TinySearch -- Semantics based Search Engine using Bert Embeddings
This addresses search engine limitations for users handling complex queries, though it appears incremental as it builds on existing BERT technology.
The paper tackles the problem of existing search engines failing to capture meaning in large, complex queries by developing a semantics-oriented search engine using BERT embeddings and neural networks. The result shows improvement over an existing search engine for complex queries on a given document set.
Existing search engines use keyword matching or tf-idf based matching to map the query to the web-documents and rank them. They also consider other factors such as page rank, hubs-and-authority scores, knowledge graphs to make the results more meaningful. However, the existing search engines fail to capture the meaning of query when it becomes large and complex. BERT, introduced by Google in 2018, provides embeddings for words as well as sentences. In this paper, I have developed a semantics-oriented search engine using neural networks and BERT embeddings that can search for query and rank the documents in the order of the most meaningful to least meaningful. The results shows improvement over one existing search engine for complex queries for given set of documents.