The problem statement selected is 'Reduce The Noise Of News Search' (No.7). Time is of essence and hence spending it by only consuming necessary information is very important. That is the problem this project tries to solve in the domain of news search.

What it does

An eclectic news search engine that helps with deduplication of news articles and enriches your reading experience. Enables readers to dig deep into a certain opinion or explore multiple facets of the news at hand.

How we built it

1) Fetched news articles from

2) Used the PyTigerGraph Python library to interact with the TigerGraph instance.

3) Used 3 different NLP models for Semantic Search, Keyword Generation and Sentiment Analysis respectively. Helps with enriching the news articles with additional metadata.

4) Later, loaded all data to the TigerGraph instance and processed/explored data in several ways using custom GSQL queries as well as special algorithms like centrality and similarity.

5) Finally, results are shown to the end-user in a Streamlit web application.

Challenges we ran into

There is a slight learning curve to graph technology and GSQL if you are learning it for the very first time. The resources and explanations provided in the TigerGraph docs were very helpful. My personal notes and useful references for this project have been linked in the Github repo.

Accomplishments that we're proud of

Being able to build a competent real-world solution for a pressing problem.

What we learned

Explored and used Graph technology for the first time and it was a very fulfilling experience. Learned about how existing news search engines work, their strengths and weaknesses and how cutting edge advances in NLP (keyword generation, semantic search) can be used to make news search better for the readers.

What's next for Gemini

The solution and the way it has been built can be extended into a regular search engine capable of generalizing on regular websites (non-news) as well; for different search terms.

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