You are here:  Home  >  Education Resources For Use & Management of Data  >  Data Daily | Data News  >  Current Article

NTENT Solves Ambiguity with Enhanced Semantic Ranking

By   /  December 22, 2016  /  No Comments

ntby Angela Guess

A recent press release reports, “Today, NTENT announced a breakthrough in its quest to transform information discovery with the advancement of its Enhanced Semantic Ranking and Knowledge Base technologies. The adjustments and improvements to these core capabilities allow NTENT to more efficiently decipher user intent, making it faster and easier for people to find information from across the web. By applying advanced semantic ranking algorithms across a vast lexicon and custom ontology using machine learning and natural language understanding, NTENT’s Enhanced Semantic Ranking disambiguates complex queries to accurately detect user intention and deliver relevant results. Using this proprietary approach, NTENT’s disambiguation technology incorporates three stages of semantic understanding referred to as Concept Ranking, Intention Ranking and Intent Answer Ranking. Each stage works symbiotically with the other to identify concepts, derive user intentions and provide answers.”

The release continues, “Concept Ranking infers the meaning of words according to the context of a query. It applies various deduction methods to distinguish between connotations for homonyms and integrates other factors, such as user location, to deduce interpretations and produce a more accurate semantic connection. Intention Ranking correlates the inferred contextual meaning of words to the most probable user motives behind them. Once defined and understood, Intent Answer Ranking interfaces with third-party data sources to provide precise answers within a specific domain such as sports or weather. ‘For example, in the query ‘Italian Friday Manhattan’ our system interprets ‘Italian’ as a type of food rather than a person who comes from Italy,’ said Chief Executive Officer for NTENT Dan Stickel. ‘Intention Ranking characterizes the semantic relationship as a user who wants to eat Italian food on Friday in Manhattan, and then uses Intent Answer Ranking to deliver a series of pertinent answers and other useful information prioritized according to relevance.’

Read more at Business Wire.

Photo credit: NTENT

You might also like...

Monetizing Information? Show Me Your Data Model

Read More →