Introduction
Artificial classification is a method of organizing data into predefined categories or classes using artificial intelligence (AI) techniques. It is used to automatically classify objects, events, or observations into different groups based on their characteristics or features. This is done by training a machine learning model on a labeled dataset, which allows the model to learn the patterns and relationships between the data and the classes. Once the model is trained, it can be applied to new, unlabeled data to classify it into the appropriate category. Applications of artificial classification include image recognition, natural language processing, and anomaly detection.
In libraries, artificial classification is a method used to organize and categorize library materials such as books, journals, and other documents. This method uses artificial intelligence (AI) techniques to automatically classify and assign materials to predefined categories or classes based on their content, subject matter, and other characteristics. This allows library patrons to more easily find and access the materials they need.
History and Background
One of the most well-known artificial classification systems used in libraries is the Dewey Decimal Classification (DDC) system, which organizes materials by subject matter and assigns each item a unique number. This system was first developed in 1876 by Melvil Dewey and is still widely used today.
Artificial classification in libraries has several advantages over traditional methods such as manual classification. It can be done more quickly and accurately, it can be applied to large amounts of data, and it can be updated and revised as new materials become available. This allows libraries to more effectively serve their patrons and support research and scholarship. Additionally, as the data in libraries are increasing exponentially, artificial classification has become crucial for efficient management and organization.
In summary, artificial classification is an important tool in libraries that enables more efficient organization and retrieval of library materials, and its use has played a significant role in the history of libraries.
Example
An example of artificial classification in a library is the use of natural language processing (NLP) techniques to classify and assign subject matter tags to books and other documents. This process involves training a machine learning model on a dataset of labeled library materials, where each item has been manually assigned a set of subject matter tags. The model can then be applied to new, unlabeled materials to automatically assign subject matter tags based on the content of the materials.
For example, a library may have a collection of books on various topics such as science, literature, and history. Using artificial classification, a machine learning model can be trained on a dataset of labeled books to learn the patterns and relationships between the content of the books and the assigned subject matter tags. The model can then be used to automatically classify and assign subject matter tags to new books as they are added to the library’s collection.
Another example is the use of Automatic Classification software, which uses algorithms to classify the documents by recognizing keywords and phrases, and matching them to predefined categories or subjects. This classification could be based on the Library of Congress Classification (LCC) or Dewey Decimal Classification (DDC) system for example. This software can be integrated with the library’s catalog system and allows for more efficient and accurate organization of the materials, and faster retrieval for the patrons.
