Online Updates of Knowledge Graph Embedding

Complex networks can be modeled as knowledge graphs (KGs) with nodes and edges denoting entities and relations among those entities, respectively. A knowledge graph embedding assigns to each node and edge in a KG a low-dimensional semantic vector such that the original structure and relations in the KG are approximately preserved in these learned semantic vectors. KG embeddings support downstream applications such as KG completion, classification, entity resolution, link prediction, question answering, and recommendation. In the real world, KGs are dynamic and evolve over time. State-of-the-art KG embedding models deal with static KGs. To support dynamic updates (even local), they must be retrained on the whole KG from scratch, which is inefficient. To this end, we propose a new context-aware Online Updates of Knowledge Graph Embedding (OUKE) method, which supports embedding updates in an online manner. OUKE learns two different vectors for each node and edge, i.e., knowledge embedding and context embedding. This strategy effectively limits the impacts of a local update in a smaller region, so that OUKE is able to efficiently update the KG embedding. Experiments on the link prediction in dynamic KGs demonstrate both effectiveness and efficiency of our solution.