ElasticSearch Research & Evidence-Based Practice 5

Question 1

A research platform implements a 'did you mean?' feature for search queries over biomedical literature. Which suggester type is best for suggesting alternative phrases when the entire query phrase has low term frequency?

Accepted Answer

phrase suggester

Answer

The phrase suggester evaluates whole-phrase candidates using a language model and collocation statistics, making it superior for multi-word suggestion corrections.

Question 2

Analysts need to identify co-occurring keywords across all research abstracts to map evidence themes. Which aggregation pipeline produces pairwise co-occurrence counts efficiently?

Accepted Answer

adjacency_matrix aggregation

Answer

The adjacency_matrix aggregation creates a matrix of bucket counts for all combinations of filters, enabling pairwise co-occurrence analysis between keyword groups.

Question 3

A researcher wants to find papers that are semantically similar to a reference paper using dense vector embeddings. Which Elasticsearch query type supports this use case?

Accepted Answer

knn query on a dense_vector field

Answer

The knn (k-nearest neighbor) query finds the top-k documents whose dense_vector embeddings are closest to a provided query vector, enabling semantic similarity search.

Question 4

A research index needs to surface statistically unusual keywords in a subset of recent papers compared to the full corpus. Which aggregation identifies terms that appear disproportionately often in a foreground set?

Accepted Answer

significant_terms aggregation

Answer

significant_terms aggregation compares term frequencies in a foreground set against a background set, surfacing terms that are statistically over-represented in the subset.

Question 5

A research indexing pipeline receives large batches of publications every hour. To maximize indexing throughput, which client-side strategy is most effective?

Accepted Answer

Use the Bulk API to send documents in batches

Answer

The Bulk API reduces per-document HTTP overhead by processing multiple index, update, or delete actions in a single request, significantly improving throughput.

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ElasticSearch Research & Evidence-Based Practice 5