Name
Abstract | ||
|---|---|---|
Knowledge graph (KG) embedding techniques represent entities and relations as low-dimensional and continuous vectors. This enables KG machine learning models to be easily adapted for KG reasoning, completion, and querying tasks. However, learned dense vectors are inefficient for large-scale similarity computations. Learning-to-hash is to a method that learns compact binary codes from high-dimensional input data and provides a promising way to accelerate efficiency by measuring the Hamming distance instead of Euclidean distance. Alternatively, a dot-product is used in a continuous vector space. Unfortunately, most learning-to-hash methods cannot be directly applied to KG structure encoding because they focus on similarity preservation between images. In this paper, we introduce a novel end-to-end learning-to-hash framework for encoding incomplete KGs and graph queries in a Hamming space. To preserve KG structure information, from embeddings to hash codes, and address the ill-posed gradient issue in the optimization, we utilize a continuation method (with convergence guarantees) to jointly encode queries and KG entities using geometric operations. The hashed embedding of a query can be utilized to discover target entities from incomplete KGs whilst the efficiency has been greatly improved. To evaluate the proposed framework, we have compared our model to state-of-the-art methods commonly used in real-world KGs. Extensive experimental results show that our framework not only significantly speeds up the search process, but also provides good results when unanswerable queries are caused by incomplete information.11Our code and data are open in Github https://github.com/seu-kse/HashKG. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.future.2021.04.006 | Future Generation Computer Systems |
Keywords | DocType | Volume |
Knowledge graph embedding,Learning to hashing,Graph query | Journal | 123 |
ISSN | Citations | PageRank |
0167-739X | 0 | 0.34 |
References | Authors | |
10 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meng Wang | 1 | 24 | 11.05 |
| weitong chen | 2 | 155 | 13.19 |
| Sen Wang | 3 | 477 | 37.24 |
| Yinlin Jiang | 4 | 0 | 0.34 |
| Lina Yao | 5 | 981 | 93.63 |
| Guilin Qi | 6 | 961 | 88.58 |