Why Nominatim Can't Find Hiroshima Peace Memorial Museum - and How to Fix It

Abe Tomoaki

Read the full write-up
https://www.clear-code.com/blog/2026/8/19/nominatim-with-pgroonga.html
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About me
✓ Abe Tomoaki
✓ GitHub: abetomo
✓ ClearCode Inc.
✓ FOSS development and support
✓ Developers of the full-text search engines
Groonga and PGroonga
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Let’s try it
Search from www.openstreetmap.org
広島平和記念資料館
(Hiroshima Peace Memorial Museum)
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Let’s try it
広島平和記念資料館本館
(… Main Building)
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Let’s try it
広島平和記念資料館東館
(… East Building)
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Results
Query
Result
広島平和記念資料館No hits
広島平和記念資料館本館Hit
広島平和記念資料館東館Hit
People rarely type the building name (本館/東館)
when searching. → You never reach it.
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The problem
The data for “広島平和記念資料館本館” exists, yet
“広島平和記念資料館” finds nothing
This kind of miss comes from how Nominatim
searches (explained next).
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Nominatim
✓ The software behind openstreetmap.org search
✓ Also shown in the search-result heading
✓ An OSM-based geocoder, multilingual
✓ But CJK (Chinese / Japanese / Korean) doesn’t
always work as expected
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How search works
✓ “Token-based search” = split a name into words
and match them
✓ Both names and queries are split into words the
same way
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Search (partial match)
A place hits if it contains all of the query’s words (=
partial match)
✓ By its nature, a partial match can hit a lot
✓ For performance, various search optimizations
are applied
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So what is actually happening?
1. The data is “広島平和記念資料館本館 / 東
館” (official name + building name)
2. “広島平和記念資料館” should be a subset of it
3. But the internal search optimization backfires
→ 0 results (details in the appendix)
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Why CJK doesn’t work well
1. The search optimization backfires
✓ Tends to happen with long names / queries
2. ICU treats kanji with Chinese readings
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1. The search optimization
backfires
✓ The longer the name, the more tokens
✓ The optimization backfires
✓ It gets long, so details are in the appendix
✓ More likely in CJK, where word boundaries are
unclear
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2. ICU treats kanji with Chinese
readings
✓ ICU romanizes kanji as Chinese
✓ Not the Japanese reading
✓ Different places can end up with the same
reading
✓ Search precision drops
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Example: li yuan
from icu import Transliterator
t = Transliterator.createInstance('Any-Latin; Latin-ASCII; Lower()')
for w in ['笠原','栗原','栃原','立原','梨原']:
print(w, '->', repr(t.transliterate(w)))
# Output:
# 笠原 -> 'li yuan'
# 栗原 -> 'li yuan'
# 栃原 -> 'li yuan'
# 立原 -> 'li yuan'
# 梨原 -> 'li yuan'
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Example: query “笠原”
docker compose exec -T -e NOMINATIM_PGROONGA=off nominatim nominatim search --query "笠原" | grep '"display_name"'
"display_name": "栃原, 大台町, 多気郡, 三重県, 519-2423, 日本",
"display_name": "立原, 福知山市, 京都府, 620-0917, 日本",
"display_name": "栃原, 下市町, 吉野郡, 奈良県, 638-0041, 日本",
"display_name": "栗原, 上郡町, 赤穂郡, 兵庫県, 678-1256, 日本",
"display_name": "栗原, 度会町, 度会郡, 三重県, 516-1238, 日本",
"display_name": "栃原, 伯耆町, 西伯郡, 鳥取県, 689-4222, 日本",
"display_name": "栃原, 美咲町, 久米郡, 岡山県, 日本",
"display_name": "栗原, 大津市, 滋賀県, 520-0516, 日本",
"display_name": "栗原, 真庭市, 岡山県, 719-3153, 日本",
"display_name": "梨原, 佐治町高山, 佐治, 鳥取市, 鳥取県, 689-1312, 日本",
(Environment details come later. These are results from the demo environment for
this talk.)
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$Search demo Demo environment: Docker built by importing the chugoku + kansai data docker compose exec \ -e NOMINATIM_PGROONGA=off \ nominatim \ nominatim search --query "広島平和記念資料館" 0 results Why Nominatim Can’t Find Hiroshima Peace Memorial Museum - and How to Fix It Powered by Rabbit 4.0.1$ Solution: full-text search
✓ Introduce PGroonga to do full-text search
✓ It’s full-text search in SQL, so it works through
the ORM too
✓ Only 3 Python files, a few lines changed
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Solution: full-text search
✓ Introduce PGroonga to do full-text search
✓ It’s full-text search in SQL, so it works through
the ORM too
✓ Only 3 Python files, a few lines changed
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What is PGroonga?
✓ A full-text search extension for PostgreSQL
✓ Based on Groonga
✓ You can choose a CJK-capable tokenizer
✓ This PoC uses the default Bigram
✓ Morphological analysis (MeCab) is also
available
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PGroonga: easy to use
✓ Usable from standard SQL
✓ Full-text search even with LIKE
✓ This PoC uses the more efficient &@ operator
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Nominatim + PGroonga
✓ Full-text search over all_names
✓ Hits even on a partial match
✓ More places are found without adding new
alt_name / short_name
✓ But if the alias is completely different from
the official name, data still has to be added
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The change (code excerpt)
# src/nominatim_api/search/db_searches/place_search.py
- for lookup in self.lookups:
-
sql = sql.where(lookup.sql_condition(t))
+ lookup_conditions = [lookup.sql_condition(t) for lookup in self.lookups]
+ if self.query_text:
+
name_match_condition = t.c.all_names.op('&@')(self.query_text)
+
if lookup_conditions:
+
sql = sql.where(sa.or_(sa.and_(*lookup_conditions), name_match_condition))
+
else:
+
sql = sql.where(name_match_condition)
+ elif lookup_conditions:
+
sql = sql.where(sa.and_(*lookup_conditions))
Just .op(‘&@’) for full-text search.
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$Demo with PGroonga docker compose exec \ nominatim \ nominatim search --query "広島平和記念資料館" 2 results (本館 / 東館 = Main / East buildings) are returned. Why Nominatim Can’t Find Hiroshima Peace Memorial Museum - and How to Fix It Powered by Rabbit 4.0.1$ Performance (speed)
Measured on chugoku + kansai (search_name:
785,292 rows):
✓ Extra import: ~3 min (+16% on top of the base
import)
✓ Search for “広島平和記念資料館”: ~0.2 ms
✓ Measured inside PostgreSQL
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Performance (speed)
Measured on chugoku + kansai (search_name:
785,292 rows):
✓ Extra import: ~3 min (+16% on top of the base
import)
✓ Search for “広島平和記念資料館”: ~0.2 ms
✓ Measured inside PostgreSQL
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Performance (size)
✓ PGroonga index size: 106 MB
✓ Same as the existing name_vector
✓ Smaller than nameaddress_vector (389 MB)
Relatively light on both build cost and search
speed, even when added on top of an existing
Nominatim.
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Why a PostgreSQL extension?
✓ Nominatim’s strength = it runs on PostgreSQL
alone
✓ No separate external search-engine service
✓ Simple to operate, light to deploy, architecture
unchanged
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Proposal: a plugin mechanism for
Nominatim
PGroonga is only one example.
There are many other useful extensions.
I’d like to propose a mechanism to plug in the
extension that fits the language / use case.
pg_trgm, pg_bigm, pgvector, fuzzystrmatch, …
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The other axis: improving OSM
data
✓ PGroonga should raise the hit rate
✓ But with no data, nothing will ever hit
✓ Technology × Data is what completes the search
experience
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Example: gaps in OSM data
Facility
広島平和記念
資料館
Common nameshort_name
原爆資料館empty
Result
not found
Even widely-used common names are often
unregistered.
“原爆資料館” differs in characters from the official name, so PGroonga can’t find it
either (data must be added).
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A challenge common to CJK
This time I only verified with Japanese.
Chinese and Korean should have the same
problem, but the PostgreSQL + PGroonga (plugin
mechanism) framework can in principle apply to
all three languages.
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Summary
✓ CJK search has real problems
✓ With PGroonga, much of it can be filled in
using existing data
✓ Runs on PostgreSQL alone, no external
service
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Outlook
✓ PGroonga is one example. A “pluggable
extension” mechanism in Nominatim would
make it even better
✓ Improve CJK search with both wheels:
technology × data
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Thank you
✓ Email: abe@clear-code.com
✓ This PoC (Nominatim + PGroonga)
✓ https://github.com/abetomo/Nominatim
(pgroonga branch)
✓ Demo environment (Docker Compose)
✓ https://github.com/abetomo/Nominatim-
docker-compose
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Appendix: Background 1
Nominatim tokens: there are name tokens and
address tokens.
Example for “広島平和記念資料館本館”:
✓ Name: 広島 / 平和 / 記念 / 資料館 / 本館
✓ Address: 平和大通り / 中島町 / 中区 / 広島市 / 広
島県
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Appendix: Background 2
The query is tokenized the same way.
Example when the query is “広島平和記念資料館”:
広島 / 平和 / 記念 / 資料館
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Appendix: Looks like it should hit…
✓ Data: 広島 / 平和 / 記念 / 資料館 / 本館
✓ Query: 広島 / 平和 / 記念 / 資料館
The data has all of the query’s tokens.
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Appendix: The actual search
Nominatim splits the query into a “name” part and
an “address” part.
Example: query “広島 / 平和 / 記念 / 資料館”
✓ Name part: 資料館
✓ Address part: 広島 / 平和 / 記念
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Appendix: The actual search
(address)
As a result, the address doesn’t contain “記念”, so it
doesn’t hit.
✓ The data’s address:
✓ 平和大通り / 中島町 / 中区 / 広島市 / 広島県
✓ Query (address part):
✓ 広島 / 平和 / 記念
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Appendix: Search optimization
Above I showed only one split example.
In reality many candidates are generated, and
the one easiest to search is chosen.
A hitting candidate is generated too, but it’s
judged too costly and dropped.
So only non-hitting candidates run → 0 results.
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Appendix: Note
The appendix explanations prioritize clarity.
So there are many imprecise descriptions. Please
bear with me.
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Description

Event: https://2026.foss4g.org/ja/ Blog: https://www.clear-code.com/blog/2026/8/19/nominatim-with-pgroonga.html

Text

Page: 1

Why Nominatim Can’t Find
Hiroshima Peace Memorial
Museum
and How to Fix It
Abe Tomoaki
ClearCode Inc.
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Page: 2

Read the full write-up
https://www.clear-code.com/blog/2026/8/19/nominatim-with-pgroonga.html
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Page: 3

About me
✓ Abe Tomoaki
✓ GitHub: abetomo
✓ ClearCode Inc.
✓ FOSS development and support
✓ Developers of the full-text search engines
Groonga and PGroonga
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Page: 4

Let’s try it
Search from www.openstreetmap.org
広島平和記念資料館
(Hiroshima Peace Memorial Museum)
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Page: 5

Let’s try it
広島平和記念資料館 本館
(… Main Building)
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Page: 6

Let’s try it
広島平和記念資料館 東館
(… East Building)
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Page: 7

Results
Query
Result
広島平和記念資料館No hits
広島平和記念資料館 本館Hit
広島平和記念資料館 東館Hit
People rarely type the building name (本館/東館)
when searching. → You never reach it.
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Page: 8

The problem
The data for “広島平和記念資料館 本館” exists, yet
“広島平和記念資料館” finds nothing
This kind of miss comes from how Nominatim
searches (explained next).
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Page: 9

Nominatim
✓ The software behind openstreetmap.org search
✓ Also shown in the search-result heading
✓ An OSM-based geocoder, multilingual
✓ But CJK (Chinese / Japanese / Korean) doesn’t
always work as expected
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Page: 10

How search works
✓ “Token-based search” = split a name into words
and match them
✓ Both names and queries are split into words the
same way
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Page: 11

Search (partial match)
A place hits if it contains all of the query’s words (=
partial match)
✓ By its nature, a partial match can hit a lot
✓ For performance, various search optimizations
are applied
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Page: 12

So what is actually happening?
1. The data is “広島平和記念資料館 本館 / 東
館” (official name + building name)
2. “広島平和記念資料館” should be a subset of it
3. But the internal search optimization backfires
→ 0 results (details in the appendix)
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Page: 13

Why CJK doesn’t work well
1. The search optimization backfires
✓ Tends to happen with long names / queries
2. ICU treats kanji with Chinese readings
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Page: 14

1. The search optimization
backfires
✓ The longer the name, the more tokens
✓ The optimization backfires
✓ It gets long, so details are in the appendix
✓ More likely in CJK, where word boundaries are
unclear
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Page: 15

2. ICU treats kanji with Chinese
readings
✓ ICU romanizes kanji as Chinese
✓ Not the Japanese reading
✓ Different places can end up with the same
reading
✓ Search precision drops
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Page: 16

Example: li yuan
from icu import Transliterator
t = Transliterator.createInstance('Any-Latin; Latin-ASCII; Lower()')
for w in ['笠原','栗原','栃原','立原','梨原']:
print(w, '->', repr(t.transliterate(w)))
# Output:
# 笠原 -> 'li yuan'
# 栗原 -> 'li yuan'
# 栃原 -> 'li yuan'
# 立原 -> 'li yuan'
# 梨原 -> 'li yuan'
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Page: 17

Example: query “笠原”
docker compose exec -T -e NOMINATIM_PGROONGA=off nominatim nominatim search --query "笠原" | grep '"display_name"'
"display_name": "栃原, 大台町, 多気郡, 三重県, 519-2423, 日本",
"display_name": "立原, 福知山市, 京都府, 620-0917, 日本",
"display_name": "栃原, 下市町, 吉野郡, 奈良県, 638-0041, 日本",
"display_name": "栗原, 上郡町, 赤穂郡, 兵庫県, 678-1256, 日本",
"display_name": "栗原, 度会町, 度会郡, 三重県, 516-1238, 日本",
"display_name": "栃原, 伯耆町, 西伯郡, 鳥取県, 689-4222, 日本",
"display_name": "栃原, 美咲町, 久米郡, 岡山県, 日本",
"display_name": "栗原, 大津市, 滋賀県, 520-0516, 日本",
"display_name": "栗原, 真庭市, 岡山県, 719-3153, 日本",
"display_name": "梨原, 佐治町高山, 佐治, 鳥取市, 鳥取県, 689-1312, 日本",
(Environment details come later. These are results from the demo environment for
this talk.)
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Page: 18

Search demo
Demo environment: Docker built by importing the chugoku + kansai data
docker compose exec \
-e NOMINATIM_PGROONGA=off \
nominatim \
nominatim search --query "広島平和記念資料館"
0 results
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Page: 19

Solution: full-text search
✓ Introduce PGroonga to do full-text search
✓ It’s full-text search in SQL, so it works through
the ORM too
✓ Only 3 Python files, a few lines changed
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Page: 20

What is PGroonga?
✓ A full-text search extension for PostgreSQL
✓ Based on Groonga
✓ You can choose a CJK-capable tokenizer
✓ This PoC uses the default Bigram
✓ Morphological analysis (MeCab) is also
available
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Page: 21

PGroonga: easy to use
✓ Usable from standard SQL
✓ Full-text search even with LIKE
✓ This PoC uses the more efficient &@ operator
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Page: 22

Nominatim + PGroonga
✓ Full-text search over all_names
✓ Hits even on a partial match
✓ More places are found without adding new
alt_name / short_name
✓ But if the alias is completely different from
the official name, data still has to be added
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Page: 23

The change (code excerpt)
# src/nominatim_api/search/db_searches/place_search.py
- for lookup in self.lookups:
-
sql = sql.where(lookup.sql_condition(t))
+ lookup_conditions = [lookup.sql_condition(t) for lookup in self.lookups]
+ if self.query_text:
+
name_match_condition = t.c.all_names.op('&@')(self.query_text)
+
if lookup_conditions:
+
sql = sql.where(sa.or_(sa.and_(*lookup_conditions), name_match_condition))
+
else:
+
sql = sql.where(name_match_condition)
+ elif lookup_conditions:
+
sql = sql.where(sa.and_(*lookup_conditions))
Just .op(‘&@’) for full-text search.
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Page: 24

Demo with PGroonga
docker compose exec \
nominatim \
nominatim search --query "広島平和記念資料館"
2 results (本館 / 東館 = Main / East buildings) are
returned.
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Performance (speed)
Measured on chugoku + kansai (search_name:
785,292 rows):
✓ Extra import: ~3 min (+16% on top of the base
import)
✓ Search for “広島平和記念資料館”: ~0.2 ms
✓ Measured inside PostgreSQL
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Performance (size)
✓ PGroonga index size: 106 MB
✓ Same as the existing name_vector
✓ Smaller than nameaddress_vector (389 MB)
Relatively light on both build cost and search
speed, even when added on top of an existing
Nominatim.
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Why a PostgreSQL extension?
✓ Nominatim’s strength = it runs on PostgreSQL
alone
✓ No separate external search-engine service
✓ Simple to operate, light to deploy, architecture
unchanged
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Proposal: a plugin mechanism for
Nominatim
PGroonga is only one example.
There are many other useful extensions.
I’d like to propose a mechanism to plug in the
extension that fits the language / use case.
pg_trgm, pg_bigm, pgvector, fuzzystrmatch, …
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The other axis: improving OSM
data
✓ PGroonga should raise the hit rate
✓ But with no data, nothing will ever hit
✓ Technology × Data is what completes the search
experience
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Example: gaps in OSM data
Facility
広島平和記念
資料館
Common nameshort_name
原爆資料館empty
Result
not found
Even widely-used common names are often
unregistered.
“原爆資料館” differs in characters from the official name, so PGroonga can’t find it
either (data must be added).
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A challenge common to CJK
This time I only verified with Japanese.
Chinese and Korean should have the same
problem, but the PostgreSQL + PGroonga (plugin
mechanism) framework can in principle apply to
all three languages.
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Summary
✓ CJK search has real problems
✓ With PGroonga, much of it can be filled in
using existing data
✓ Runs on PostgreSQL alone, no external
service
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Outlook
✓ PGroonga is one example. A “pluggable
extension” mechanism in Nominatim would
make it even better
✓ Improve CJK search with both wheels:
technology × data
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Thank you
✓ Email: abe@clear-code.com
✓ This PoC (Nominatim + PGroonga)
✓ https://github.com/abetomo/Nominatim
(pgroonga branch)
✓ Demo environment (Docker Compose)
✓ https://github.com/abetomo/Nominatim-
docker-compose
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Appendix: Background 1
Nominatim tokens: there are name tokens and
address tokens.
Example for “広島平和記念資料館 本館”:
✓ Name: 広島 / 平和 / 記念 / 資料館 / 本館
✓ Address: 平和大通り / 中島町 / 中区 / 広島市 / 広
島県
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Appendix: Background 2
The query is tokenized the same way.
Example when the query is “広島平和記念資料館”:
広島 / 平和 / 記念 / 資料館
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Appendix: Looks like it should hit…
✓ Data: 広島 / 平和 / 記念 / 資料館 / 本館
✓ Query: 広島 / 平和 / 記念 / 資料館
The data has all of the query’s tokens.
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Appendix: The actual search
Nominatim splits the query into a “name” part and
an “address” part.
Example: query “広島 / 平和 / 記念 / 資料館”
✓ Name part: 資料館
✓ Address part: 広島 / 平和 / 記念
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Appendix: The actual search
(address)
As a result, the address doesn’t contain “記念”, so it
doesn’t hit.
✓ The data’s address:
✓ 平和大通り / 中島町 / 中区 / 広島市 / 広島県
✓ Query (address part):
✓ 広島 / 平和 / 記念
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Appendix: Search optimization
Above I showed only one split example.
In reality many candidates are generated, and
the one easiest to search is chosen.
A hitting candidate is generated too, but it’s
judged too costly and dropped.
So only non-hitting candidates run → 0 results.
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Appendix: Note
The appendix explanations prioritize clarity.
So there are many imprecise descriptions. Please
bear with me.
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Why Nominatim Can't Find **Hiroshima Peace Memorial Museum** - and How to Fix It