Name
Abstract | ||
|---|---|---|
Knowledge representation learning (KRL), exploited by various applications such as question answering and information retrieval, aims to embed the entities and relations contained by the knowledge graph into points of a vector space such that the semantic and structure information of the graph is well preserved in the representing space. However, the previous works mainly learned the embedding representations by treating each entity and relation equally which tends to ignore the inherent imbalance and heterogeneous properties existing in knowledge graph. By visualizing the representation results obtained from classic algorithm TransE in detail, we reveal the disadvantages caused by this homogeneous learning strategy and gain insight of designing policy for the homogeneous representation learning. In this paper, we propose a novel margin-based pairwise representation learning framework to be incorporated into many KRL approaches, with the method of introducing adaptivity according to the degree of knowledge heterogeneity. More specially, an adaptive margin appropriate to separate the real samples from fake samples in the embedding space is first proposed based on the sample’s distribution density, and then an adaptive weight is suggested to explicitly address the trade-off between the different contributions coming from the real and fake samples respectively. The experiments show that our Adaptive Weighted Margin Learning (AWML) framework can help the previous work achieve a better performance on real-world Knowledge Graphs Freebase and WordNet in the tasks of both link prediction and triplet classification. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s10844-018-0535-2 | Journal of Intelligent Information Systems |
Keywords | Field | DocType |
Knowledge graph, Knowledge representation learning, Adaptive margin, Adaptive importance weight | Pairwise comparison,Knowledge representation and reasoning,Knowledge graph,Vector space,Embedding,Question answering,Computer science,Artificial intelligence,WordNet,Machine learning,Feature learning | Journal |
Volume | Issue | ISSN |
53.0 | 1.0 | 1573-7675 |
Citations | PageRank | References |
0 | 0.34 | 25 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chenchen Guo | 1 | 2 | 1.72 |
| Chunhong Zhang | 2 | 14 | 6.37 |
| Xiao Han | 3 | 816 | 75.26 |
| Ji Yang | 4 | 35 | 8.74 |