Document Lens is an advanced tool designed to recognize and extract entities from text files in PDF, DOCX, and TXT formats. Utilizing a scalable Natural Language Processing (NLP pipeline, it efficiently retrieves entities from multi-domain knowledge graphs or datasets accessible via SPARQL endpoints. This capability enables users to transform unstructured documents into structured, actionable data, facilitating seamless integration into various data processing workflows. Key Features and Functionality: - Entity Recognition and Extraction: Identifies and extracts entities from text documents, converting unstructured data into structured formats. - Multi-Format Support: Processes documents in PDF, DOCX, and TXT formats, ensuring versatility across different document types. - Scalable NLP Pipeline: Employs a robust NLP pipeline that can be configured to retrieve entities from diverse knowledge graphs or datasets via SPARQL endpoints. - Configurable Options: Offers a range of configurable settings, allowing users to tailor the tool to specific requirements and data sources. - Integration Capabilities: Designed to function as part of a larger end-to-end system, integrating seamlessly with other data processing tools and workflows. Primary Value and Problem Solved: Document Lens addresses the challenge of extracting meaningful information from unstructured text documents. By automating the recognition and extraction of entities, it significantly reduces the time and effort required for manual data processing. This automation enhances data accuracy and consistency, enabling organizations to efficiently integrate valuable information into their data ecosystems. As a result, users can make more informed decisions, streamline operations, and unlock insights from previously inaccessible data sources.