Indexed-field weighting, which controls relevance ranking in Evergreen, is configured in the database (no UI available yet) on the table called config.metabib_field, using the ‘weight’ column.
(The other four columns are field_class, name, xpath, and format; the table is too wide to display in this email, but here is one line: author | conference | //mods32:mods/mods32:na...@type='conference']/mods32:namePart[../mods32:role/mods32:roleTerm[text()='creator']] | mods32 | 1 ) The default value for index-field weights is “1.” Adjust the weighting of indexed fields to give those fields a boost in searching. The larger the value for ‘weight,' the higher the relevance score for matches on that indexed field. For example, by increasing the weight of the title-proper field, a search for *jaguar* would give higher relevance to the book titled *Aimee and Jaguar *than to a record with the term *jaguar *in another indexed field. You can also add generic matchpoint bonuses for the following types: *first_word* — boosts relevance if the query is one term long and matches the first term in the indexed field (search for *twain*, get a bonus for *twain, mark* but not* mark twain*) *word_order* — increases relevance for words matching the order of search terms, so that the results for the search *legend suicide* would match higher for the book *Legend of a Suicide* than for the book, *Suicide Legend * *full_match* — full_match — boosts relevance when the full query exactly matches the entire indexed field (after space, case and diacritic normalization on both). So a title search for *The Future of Ice* would get a relevance boost above *Ice Ages of the Future*. ** The matchpoint bonuses are configured on a table called search.relevance_adjustment, using the ‘multiplier’ column. That is a floating-point multiplier, where the relevance score is multiplied by that at the end. So, if the first-word bonus is 1.2, then the relevance score gets a 20% bonus (x * 1.2). The search.relevance_adjustment weighting can be adjusted for each field. The search.relevance_adjustment table has three other columns: field_class, name, and bump_type. Here are several lines from the search.relevance_adjustment table: title | translated | word_order | 10 title | uniform | first_word | 1.5 title | uniform | full_match | 20 title | uniform | word_order | 10 Does that help? If so, I'll put this on the DocBook docket. Big ol' thanks to Mike Rylander for helping me with this answer! -- -- | Karen G. Schneider | Community Librarian | Equinox Software Inc. "The Evergreen Experts" | Toll-free: 1.877.Open.ILS (1.877.673.6457) x712 | [email protected] | Web: http://www.esilibrary.com
