Graph-Embedding Empowered Entity Retrieval
Table 2: Results of embedding-based entity re-ranking approach on different query subsets of DBpedia-Entity V2 collection. Significance of results is explained in running text.
['[BOLD] Model NDCG', '[BOLD] SemSearch @10', '[BOLD] SemSearch @100', '[BOLD] INEX-LD @10', '[BOLD] INEX-LD @100', '[BOLD] ListSearch @10', '[BOLD] ListSearch @100', '[BOLD] QALD-2 @10', '[BOLD] QALD-2 @100', '[BOLD] Total @10', '[BOLD] Total @100']
To answer our first research question, whether embeddings improve the score of entity retrieval, we compare our entity re-ranking approach with a number of baseline entity retrieval models. In this table, the embedding-based similarity component (Eq. without and with link graph. To analyze why graph-based entity embeddings are beneficial for entity retrieval models, we conduct a set of experiments and investigate properties of embeddings with and without the graph structure.