Search lies at the very heart of the web, with over 30 trillion websites. The web provides us with a diverse and ever-increasing amount of data, but the way we currently search information can mean we jump from one website to another to gather all the data we need; this is because answers provided by these searches continue to direct us to be individual and isolated websites. For example, if a user wanted to find the specific information, they most likely would use a web search engine that would bring up many individual websites with only parts of the answers to their query. The world of information that they contained would become unnavigable.
So the user will end up comparing copying and pasting results from their search engines, and from social networking sites, etc.; endless to gather all the information they require combined it and draw conclusions from their past search experiences or according to user preferences because the search engine does not know what they like or what in user area means. Searching in this way does not allow users to search all these sites with just one question. Each of these websites is built using different standards and stores its information differently; that is what a search engine cannot understand. Here we are going to describe the semantic search and its uses.
What is Semantic Search?
- The word “Semantic” refers to the meaning or essence of something.
- They are applied to search, “Semantics” essentially related to the study of words and their logic.
- Semantic search seeks to improve search accuracy by understanding a searcher’s intent through contextual meaning.
- Semantic search is not natural language processing, to put it in simple terms. It’s a simple text search where the user can have a more natural English-type search.
Why Use Semantic Search?
- A deeper understanding of user intent.
- A more natural language (i.e. conversational) search.
- Understanding all the data and their Context maximizes the possibility of a user getting the best search experience possible.
How does it work?
How can Semantic technology help to improve information retrieval?
- Prerequisite: Annotation with explicit semantic, as, e.g., semantic entities.
- Enables entity-based information retrieval
- Language independent
- Make use of underlying knowledge base, as e.g.
- Content-based similarities among entities.
- Content-based relationship between entities.
- Interoperable metadata via semantic annotations
- Useful for content-based description.
- For a structural and technical description
- Content-Based Navigation and Result Filtering (Search Facets)
The lack of harmonization hinders the power of the internet as one vast base of knowledge. What is needed is that your computer can answer the question without visiting all these websites. This requires websites to have some extra information called resource description for micro-formats which their computer understands. These formats are embedded in the internal structure of the website, also called the HTML markup.
The resource descriptions and microformats tell automated programs that a web page talks about a person’s events, musicians, etc. The text block on the site results from users’ specific search queries. This will help those programs trawl through websites to easily collect, compare, and select all the information needed and draw conclusions using automated reasoning techniques.
More and more newspaper sites, encyclopedias like Wikipedia, movies, database music portals, event sites, and personal websites provide this helpful extra information to guide computers when finding answers to users’ questions.
Prototype Search Engine
Prototype search engines such as Cindy J and Sigma collect and index this information and allow users to question the web. Users‘ computer will translate these to a language called sparkle which is the standard query language that enables users to formulate structure questions that a computer can answer for users’ queries by matching users’ query to those sites on the web which provides the relevant extra information, combined with the information available on users’ computer. With Semantic search, users don’t need to spend their time searching through lots of websites to get an appropriate result.
Semantic Search Vs. Keyword-Based Search
Query String Refinement: Enable more precise or more complete search results.
Cross Referencing: Enable to complement search results with additional associated or similar information.
Fuzzy Search: Enables the determination of nearby results and related content.
Exploratory Search: Enable visualization and navigation of the search space.
Reasoning: Enable to complement search results with implicitly given information.
Retrieving the Result based on the Context
There are multiple ways to get the data based on the query.
- Build dynamic query based on the Context.
- Based on the output of the Context, we can pass it to any DB such as elastic search to retrieve data.
- Pass the context data to an already available store procedure to retrieve the data for our existing application.
Different ways of user Input
- Speech to text conversion
- Can use Google speech to text conversion API for quick implementation.
- Build a data set to train a neural network to convert speech to text.
- Text auto-suggestion
- Based on the first input of the user, the system predicts the next input for the user.
- Can build an RNN model with the most-used text searches for the domain and train the network.
Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.