Have you get irritated when you have millions of documents & your search results couldn’t meet your requirement.
So here we go !!!
Searching is not about finding any document,
Searching is about finding the more relevant one.
Google does it amazing for us !!! But But But…..
The curious mind raised a question, can we integrate somehow “Google Search” on our files or documents.
If it’s somehow possible then that would be great for me, because if have millions of documents/files google could just use his wand and serve my complex searches.
It’s an evil world man, all the good things would not come at the same time, it will take some time to reach to you. So the answer is NO for the time being.
As you are on this post and you are looking for the solutions it’s my duty to serve you and solve your concerned. A long time ago we have decided to not rely on “Google” or anyone else to introduce any sort of services for this custom search on your own files/documents.
Once a wise man name as PLATO said: “Necessity is the mother of invention“. As he is a wise man & also he said an amazing thing, so we had taken inspiration from this and made service for the community to address and solve this issue.
What you can expect ??
Before your expectations, what we can expect, that you have documents minimum is at least 1 & for maximum: it’s up to you, will take any numbers of documents millions, trillions and all are good, no complaints.
Now it’s time to serve your expectations. After getting the documents it’s our responsibility to serve all queries.
The system has more than 20 features. Let me highlight some of the important ones.
Keyword searches: It’s as simple as that if query word is present in your documents, the document will present in the result set. It’s very common to understand the concept for keyword matches. Have you tried to find a particular word in a 100 MB text files, then you should notice it takes some time to find your matches. Just assume you have 1TB of data and you have searched for a word in that. As I said just assume, don’t do that :). To avoid this kind of issue we are using an inverted index concept to get the latency for each and every searches.
Semantic Searches: Semantic search is a data searching technique in a which a search query aims to not only find keywords but to determine the intent and contextual meaning of the words a person is using for search.
Semantic search provides more meaningful search results by evaluating and understanding the search phrase and finding the most relevant results in a website, database or any other data repository.
Semantic search works on the principles of language semantics. Unlike typical search algorithms, semantic search is based on the context, substance, intent, and concept of the searched phrase. Semantic search also incorporates location, synonyms of a term, current trends, word variations and other natural language elements as part of the search. Semantic search concepts are derived from various search algorithms and methodologies, including keyword-to-concept mapping, graph patterns, and fuzzy logic.
KeyPhrases Extraction: The extraction of important topical words and phrases from documents, commonly known as terminology extraction or automatic keyphrase extraction. Keyphrases provide a concise description of a document’s content; they are useful for document categorization, clustering, indexing, search, and summarization; quantifying semantic similarity with other documents; as well as conceptualizing particular knowledge domains.
There are many other features like clustering of documents based on our custom models, Clustering of words from all the documents, text summarization, moreLikeThis, suggest components, feedback learning and many more that we should elaborate in our next blog. Stay tuned till our next release.