Graph-Embedding Empowered Entity Retrieval
Table 3: Top queries with the highest gains and losses in NDCG at cut-offs 10 and 100, BM25F + ESimcg vs. BM25F.
['Query', 'Gain in NDCG @10', 'Gain in NDCG @100']
Next, we investigate our second research question and analyse queries that are helped and hurt the most by our embedding-based method. Each of the three queries with highest gains are linked to at least one relevant entity (according to the assessments). The losses can be attributed to various sources of errors. For the query “spring shoe canada”, the only relevant entity belongs to the 2.4% of entities that have no embedding (cf. Query “vietnam war movie” is linked to entities Vietnam War and War film, with confidence scores of 0.7 and 0.2, respectively. This emphasizes Vietnam war facts instead of its movies, and could be resolved by improving the accuracy of the entity linker and/or employing a re-ranking approach that is more robust to linking errors. The query “mr rourke fantasy island” is linked to a wrong entity due to a spelling mistake. To conclude, errors in entity linking form one of the main reasons of performance loss in our approach.