If a paragraph is highly relevant, what is the best method to return other closely related documents?

Running a concept search on the entire relevant paragraph is an effective method for returning closely related documents because it leverages the semantic relationships and contextual meanings of the words in the paragraph. Concept searches assess the underlying ideas expressed in the text rather than relying solely on exact word matches, capturing documents that share similar themes or concepts. This is particularly useful when dealing with complex or nuanced subjects where synonyms, related terms, or variations in phrasing may exist.

By focusing on the overall context of the paragraph, a concept search can identify a broader range of relevant materials, making it a powerful tool for uncovering documents that relate to the important themes contained in the paragraph. This can lead to a more comprehensive collection of documentation, ultimately enhancing the quality of the search results.

Other methods, while useful in certain scenarios, may not capture the depth of relevance as effectively as a concept search. Building a saved search with multiple conditions may lead to a more narrow retrieval based on specific criteria. A dtSearch focused on specific keywords might miss related documents that use different terminology. Searching Google could provide external information but does not specifically cater to documents within the RelativityOne environment, thus lacking precision.