The tokenizer module in Nominatim is responsible for analysing the names given to OSM objects and the terms of an incoming query in order to make sure, they can be matched appropriately.
Nominatim offers different tokenizer modules, which behave differently and have different configuration options. This sections describes the tokenizers and how they can be configured.
The use of a tokenizer is tied to a database installation. You need to choose and configure the tokenizer before starting the initial import. Once the import is done, you cannot switch to another tokenizer anymore. Reconfiguring the chosen tokenizer is very limited as well. See the comments in each tokenizer section.
The legacy tokenizer implements the analysis algorithms of older Nominatim versions. It uses a special Postgresql module to normalize names and queries. This tokenizer is automatically installed and used when upgrading an older database. It should not be used for new installations anymore.
Compiling the PostgreSQL module
The tokeinzer needs a special C module for PostgreSQL which is not compiled by default. If you need the legacy tokenizer, compile Nominatim as follows:
mkdir build cd build cmake -DBUILD_MODULE=on make
Enabling the tokenizer
To enable the tokenizer add the following line to your project configuration:
The Postgresql module for the tokenizer is available in the
and also installed with the remainder of the software under
lib/nominatim/module/nominatim.so. You can specify a custom location for
the module with
NOMINATIM_DATABASE_MODULE_PATH=<path to directory where nominatim.so resides>
This is in particular useful when the database runs on a different server. See Advanced installations for details.
There are no other configuration options for the legacy tokenizer. All normalization functions are hard-coded.
The ICU tokenizer uses the ICU library to normalize names and queries. It also offers configurable decomposition and abbreviation handling. This tokenizer is currently the default.
To enable the tokenizer add the following line to your project configuration:
How it works
On import the tokenizer processes names in the following three stages:
- During the Sanitizer step incoming names are cleaned up and converted to full names. This step can be used to regularize spelling, split multi-name tags into their parts and tag names with additional attributes. See the Sanitizers section below for available cleaning routines.
- The Normalization part removes all information from the full names that are not relevant for search.
- The Token analysis step takes the normalized full names and creates all transliterated variants under which the name should be searchable. See the Token analysis section below for more information.
During query time, only normalization and transliteration are relevant. An incoming query is first split into name chunks (this usually means splitting the string at the commas) and the each part is normalised and transliterated. The result is used to look up places in the search index.
The ICU tokenizer is configured using a YAML file which can be configured using
NOMINATIM_TOKENIZER_CONFIG. The configuration is read on import and then
saved as part of the internal database status. Later changes to the variable
have no effect.
Here is an example configuration file:
normalization: - ":: lower ()" - "ß > 'ss'" # German szet is unimbigiously equal to double ss transliteration: - !include /etc/nominatim/icu-rules/extended-unicode-to-asccii.yaml - ":: Ascii ()" sanitizers: - step: split-name-list token-analysis: - analyzer: generic variants: - !include icu-rules/variants-ca.yaml - words: - road -> rd - bridge -> bdge,br,brdg,bri,brg mutations: - pattern: 'ä' replacements: ['ä', 'ae']
The configuration file contains four sections:
Normalization and Transliteration
The normalization and transliteration sections each define a set of ICU rules that are applied to the names.
The normalisation rules are applied after sanitation. They should remove any information that is not relevant for search at all. Usual rules to be applied here are: lower-casing, removing of special characters, cleanup of spaces.
The transliteration rules are applied at the end of the tokenization process to transfer the name into an ASCII representation. Transliteration can be useful to allow for further fuzzy matching, especially between different scripts.
Each section must contain a list of
ICU transformation rules.
The rules are applied in the order in which they appear in the file.
You can also include additional rules from external yaml file using the
!include tag. The included file must contain a valid YAML list of ICU rules
and may again include other files.
The ICU rule syntax contains special characters that conflict with the YAML syntax. You should therefore always enclose the ICU rules in double-quotes.
The sanitizers section defines an ordered list of functions that are applied to the name and address tags before they are further processed by the tokenizer. They allows to clean up the tagging and bring it to a standardized form more suitable for building the search index.
Sanitizers only have an effect on how the search index is built. They do not change the information about each place that is saved in the database. In particular, they have no influence on how the results are displayed. The returned results always show the original information as stored in the OpenStreetMap database.
Each entry contains information of a sanitizer to be applied. It has a
step which gives the name of the sanitizer. Depending
on the type, it may have additional parameters to configure its operation.
The order of the list matters. The sanitizers are applied exactly in the order that is configured. Each sanitizer works on the results of the previous one.
The following is a list of sanitizers that are shipped with Nominatim.
Sanitizer that splits lists of names into their components.
This sanitizer creates additional name variants for names that have addendums in brackets (e.g. "Halle (Saale)"). The additional variant contains only the main name part with the bracket part removed.
This sanitizer sets the
analyzer property depending on the
language of the tag. The language is taken from the suffix of the name.
If a name already has an analyzer tagged, then this is kept.
Sanitizer that preprocesses address tags for house numbers. The sanitizer allows to
- define which tags are to be considered house numbers (see 'filter-kind')
- split house number lists into individual numbers (see 'delimiters')
Token analyzers take a full name and transform it into one or more normalized form that are then saved in the search index. In its simplest form, the analyzer only applies the transliteration rules. More complex analyzers create additional spelling variants of a name. This is useful to handle decomposition and abbreviation.
The ICU tokenizer may use different analyzers for different names. To select
the analyzer to be used, the name must be tagged with the
by a sanitizer (see for example the
The token-analysis section contains the list of configured analyzers. Each
analyzer must have an
id parameter that uniquely identifies the analyzer.
The only exception is the default analyzer that is used when no special
analyzer was selected. There is one special id '@housenumber'. If an analyzer
with that name is present, it is used for normalization of house numbers.
Different analyzer implementations may exist. To select the implementation,
analyzer parameter must be set. The different implementations are
described in the following.
Generic token analyzer
The generic analyzer
generic is able to create variants from a list of given
abbreviation and decomposition replacements and introduce spelling variations.
The optional 'variants' section defines lists of replacements which create alternative spellings of a name. To create the variants, a name is scanned from left to right and the longest matching replacement is applied until the end of the string is reached.
The variants section must contain a list of replacement groups. Each group defines a set of properties that describes where the replacements are applicable. In addition, the word section defines the list of replacements to be made. The basic replacement description is of the form:
<source>[,<source>[...]] => <target>[,<target>[...]]
The left side contains one or more
source terms to be replaced. The right side
lists one or more replacements. Each source is replaced with each replacement
The source and target terms are internally normalized using the normalization rules given in the configuration. This ensures that the strings match as expected. In fact, it is better to use unnormalized words in the configuration because then it is possible to change the rules for normalization later without having to adapt the variant rules.
In its standard form, only full words match against the source. There is a special notation to match the prefix and suffix of a word:
- ~strasse => str # matches "strasse" as full word and in suffix position - hinter~ => hntr # matches "hinter" as full word and in prefix position
There is no facility to match a string in the middle of the word. The suffix and prefix notation automatically trigger the decomposition mode: two variants are created for each replacement, one with the replacement attached to the word and one separate. So in above example, the tokenization of "hauptstrasse" will create the variants "hauptstr" and "haupt str". Similarly, the name "rote strasse" triggers the variants "rote str" and "rotestr". By having decomposition work both ways, it is sufficient to create the variants at index time. The variant rules are not applied at query time.
To avoid automatic decomposition, use the '|' notation:
- ~strasse |=> str
simply changes "hauptstrasse" to "hauptstr" and "rote strasse" to "rote str".
Initial and final terms
It is also possible to restrict replacements to the beginning and end of a name:
- ^south => s # matches only at the beginning of the name - road$ => rd # matches only at the end of the name
So the first example would trigger a replacement for "south 45th street" but not for "the south beach restaurant".
Replacements vs. variants
The replacement syntax
source => target works as a pure replacement. It changes
the name instead of creating a variant. To create an additional version, you'd
have to write
source => source,target. As this is a frequent case, there is
a shortcut notation for it:
<source>[,<source>[...]] -> <target>[,<target>[...]]
The simple arrow causes an additional variant to be added. Note that decomposition has an effect here on the source as well. So a rule
- "~strasse -> str"
means that for a word like
hauptstrasse four variants are created:
The 'mutation' section in the configuration describes an additional set of replacements to be applied after the variants have been computed.
Each mutation is described by two parameters:
The pattern must contain a single regular expression to search for in the
variant name. The regular expressions need to follow the syntax for
Python regular expressions.
Capturing groups are not permitted.
replacements must contain a list of strings that the pattern
should be replaced with. Each occurrence of the pattern is replaced with
all given replacements. Be mindful of combinatorial explosion of variants.
The generic analyser supports a special mode
variant-only. When configured
then it consumes the input token and emits only variants (if any exist). Enable
the mode by adding:
to the analyser configuration.
Housenumber token analyzer
housenumbers is purpose-made to analyze house numbers. It
creates variants with optional spaces between numbers and letters. Thus,
house numbers of the form '3 a', '3A', '3-A' etc. are all considered equivalent.
The analyzer cannot be customized.
Changing the configuration after the import is currently not possible, although this feature may be added at a later time.