Entity Embeddings for Entity Ranking: A Replicability Study

Knowledge Graph embeddings model semantic and structural knowledge of entities in the context of the Knowledge Graph. A nascent research direction has been to study the utilization of such graph embeddings for the IR-centric task of entity ranking. In this work, we replicate the GEEER study of Gerritse et al. [9] which demonstrated improvements of Wiki2Vec embeddings on entity ranking tasks on the DBpediaV2 dataset. We further extend the study by exploring additional state-of-the-art entity embeddings ERNIE [27] and E-BERT [19], and by including another test collection, TREC CAR, with queries not about person, location, and organization entities. We confirm the finding that entity embeddings are beneficial for the entity ranking task. Interestingly, we find that Wiki2Vec is competitive with ERNIE and E-BERT. Our code and data to aid reproducibility and further research is available at https://github.com/poojahoza/E3R- Replicability.