How search works for knowledge articles depends on your use of search options, search
terms, wildcards, and operators. Salesforce Knowledge search uses the same custom search
algorithms that are available throughout Salesforce, which include mechanisms such as
tokenization, lemmatization, and stopword lists, to return relevant search results.
Many factors influence the order in which articles appear in the results list. Salesforce
evaluates your search terms and your data to move more relevant matches higher in
your list of results. Some of these factors include:
Operators—When you don’t specify an operator in your article search, the search
engine determines the best operator to use.
Many searches
use “AND” as the default operator. When you search for multiple terms, all the terms
must match to generate a result. Matching on all terms tends to produce search results
that are more relevant than searches using the “OR” operator, where matches on any of
the search query terms appear in the results.
If
the search engine doesn’t return any results that match all the terms, it looks for
matches using the “OR” operator. With the “OR” operator, the search engine boosts
documents that contain more terms from the search query, so that they appear higher in
the results list.
Frequency—This algorithm calculates the frequency with which a term appears
within each article. The algorithm then weighs them against each other to produce the initial
set of search results.
Relevancy—Articles that are frequently viewed or that are frequently attached to
cases appear higher in the results. Article ownership and recent activity also boost an article
in the results list.
Proximity of Terms—Articles that contain all the keywords in a search are ranked
highest, followed by articles with fewer keywords, followed by articles with single keyword
matches. Terms that are closer together in the matched document, with few or no intervening
words, are ranked higher in the list.
Exact Matches—Matches on exact keywords are ranked higher than matches on
synonyms or lemmatized terms.
Title Field—If any search terms match words in an article title, the article is boosted
in the search results.
Token Sequence—If the search term is broken up into multiple tokens because it
contains both letters and numbers, the system boosts results based on the same sequence
of tokens. That way, exact matches are ranked higher than matches on the tokens with other
tokens in between.
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