AI-driven scalable semantic search solution for university
One of the top universities in the US wanted to have a solution for finding the documents which are contextually similar. It can be a useful technique to identify potentially relevant documents based on contextual similarity. The goal of the system is to find related documents which represents similar questions/answers with different content representation.
Extract text segments from text file.
Build semantic search models.
Deploy models to serve users ad hoc queries.
Highlight matched text segments.
Reporting on results and export/save documents.
There were multiple challenges while developing the solution like,
Handling large amount of documents,
Providing distributed search with second level latency.
Developing custom document viewer for displaying quick result of contextual similar paragraphs.
We followed an iterative model approach to implement the solution that included the following phases:
Business requirements analysis.
Setup environment and application deployment.
Our Big Data, Machine learning development team have delivered elegant solution to the client organization as a result the product users were able to search, categorize, highlight, documents easily through the semantic search process which helped them in analyzing documents similarity, plagiarism and relevancy.
Contact us for your business needs anytime and our support team will be available to you 24 * 7 to answer your queries.