Semantic Web: Deep Mining of Search Queries with Accurate Interpretation

From the data analytics reports of InternetLiveStats.com, Google processes more than 40,000 search queries per second. Around 20% queries on Google are those which are new everyday and never entered before on the search engine. The major challenge with the search engines is to get the accurate results without irrelevant outcomes.

Semantic Web in broad terms refers to the Web with Meaning using Interconnections. The web in semantic web is able to describe things in a way that computers can understand the actual meaning which reside in the search query or browsing behavior. 

The wonderful powers of semantic web can be seen on the prominent website like Skyscanner.net and Trivago.com. These portals compare the real time prices from different services providing portals and give the best results in form of comparison. The back-end libraries of these portals communicate with different websites and then fetch the results so that the users can see the minimum price of hotels or flights. The protocols of semantic web work with these websites to fetch the related information from multiple locations.

Whenever the information about specific service, product, company or object is required, the users navigate different search engines so that the related website can be fetched and particular information can be found. Now, the major issues with the traditional search engines are that the information may be scattered and irrelevant.

For example, if a user enters a search keyword “gold today” on the search engine, there are many haphazard results which may include the following aspects

• Gold Price
• Gold Jewellers
• Star Gold
• Gold Loan

If the user is fond of watching Movies on Star Gold, then the semantic web search engine should give all results of “Star Gold” on searching of “Gold”.

Open Source Tools and Libraries for Semantic Web

• Apache Jena: http://jena.apache.org/
• Apache TinkerPop: https://tinkerpop.apache.org
• D2R: http://d2rq.org/d2r-server
• Linked Media Framework: https://code.google.com/p/lmf/
• Open Semantic Framework: http://opensemanticframework.org/
• Paget: http://code.google.com/p/paget/
• Protégé: https://protege.stanford.edu
• RDFLib: https://github.com/RDFLib/rdflib
• Semantic Media: http://semantic-mediawiki.org/wiki/Semantic_MediaWiki
• Sesame: http://www.openrdf.org/
• and many others