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README.md

@@ -39,81 +39,32 @@ tags:
 
 # Library Classification Systems
 
-## Dataset Summary
-
 This comprehensive dataset encompasses hierarchical outlines of five major library classification systems:
 
-1. Dewey Decimal Classification (DDC)
-2. Library of Congress Classification (LCC)
-3. Universal Decimal Classification (UDC)
-4. Chinese Library Classification (CLC)
-5. Regensburger Verbundklassifikation (RVK)
-
-The dataset provides freely-available outlines of these classification systems, which are widely used in libraries and information management systems worldwide. It captures the hierarchical structure and descriptions for each classification, offering a valuable resource for researchers, librarians, and information scientists. However, it's important to note that the full text of the first three classification systems is not included and must be purchased separately from their organizations.
-
-### Data Format and Content
-
-The data is structured in JSONL (JSON Lines) format, with each entry containing fields for:
+| Classification System | Abbreviation | Primary Usage | Language | Entries |
+|-----------------------|--------------|----------------|----------|---------|
+| Dewey Decimal Classification | DDC | International | English | 1,110 |
+| Library of Congress Classification | LCC | International | English | 6,517 |
+| Universal Decimal Classification | UDC | International | English | 2,431 |
+| Chinese Library Classification | CLC | China | Simplified Chinese | 8,826 |
+| Regensburger Verbundklassifikation | RVK | German universities | German | 5,032 |
 
-- Call numbers
-- Descriptions
-- Broader categories
-- Narrower subcategories
-
-This format ensures easy parsing and integration into various data processing workflows.
-
-### Classification Systems Overview
-
-- **DDC, LCC, and UDC**: These are internationally recognized systems used in libraries across the globe.
-- **CLC**: Primarily used in China by public schools, libraries, and publishers.
-- **RVK**: Employed by several German universities for organizing their collections.
-
-## Supported Tasks and Leaderboards
+**Total Entries:** 23,916
 
-This dataset is particularly valuable for a range of tasks in information science and machine learning, including:
-
-1. Hierarchical Text Classification
-2. Library Science and Information Organization
-3. Ontology and Knowledge Graph Construction
-4. Comparative Studies of Classification Systems
-5. Machine Learning for Library Cataloging
-
-While there are no specific leaderboards associated with this dataset, it presents opportunities for developing benchmarks in areas such as automated classification accuracy or ontology mapping.
-
-## Languages
-
-The dataset incorporates multiple languages, reflecting the global nature of library classification systems:
-
-- English: DDC, LCC, and UDC
-- Simplified Chinese: CLC
-- German: RVK
-
-This multilingual aspect enhances the dataset's utility for cross-lingual information retrieval and classification tasks.
+The dataset provides freely-available outlines of these classification systems, which are widely used in libraries and information management systems worldwide. It captures the hierarchical structure and descriptions for each classification, offering a valuable resource for researchers, librarians, and information scientists. However, it's important to note that the full text of the first three classification systems is not included and must be purchased separately from their organizations.
 
 ## Data Structure
 
-### Data Instances
-
 Each instance in the dataset represents a library classification entry. The structure is designed to capture the hierarchical nature of the classification scheme.
 
-### Data Fields
-
-- `call_number`: string
-  - The alphanumeric call number that uniquely identifies the classification
-  - Example: `"AC1-999"`
+The data is structured in **JSONL (JSON Lines)** format, with each entry containing the following fields:
 
-- `description`: string
-  - A textual description of the classification topic or subject area
-  - Example: `"Collections. Series. Collected works"`
-
-- `broader`: string or null
-  - The call number of the parent/broader classification
-  - Null for root-level classifications
-  - Example: `null` (for a top-level classification)
-
-- `narrower`: list of strings
-  - An array of call numbers representing child/narrower classifications
-  - Example: `["AC1-195", "AC200", "AC801-895", "AC901-995", "AC999"]`
+| 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
 
@@ -126,78 +77,68 @@ Each instance in the dataset represents a library classification entry. The stru
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-This structure allows for efficient navigation and querying of the classification hierarchy, enabling users to traverse from broader to narrower topics and vice versa.
-
-### Data Splits
-
-The full dataset contains:
-
-- 1,110 DDC entries
-- 6,517 LCC entries
-- 2,431 UDC entries
-- 8,826 CLC entries
-- 5,032 RVK entries
-
-Total: 23,916 entries
-
 ## Dataset Creation
 
-### Curation Rationale
-
-This dataset was curated to provide open access to the hierarchical structure of major library classification systems. It aims to support research, education, and innovation in information organization and retrieval.
-
-### Source Data
-
-The dataset is derived from the publicly available outlines of the Dewey Decimal Classification (DDC), Library of Congress Classification (LCC), and Universal Decimal Classification (UDC). Full classification texts are proprietary and require purchase from the respective organizations.
+On 2024-09-18, publicly available classification outlines were collected from:
 
-#### Initial Data Collection and Normalization
-
-Classification outlines were collected on 2024-09-18 from the following authoritative sources:
-
-- Library of Congress Classification (LCC): Library of Congress Classification Outline
-- Universal Decimal Classification (UDC): Universal Decimal Classification Summary (ROSSIO Vocabulary Server)
-- Dewey Decimal Classification (DDC): Dewey Services - Resources (OCLC)
-- Chinese Library Classification (CLC): CLC Index
-- Regensburger Verbundklassifikation (RVK): RVK Online
+- **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
 
 Versions used:
-- UDC Master Reference File (UDC MRF) 2011
+- UDC Master Reference File (2011)
 - DDC 23 Summaries
 - RVK aktuelle csv-Datei (2023_2)
 
 Data processing steps:
-1. DDC: The summary PDF was downloaded and manually formatted for consistency.
-2. LCC: Microsoft Word files were converted to plain text, with headings and call numbers extracted programmatically.
-3. UDC: The `udc-summary.rdf` file was parsed using RDFlib, retaining only English labels.
-4. CLC: The data was collected by crawling through the website. Only the first 3 levels of the hierarchy were included.
-5. RVK: The CSV file was processed, keeping only the first 3 levels of the hierarchy.
+1. **DDC**: Downloaded summary PDF and formatted for consistency.
+2. **LCC**: Converted Word files to plain text, extracting headings and call numbers programmatically.
+3. **UDC**: Parsed `udc-summary.rdf` using RDFlib, retaining only English labels.
+4. **CLC**: Crawled website, including only the first 3 hierarchy levels.
+5. **RVK**: Processed CSV file, keeping only the first 3 hierarchy levels.
+
+## Supported Tasks and Leaderboards
+
+This dataset is valuable for various tasks in information science and machine learning, including:
+
+1. **Hierarchical Text Classification**
+2. **Library Science and Information Organization**
+3. **Ontology and Knowledge Graph Construction**
+4. **Comparative Studies of Classification Systems**
+5. **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 of Dataset
+### Social Impact
 
-This dataset expands access to library classification knowledge, potentially:
-- Facilitating research in information science and digital humanities
-- Supporting the development of improved information retrieval systems
-- Enhancing educational resources for library and information science students
+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
 
-Users should be aware that library classification systems may reflect historical, cultural, and geographical biases in knowledge organization. These biases could manifest as:
-- Overrepresentation of Western perspectives
-- Outdated terminology or categorizations for certain topics
-- Uneven depth of coverage across different subject areas
+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
 
-1. Scope: The dataset includes only freely available outlines, not the complete classification texts.
-2. Granularity: The level of detail varies between classification systems and within subject areas.
-3. Currency: Updates to the original classification systems may not be immediately reflected in this dataset.
-4. Structural differences: The UDC system's structure, with its main and auxiliary tables, differs slightly from DDC and LCC, which may affect comparisons.
+1. **Scope**: Includes only freely available outlines, not complete texts.
+2. **Granularity**: Detail levels vary across systems and subjects.
+3. **Currency**: Updates to classification systems may not be immediately reflected.
+4. **Structural Differences**: UDC's structure differs from DDC and LCC, affecting comparisons.
 
 ## Additional Information
 
-### Dataset Curators
+### Dataset Curator
 
 Alan Tseng