This project aims at developing a Semantic Searching Engine that could search with meaning not only to find keywords but to determine the intent and contextual meaning of the input sentence. We will demonstrate another finding technology known as Semantic search by utilizing sophisticated Artificial Intelligence technologies and some Natural Language Processing models.
Introduction
There are so many searching engines currently, but most of them are using the Keyword Search method. Keyword search is looking at terms wherever they appear, even if part of a larger phrase or used in a different context, Keyword Search will find words anywhere in the record. So the Keyword Search has a limitation in that the words often have multiple meanings, so Keyword Search will return the irrelevant results.
Unlike Keyword Search, Semantic Search is a search with meaning, it will try to get at the intent and contextual meaning of terms and your search query. And we have built a Semantic Search engine on the database from https://wikitravel.org/en/Main_Page. It will find and show you the top k (default k=10) articles with the best matching meaning to your search query.
Our Approaches
Data
We crawled all existing articles from https://wikitravel.org/en/Main_Page. After cleaning and preprocessing the crawled data to remove the non-existent articles, wrong, and repeat posts... the final number of articles is 31249 articles. And we store them into a .csv file for use in the searching step.
Text Similar Model
In order to search the input sentence based on its matching meaning with the article database. We have applied a Deep Learning model to represent the sentence with an embedding vector, and then we will compute the sentence similarly based on these embeddings vectors to find the best matching results.
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