ShriGB, as the name goes, is about extracting valuable insights (“Shri” – respect) from large/big data (“GB”) . The project is aimed to leverage semantic web & big data technologies to extract meaningful insights from unstructured financial data lying across the web. The data is mostly present in raw form and is useful to some sections of society although, can be used by different section of people for different reasons.
Lets take a look at following example:
The above data can mean some of the following:
- More jobs are going to be created in Uttaranchal region
- This may lead to boost in the real estate business; Anyone planning to get into real estate business can use this news to plan
- This may as well be taken up by recruitment consultants to contact Dabur for hiring local talent in Uttaranchal
- Additionally, others can use the news to plan their business investments.
However, the data is not currently presented in the form & structure (linked) which can be easily consumed by above mentioned section of society.
This is where ShriGB comes into picture. At present, ShriGB is doing following:
- Gathering data from various financial portals from India
- Creating ontology/taxonomy on this data
- Create structure around the data based on RDF (Resource Description Framework) Triples and store them accordingly
- Provide URI to different resources; Take a look at following examples
- Companies investment information can be retrieved using entity URI such as http://vitalflux.com/investments/entity/<company name>; For example, http://vitalflux.com/investments/entity/infosys
- Investments in different regions can be retrieved using region URI such as http://vitalflux.com/investments/entity/<region name>; For example, http://vitalflux.com/investments/region/maharashtra
- Use Schema.org vocabulary to tag data
- Linking data in a structured and meaningful way for easy consumption
He has also authored the book, Building Web Apps with Spring 5 and Angular.
Latest posts by Ajitesh Kumar (see all)
- Difference between Machine Learning & Traditional Software - October 30, 2019
- Neural Network Architecture for Text-to-Speech Synthesis - July 29, 2019
- Reverse Image Search using Deep Learning (CNN) - July 27, 2019