Google llc (20240126827). Transferable Neural Architecture for Structured Data Extraction From Web Documents simplified abstract
Contents
- 1 Transferable Neural Architecture for Structured Data Extraction From Web Documents
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Transferable Neural Architecture for Structured Data Extraction From Web Documents - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
Transferable Neural Architecture for Structured Data Extraction From Web Documents
Organization Name
Inventor(s)
Ying Sheng of Mountain View CA (US)
Yuchen Lin of Los Angeles CA (US)
Sandeep Tata of Mountain View CA (US)
Nguyen Vo of Mountain View CA (US)
Transferable Neural Architecture for Structured Data Extraction From Web Documents - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240126827 titled 'Transferable Neural Architecture for Structured Data Extraction From Web Documents
Simplified Explanation
The technology described in the patent application utilizes neural network architectures to efficiently identify and extract machine-actionable structured data from web documents. By processing the raw HTML content of seed websites, transferable models are created to extract information of interest from other websites in a structured form.
- Neural network architectures are used to process raw HTML content of seed websites.
- Transferable models are created to identify information of interest on other websites.
- Extracted data is in a structured form for further processing.
Potential Applications
The technology can be applied in various fields such as data mining, web scraping, and information retrieval.
Problems Solved
1. Efficient extraction of structured data from web documents. 2. Scalable identification of machine-actionable information across multiple websites.
Benefits
1. Improved efficiency in data extraction. 2. Enhanced accuracy in identifying relevant information. 3. Scalability for processing data from multiple websites.
Potential Commercial Applications
The technology can be utilized in industries such as e-commerce, market research, and competitive analysis.
Possible Prior Art
One possible prior art is the use of rule-based systems for web scraping and data extraction.
What are the limitations of the neural network architectures used in this technology?
The limitations of the neural network architectures used in this technology may include the need for large amounts of training data and computational resources to create accurate models.
How does this technology compare to traditional web scraping methods in terms of efficiency and accuracy?
This technology offers improved efficiency and accuracy compared to traditional web scraping methods by utilizing neural network architectures to create transferable models for data extraction.
Original Abstract Submitted
systems and methods for efficiently identifying and extracting machine-actionable structured data from web documents are provided. the technology employs neural network architectures which process the raw html content of a set of seed websites to create transferrable models regarding information of interest. these models can then be applied to the raw html of other websites to identify similar information of interest. data can thus be extracted across multiple websites in a functional, structured form that allows it to be used further by a processing system.
