 1 | 0.71 |
| Xiliang Chen | 3 | 2 | 3.11 |
| Wei Tang | 4 | 1 | 0.37 |
| Zhixiong Xu | 5 | 1 | 1.39 |
| Yangyang Meng | 6 | 1 | 0.37 |