metadata

configs:
  - config_name: DDC
    default: true
    data_files:
      - split: train
        path:
          - DDC.jsonl.gz
  - config_name: LCC
    data_files:
      - split: train
        path:
          - LCC.jsonl.gz
  - config_name: UDC
    data_files:
      - split: train
        path:
          - UDC.jsonl.gz
  - config_name: CLC
    data_files:
      - split: train
        path:
          - CLC.jsonl.gz
  - config_name: RVK
    data_files:
      - split: train
        path:
          - RVK.jsonl.gz
  - config_name: BBK
    data_files:
      - split: train
        path:
          - BBK.jsonl.gz
task_categories:
  - text-classification
task_ids:
  - multi-class-classification
pretty_name: Library Classification Systems
tags:
  - library-science
  - information-organization
  - ontology

Library Classification Systems

This comprehensive dataset contains hierarchical outlines of major library classification systems, offering a valuable resource for researchers, librarians, and information scientists.

Classification System	Abbreviation	Primary Usage	Language	Entries
Dewey Decimal Classification	DDC	International	English	1110
Library of Congress Classification	LCC	International	English	6517
Universal Decimal Classification	UDC	International	English	2431
Chinese Library Classification	CLC	China	Simplified Chinese	8826
Regensburger Verbundklassifikation	RVK	German universities	German	5032
Библиотечно-библиографическая классификация	BBK	Russia	Russian	2588

Total Entries: 26504

Note that the full classification systems can be much more detailed than presented here. For DDC, LCC, and UDC, they have to be purchased from their organizations.

Data Structure

Each instance in the dataset represents a library classification entry.

The data is structured in JSONL (JSON Lines) format, with each entry containing the following fields:

Field	Type	Description	Example
`call_number`	string	The alphanumeric call number that uniquely identifies the classification	`"AC1-999"`
`description`	string	A textual description of the classification topic or subject area	`"Collections. Series. Collected works"`
`broader`	string or null	The call number of the parent/broader classification; null for root-level classifications	`null` (for a top-level classification)
`narrower`	list of strings	An array of call numbers representing child/narrower classifications	`["AC1-195", "AC200", "AC801-895", "AC901-995", "AC999"]`

Example Instance

{
  "call_number": "AC1-999",
  "description": "Collections. Series. Collected works",
  "broader": null,
  "narrower": ["AC1-195", "AC200", "AC801-895", "AC901-995", "AC999"]
}

Dataset Creation

Click for details

On 2024-09-18, publicly available classification outlines were collected from:

LCC: Library of Congress Classification Outline
UDC: Universal Decimal Classification Summary (ROSSIO Vocabulary Server)
DDC: Dewey Services - Resources (OCLC)
CLC: CLC Index
RVK: RVK Online
BBK: КлассИнформ (crawled 2024-09-19)

Versions used:

UDC Master Reference File (2011)
DDC 23 Summaries
RVK aktuelle csv-Datei (2023_2)

Data processing steps:

DDC: Downloaded summary PDF and formatted for consistency.
LCC: Converted Word files to plain text, extracting headings and call numbers programmatically.
UDC: Parsed udc-summary.rdf using RDFlib, retaining only English labels.
CLC: Crawled website, including only the first 3 hierarchy levels.
RVK: Processed CSV file, keeping only the first 3 hierarchy levels.
BBK: Crawled website, no limit on hierarchy levels.

Supported Tasks and Leaderboards

This dataset is valuable for various tasks in information science and machine learning, including:

Hierarchical Text Classification
Library Science and Information Organization
Ontology and Knowledge Graph Construction
Comparative Studies of Classification Systems
Machine Learning for Library Cataloging

While no leaderboards are currently associated with this dataset, it provides opportunities for developing benchmarks in automated classification accuracy and ontology mapping.

Considerations for Using the Data

Social Impact

This dataset broadens access to library classification knowledge, potentially:

Facilitating research in information science and digital humanities.
Supporting improved information retrieval systems.
Enhancing educational resources for library and information science students.

Discussion of Biases

Be aware that library classification systems may reflect historical, cultural, and geographical biases, such as:

Overrepresentation of Western Perspectives
Outdated Terminology or Categorizations
Uneven Depth of Coverage across subject areas.

Known Limitations

Scope: Includes only freely available outlines, not complete texts.
Granularity: Detail levels vary across systems and subjects.
Currency: Updates to classification systems may not be immediately reflected.
Structural Differences: UDC's structure differs from DDC and LCC, affecting comparisons.

Datasets:

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library-classification-systems