Databorg

DataBorg's suite of APIs revolutionizes how we extract and structure information from text across multiple languages. From identifying key entities to building comprehensive knowledge graphs, these tools push the boundaries of what's possible in natural language processing.

DataBorg Named Entity Recognition (NER) API

The DataBorg NER API utilizes custom transformer models to achieve state-of-the-art named entity recognition quality across multiple languages. It excels particularly in English, where it can identify and classify close to 5000 different types of entities.

With support for nine languages including German, French, Italian, Spanish, Portuguese, Dutch, Polish, and Russian, the API demonstrates robust multilingual capabilities. In these languages, it successfully extracts structured information, assigning each recognized entity a specific Wikidata class for precise categorization.

The system's accuracy and comprehensive entity recognition capabilities make it a powerful tool for information extraction across various languages and domains. Its integration of multilingual support and detailed entity classification sets it apart in the field of named entity recognition.

DataBorg Text to Knowledge Graph (T2KG) API

The DataBorg Text to Knowledge Graph (T2KG) API builds upon the foundation of named entity recognition by converting raw text into structured knowledge graphs, a process that requires identifying not only individual entities but also their relationships. This advanced capability enables the extraction of multiple entity types and their complex relationships across multiple languages, with the system demonstrating particularly strong performance in English.

Each text input to the T2KG API undergoes a sophisticated entity recognition process that identifies over 4000 distinct types of named entities, each assigned a precise Wikidata class for classification. The system's understanding extends beyond simple entity recognition to encompass over 400 types of relationships between these entities, creating a rich semantic network from the input text. This level of relationship extraction sets the T2KG API apart from simpler NER tools and enables downstream applications to leverage the interconnected nature of the extracted information.

The API's multilingual capabilities mirror those of the NER API, supporting the same nine languages with comparable entity recognition capabilities. The supported languages demonstrate varying degrees of development, as evidenced by the difference in class and relation counts between English (4948 classes, 400 relations) and Spanish (1974 classes, 400 relations), highlighting both the system's scalable architecture and its particular strengths in languages with larger available training corpora.

Web Question Answering (WebQA) API

Developed alongside the NER and T2KG APIs, the Web Question Answering (WebQA) API stands as DataBorg's specialized tool for extracting information from publicly accessible web content. This API enables users to pose natural language questions directly over indexed websites, returning answers complete with source citation information (p. 2).

The WebQA platform currently supports ten languages, broadening accessibility for diverse user bases (p. 1). Crucially, it functions by querying indexed search engine websites rather than scraping content directly, which helps maintain both efficiency and legality in information retrieval (p. 3).

Unlike the entity extraction capabilities of DataBorg's other APIs, WebQA focuses on answering specific questions rather than building comprehensive knowledge graphs. This targeted approach enables it to provide precise responses while maintaining reasonable processing times (p. 3).

Language Support and Entity Recognition

The API suite supports nine distinct languages: English, German, French, Italian, Spanish, Portuguese, Dutch, Polish, and Russian. Across these languages, the system demonstrates varying degrees of entity and class recognition capabilities, with English showing particular strength in both areas.

The Named Entity Recognition (NER) API can extract close to 5000 different types of entities from English text, marking its performance in this language as state-of-the-art (document 2 and 4). For other languages, the system's capabilities range from 2729 entity classes for Italian to 3785 for Russian, indicating robust multilingual support while acknowledging differences in system development across languages (document 3 and 5).

Similarly, the Text to Knowledge Graph (T2KG) API extracts more than 4000 entity types and 400 relationships between them in English. This consistent relationship count across languages suggests a standardized approach to entity recognition and relation extraction, although the number of extractable entity types varies, from 1974 for Spanish to 4948 for English (document 2 and 3).

The underlying technology employs custom transformer models to deliver these capabilities across the supported languages (document 1 and 2). Each recognized entity, regardless of language, receives a precise Wikidata class assignment, demonstrating the system's commitment to structured, semantically rich information extraction (document 2 and 5).

API Capabilities and Implementation

DataBorg provides detailed documentation for each API, explaining their capabilities and implementation requirements. The Named Entity Recognition (NER) API, for instance, returns results in a structured format that includes the text label and corresponding Wikidata class for each recognized entity.

The Text to Knowledge Graph (T2KG) API outputs its results in Turtle format, representing the extracted knowledge graph. Each entity recognized by the API is assigned a specific Wikidata class, enabling users to work with structured semantic data.

For implementation, users need to follow several key steps outlined in the official documentation:

1. Obtain API keys through DataBorg's registration process

2. Include the appropriate API endpoint in their request URLs

3. Format their input text according to the required specifications

4. Process the API response, which includes extracted entities and their relationships

The Web Question Answering (WebQA) API follows a slightly different implementation process. Users must first set up access to their preferred search engine's API, as the DataBorg WebQA API functions by querying these indexed websites. Once set up, users can directly send questions over the WebQA API, which processes them and returns answers along with source information.

Throughout the documentation, DataBorg emphasizes the importance of proper error handling in API requests. This includes checking response codes and handling potential rate limiting to ensure smooth integration into larger systems.