Estimates regarding the time employees spend searching for information can range from a few minutes to several hours per week – a recent survey shows that knowledge workers spend at least 2 hours every day looking for documents they need. The time spent searching for information has a significant impact on productivity, hence the raising adoption of document management systems (DMS), and more widely of enterprise content management systems, to improve access to information withing larger organizations.
The goal of a DMS is to provide a centralized repository for users across an organization to easily locate and access files, avoiding the creation of duplicates and improving the efficiency of managing and utilizing documents. However, the complexity of the information architecture, the quality of metadata, the user-friendliness of search interfaces, and the relevance and accuracy of search results all influence the time employees spend looking for information.
Metadata are descriptive information about digital documents. They are crucial for efficient DMS, as they make it easier to search and organize documents. However, maintaining consistent and standardized metadata across a large volume of documents can be challenging. Variations in terminology or the lack of a standardized taxonomy can hinder effective searching and retrieval. Also, ensuring that metadata accurately reflects the content of the documents is essential. Inaccurate or incomplete metadata can lead to misclassification and difficulties in locating the right documents. The process of entering and updating metadata can be time-consuming. If it is perceived as burdensome, users may skip or neglect this step, leading to incomplete metadata records. In global organizations, dealing with metadata in multiple languages can be challenging too. Ensuring that metadata is translated accurately and consistently adds complexity to the management process.
Our Natural Language Processing API can significantly enhance metadata management in document management systems by automatically identifying relevant keywords (keyword extraction endpoint) and creating labels reflecting the content of digital documents (similar terms endpoint). Here’s how our API can improve metadata management:
- Automated Metadata Generation:
Our API can automatically analyze the content of digital documents and extract the most relevant keywords or identify semantically similar terms in order to generate labels and tag documents. This greatly reduces the manual effort required from users and speeding up the metadata generation process.
- Consistency:
By using a Keyword Extraction API, organizations can ensure a consistent approach to metadata generation because the API follows predefined algorithms that standardize the keywords assigned to different types of documents.
- Enhanced Search:
Extracted keywords and semantically similar terms can be used to improve search capabilities within DMS. Users can search for assets based on automatically generated keywords and similar terms, making it easier to locate specific content.
- Multilingual Suppo
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- Dynamic Updating:
Our API can be used to dynamically update metadata as the content of documents evolves. This is particularly useful for content that undergoes regular updates or changes over time.
- Integration:
Our API works seamlessly with DMS. Extracted keywords and labels can be automatically fed into the DMS, enriching the metadata associated with each document.
- Scalability:
As the volume of digital documents grows, manual metadata management can become overwhelming. Our Natural Language Processing API offers a scalable solution to handle large quantities of documents and consistently generate relevant metadata.
All in all, our Natural Language Processing API helps you increase the efficiency of the metadata management process and the productivity of users, who can focus on more strategic tasks, while routine tagging is handled automatically. Contact us at sales@cortical.io to learn how to integrate our API in your document management system.
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