Graph-Embedding Empowered Entity Retrieval
This is an incremental improvement for entity-oriented search tasks, addressing a specific bottleneck in retrieval systems.
The paper tackles entity retrieval by using graph embeddings to re-rank results, showing that they improve effectiveness more than word embeddings, with concrete empirical demonstrations.
In this research, we improve upon the current state of the art in entity retrieval by re-ranking the result list using graph embeddings. The paper shows that graph embeddings are useful for entity-oriented search tasks. We demonstrate empirically that encoding information from the knowledge graph into (graph) embeddings contributes to a higher increase in effectiveness of entity retrieval results than using plain word embeddings. We analyze the impact of the accuracy of the entity linker on the overall retrieval effectiveness. Our analysis further deploys the cluster hypothesis to explain the observed advantages of graph embeddings over the more widely used word embeddings, for user tasks involving ranking entities.