Achieving Japanese Full-Text Search with the MroongaSearch Module for Omeka-S

Introduction

Omeka-S is a powerful digital archive system, but by default, Japanese full-text search barely functions. This article explains how to achieve Japanese full-text search by introducing the MroongaSearch module.

Why the MroongaSearch Module is Essential

Problems with Omeka-S Standard Search

Omeka-S’s standard full-text search (FullTextSearch module) uses the InnoDB engine, which has the following critical issues:

Example of Japanese word search:

Since InnoDB’s full-text search assumes space-delimited languages like English, for Japanese:

• Word search is impossible: The entire string is treated as a single word
• Partial matching also fails: FULLTEXT indexes cannot process Japanese correctly
• Zero search results: Users cannot find anything

The MroongaSearch Module Solution

The MroongaSearch module solves this problem in two stages:

1. Fallback Feature (Active Immediately After Module Installation)

Important: Simply installing the MroongaSearch module enables Japanese search to work without any special configuration.

The MroongaSearch module’s fallback feature:

• Automatically detects CJK (Japanese, Chinese, Korean) single-word searches
• Automatically falls back to LIKE '%term%' search
• Works even without Mroonga configured
• Without this, Japanese full-text search simply does not work properly

2. Fast and Precise Search with Mroonga + TokenMecab (Recommended)

Furthermore, by configuring the Mroonga plugin for MariaDB:

• Precise word search through morphological analysis
• Fast full-text search (hundreds of times faster than LIKE)
• Strict control of AND/OR search

What is the MroongaSearch Module?

MroongaSearch is a full-text search enhancement module for Omeka-S.

Main Features

1. Automatic fallback feature

   • Enables CJK search even without Mroonga configured
   • Automatic switching to LIKE search
   • Ready to use immediately without configuration

2. Mroonga integration

   • Precise search through morphological analysis
   • TokenMecab support
   • Fast index-based search

3. Diagnostic page

   • Plugin status verification
   • Table engine display
   • Tokenizer information
   • Manual engine switching

4. Strict AND/OR search

   • More precise search logic than standard FullTextSearch

Developers

• Kentaro Fukuchi (initial version)
• Kazufumi Fukuda (feature extensions)
• Toshihito Waki (current maintainer)

Setup Procedure

Step 1: Install the MroongaSearch Module

Activate the module from the Omeka-S admin panel.

Japanese search will work with just this! (via LIKE search fallback)

Step 2: Build the Mroonga Environment (Recommended)

For faster and more precise search, configure the Mroonga plugin for MariaDB.

For Docker Environments

Directory structure:

mariadb/Dockerfile:

mariadb/init.sql:

docker-compose.yml (mariadb section):

Rebuilding containers:

Step 3: Verify the Setup

1. Verify the Mroonga Plugin

Expected output:

2. Verify TokenMecab

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Expected output (excerpt):

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If TokenMecab is included, you are all set.

3. Check the MroongaSearch Diagnostic Page

In the Omeka-S admin panel:

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Displayed information:

• Plugin status: ACTIVE / NOT ACTIVE
• Table engine: InnoDB / Mroonga
• Tokenizer: TokenMecab / None
• Mroonga effective: YES / NO

If “Mroonga effective: NO”:

• Plugin is ACTIVE but the table engine remains InnoDB
• Fallback search (LIKE) is used
• It works but is slow

To set “Mroonga effective: YES”:

• Manually switch the engine to Mroonga from the diagnostic page

• Or change directly via SQL:

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4. Re-index

Run re-indexing from the diagnostic page or the Omeka-S admin panel.

How Search Works

Without Mroonga Configured (Fallback)

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With Mroonga + TokenMecab Configured

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Substring Search Also Works

Mroonga supports not only morphological analysis but also substring search:

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This allows users to get results even when they do not know the exact words.

Morphological Analysis with TokenMecab

What is Morphological Analysis?

Since Japanese does not have space-delimited words like English, sentences need to be segmented into words.

Example:

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This enables word-level searching for terms like “Tokyo” and “university.”

Limitations of Morphological Analysis

TokenMecab is powerful, but may not work as expected in the following cases:

1. Proper Nouns (New Words Not in the Dictionary)

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2. Compound Words and Technical Terms

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3. Coined Words and Neologisms

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4. Multiple Segmentation Patterns

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Solutions

• User dictionary: Add custom words to the MeCab dictionary
• TokenBigram: Supplement partial matches with 2-character N-grams
• Fallback: MroongaSearch automatically uses LIKE search as well

Available Tokenizers

Performance Comparison

LIKE Search (Fallback)

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• Full table scan
• Delay proportional to data volume
• However, search results are returned (zero without the module)

Mroonga Full-Text Search

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• Uses indexes
• Fast search (hundreds of times faster than LIKE)
• Scalable

Summary

Importance of the MroongaSearch Module

1. Essential: The MroongaSearch module is essential for Japanese full-text search in Omeka-S
2. Immediate effect: Search is possible immediately after installation via the fallback feature
3. Incremental improvement: Further speed improvement with Mroonga configuration

Recommended Setup

Benefits of Implementation

1. Japanese search becomes possible: Immediately functional via fallback
2. Improved accuracy: Word-level search with TokenMecab
3. Speed improvement: Optimization with the Groonga engine
4. Flexibility: Both morphological search and partial matching

Conclusion: When handling Japanese content in Omeka-S, the MroongaSearch module is essential.

Reference Links

• MroongaSearch GitHub
• Mroonga Official Site
• MeCab Official Site
• Omeka-S Official Documentation
• MariaDB Mroonga Plugin

Testing Environment

• Omeka-S: 4.1.1
• MroongaSearch: latest
• MariaDB: latest (11.x)
• Docker Compose
• macOS (Darwin 24.6.0)

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