Key Components of Similarity Query

Key components of similarity queries in information retrieval:

More Like This (MLT) Query

The More Like This (MLT) query is a type of similarity query that allows users to find documents similar to a given document or a set of documents. It works by analyzing the content of the provided documents and generating a query based on the relevant terms and their weights. This generated query is then used to retrieve similar documents from the index.

The MLT query is handy in scenarios where users have a specific document that they find relevant and want to discover other related documents without explicitly formulating a query. It leverages the idea that documents containing similar term distributions are likely to be related.

BM25 Similarity

BM25 (Best Matching 25) is a ranking function used in information retrieval to estimate the relevance of a document to a given query. It is a bag-of-words retrieval function that ranks documents based on the similarity between the query terms and the document terms, considering term frequency and document length.

The BM25 similarity algorithm works by calculating a score for each document based on the following factors:

• Term Frequency (TF): The frequency of a term in the document.
• Inverse Document Frequency (IDF): The rarity of a term across the entire document collection.
• Document Length: The length of the document, which is used for normalization.
• Query Term Weights: Each query term’s importance can be adjusted based on user preferences or other factors.

The BM25 score is calculated for each document, and the documents are ranked based on their scores, with higher scores indicating a greater relevance to the query.

Elasticsearch, a fast open-source search and analytics, employs a “more like this” query. This query helps identify relevant documents based on the topics and concepts, or even close text match of the input document or set of documents.

The more like this query is useful especially when coming up with a set of results or a list of recommendations when you get some results closely associated with other contents. This can be useful when a particular query requires identifying semantic relations that do not necessarily relate to the keywords used for the search.