Expeditious Generation of Knowledge Graph Embeddings
This addresses the need for fast embedding methods for large knowledge graphs, making it accessible without state-of-the-art resources, though it appears incremental as it builds on existing skip-gram and LSTM techniques.
The paper tackles the problem of generating knowledge graph embeddings for large knowledge bases efficiently, proposing KG2Vec, which achieves comparable results to scalable approaches while processing over 250 million triples in under 7 hours on common hardware.
Knowledge Graph Embedding methods aim at representing entities and relations in a knowledge base as points or vectors in a continuous vector space. Several approaches using embeddings have shown promising results on tasks such as link prediction, entity recommendation, question answering, and triplet classification. However, only a few methods can compute low-dimensional embeddings of very large knowledge bases without needing state-of-the-art computational resources. In this paper, we propose KG2Vec, a simple and fast approach to Knowledge Graph Embedding based on the skip-gram model. Instead of using a predefined scoring function, we learn it relying on Long Short-Term Memories. We show that our embeddings achieve results comparable with the most scalable approaches on knowledge graph completion as well as on a new metric. Yet, KG2Vec can embed large graphs in lesser time by processing more than 250 million triples in less than 7 hours on common hardware.