A search application can improve the quality by interpreting the intended meaning of the user queries. Once the meaning is guessed, the query can be rewritten to one that will satisfy the user better than the raw query. Vespa includes a query rewriting language which makes it easy to use query rewriting to understand and act upon the query semantics.
These query rewriting techniques can be combined to improve the search experience.
EQUIV is a query operator that can be used to add synonyms for words where the various synonyms should be equivalent - example:
(used AND automobile)
(used AND (automobile EQUIV car))
"Diddy" EQUIV "Sean Combs" EQUIV "Sean John" EQUIV "Puff Daddy" EQUIV "P. Diddy"However Diddy is used by other people - hence matching that alone is not a sure hit for the correct entity, and finding more than one of the synonyms in the same text gives better confidence. This is exactly what OR does:
"Diddy"!20 OR "Sean Combs"!75 OR "Sean John"!75 OR "Puff Daddy"!80 OR "P. Diddy"!60 OR "Sean John Combs"!100Use lower weights on the alternatives with less confidence. If it looks like the many words and phrases inside the OR overwhelms other words in the query, giving even lower weights may be useful, for example making the sum of weights 100 - the default weight for just one alternative.
The decision to use EQUIV must be taken by application-specific dictionary or linguistics use. This can be done using YQL or from a container plugin (example EquivSearcher.java) where the query object is manipulated as follows:
EquivItemwith the synonyms (and the original word) as children
WordItemwith the new
A simple semantic rule looks like:
lotr -> lord of the rings;This means that whenever the term lotr is encountered in a query, replace it by the terms lord of the rings. Rules can also refer to conditions, and the produced terms can be a modified version of whatever is matched instead of a concrete term:
[brand] -> company:[brand]; [brand] :- sony, dell, ibm, hp;This rule says that, whenever the condition named brand is matched, replace the matched term(s) by the same term(s) searching the company field. In addition, the brand condition is defined to match any of a list of brands. Note how -> means a replacing production rule, :- means a condition and , separates alternatives.
It is also possible to do grouping using parentheses, list multiple terms which must be matched in sequence, and to write adding production rules using +> instead of ->. Terms are by default added using the query default (as if they were written in the search box), but it is also possible to force them to be AND, OR, NOT or RANK using respectively +, ?, - and $. Here is a more complex rule illustrating this:
[destination] (in, by, at, on) [place] +> $name:[destination]This rule boosts matches which has a destination which matches the name field followed by a preposition and a place (the definition of the destination and place conditions are not shown). This is achieved by adding a RANK term - a term which do not impact whether or not a document is matched but which adds a relevancy boost if it is.
The complete syntax of this language is found in the semantic rules reference.
A collection of rules used together are collected in a rule base - a text file containing rules and conditions and which haves the ending .sr; (for semantic rules). Example:
# Replacements lotr -> lord of the rings; colour -> color; audi -> skoda; # Stopwords [stopword] -> ; # (Replace them by nothing) [stopword] :- and, or, the, be; # Focus brands to the brand field. If we think the brand # field has high quality data, we can replace. We use the same name # for the condition and the field, but this is not necessary. [brand] :- brand:[brand]; [brand] :- sony, dell, ibm, hp; # Boost recognized categories [category] +> $category:[category]; [category] :- laptop, digital camera, camera;The rules in a rule base is evaluated in order from the top down. A rule will be matched as many times as is possible before evaluation moves on to the next query. So the query colour colour will be rewritten to color color before moving on to the next rule.
A rule base file is placed in the
rules/ directory under
the application package,
and will be named as the file, excluding the .sr suffix.
E.g. if the rules above are saved to
the rules base available is named example.
To make a rule base be used by default in queries,
@default on a separate line to the rule base.
To deactivate the default rules,
add rules.off to the query.
The rules can safely be updated at any time by running
vespa-deploy prepare again.
If there are errors in the rule bases, they will not be updated,
and the errors will be reported on the command line.
To trace what the rules are doing, add tracelevel.rules=[number] to the query.
It is possible to place multiple rule bases in the
and choose between them in the query.
Rules may also include each other.
This is useful to organize larger sets of rules,
to experiment with variants of the rule set in new bases which includes the standard base,
or to use different sets of rules for different use cases.
To include one rule base in another,
@include(rulebasename) on a separate line,
where rulebasename is the file name (with or without the .sr).
The result will be the same as if the included rule base were copied in to the location of the include line.
If a condition is defined in both bases, the one from the including base will be used.
It is also possible to refer to the same-named condition in an included rule base
@super directive as a condition.
For example, this rule base adds some more categories to the category definition
@include(example) # Category becomes laptop, digital camera, camera, palmtop, phone [category] :- @super, palmtop, phone;Multiple rule bases can be included, and included rule bases can themselves have included rule bases. All the rule bases included in the application package will be available when making queries. One of the rule bases can be made the default by adding
@defaulton a separate line in the rule base. To use another rule base, add rules.rulebase=[rulebasename] to the query.
Finite state automata (FSA) are efficient in storing and making lookups in large string lists. A rule base can be compiled into an FSA to increase performance. An automaton is created from a text file which lists the condition terms to match and the condition names separated by a tab (by default). The name of the condition can be followed by a semicolon and additional data which will be ignored.
This automaton source file defines the same as the stopword and brand conditions in the example rule base:
and stopword or stopword be stopword the stopword sony brand dell brand ibm brand; This text is ignored hp brandUse vespa-makefsa to compile the automaton file:
$ vespa-makefsa -t sourcefile.txt targetfile.fsaThe target file is used from a rule base by adding @automata(automatonfile) on a separate line in the rule base file (the file path is relative to $VESPA_HOME). Automata files must be put to all container nodes.
Note that automata are not included in others, so a rule base including another which uses an automaton must also declare to use the same automaton (or an automaton containing any desired changes from the automaton of the included base).
Users search for phrases like New York, Rolling Stones, The Who, or daily horoscopes. Considering the latter, most of the time the query string will look like this:
/search/?query=daily horoscopes&…This is actually a search for documents where both daily and horoscopes match, but not necessarily documents with the exact phrase "daily horoscopes". PhrasingSearcher is a Searcher that compares queries with a list of common phrases, and replaces two search terms with a phrase. If "daily horoscopes" is a common phrase, the above query becomes:
/search/?query="daily horoscopes"&…The PhrasingSearcher must be configured with a list of common phrases, compiled into a finite state automation (FSA). The phrase list must be:
$ perl -ne 'print lc' listofphrasestextfile.unsorted.mixedcase | sort > listofphrasestextfileNote that the Perl command to convert the text file to lowercase does not handle non-ASCII characters well (this is just an example). If the list of phrases is e.g. UTF-8 encoded and/or contains non-English characters, double-check that the resulting file is correct.
Use vespa-makefsa to compile the list into an FSA file:
$ vespa-makefsa listofphrasestextfile phrasefsa
Put the file on all container nodes, configure the location and deploy:
<container id="default" version="1.0"> <config name="container.qr-searchers"> <com> <yahoo> <prelude> <querytransform> <PhrasingSearcher> <automatonfile>/path/phrasefsa</automatonfile> </PhrasingSearcher> </querytransform> </prelude> </yahoo> </com> </config>
Query tokens are built from text characters, as defined by
To query for terms with other characters, like c++ or .net, use special tokens.
Unlike query rewriting/phrasing, special tokens modifies data at feeding time,
changes to configuration must hence be followed by document re-feed.
Add a specialtokens config to services.xml to enable. Specify a token list called default, with a list of tokens. A token can have an optional replacement. All special tokens must be in lower-case. There is no need to enable it for particular fields, or indicate the need for special token handling during query input. Refer to specialtokens.def for details. Example configuration:
<?xml version="1.0" encoding="UTF-8"?> <services version="1.0"> <config name="vespa.configdefinition.specialtokens"> <tokenlist> <item> <name>default</name> <tokens> <item> <token>c++</token> </item> <item> <token>wal-mart</token> <replace>walmart</replace> </item> <item> <token>.net</token> </item> </tokens> </item> </tokenlist> </config> ... </services>