Phonetic matching attempts to match words by pronunciation instead of spelling. Words are typically misspelled and exact matches result in them not being found.Algorithms such as Soundex and Metaphone were developed to address this problem and they have found usage in the areas of voice assistants, search, record linking and fraud detection, misspelled names of things (for example, medical records) etc.
So you have followed the Deep Dive into Neo4j’s Full Text Search tutorial, learned even how to create custom analyzers and finally watched the Full Text Search tips and tricks talk at the Nodes19 online conference?
We have already blogged about fulltext search available in Neo4j 3.5. The list of available analyzers covers many languages and fits various use cases. However once you expose the search to real users they will start pointing out edge cases and complain about the search not being google-like.
In this blog we will go over the Full Text Search capabilities available in the latest major release of Neo4j.
“Relevance is the practice of improving search results for users by satisfying their information needs in the context of a particular user experience, while balancing how ranking impacts business’s needs.” 
A great part of the world’s knowledge is stored using text in natural language, but using it in an effective way isstill a major challenge. Natural Language Processing (NLP) techniques provide the basis for harnessing this huge amountof data and converting it into a useful source of knowledge for further processing.
Last month, I had the pleasure of speaking at GraphConnect in San Francisco, introducing the Graph-Aided Search to alarge audience of Neo4j users and graph enthusiasts. For those who missed the conference, the recording and slides havenow been made available. Enjoy and get in touch with feedback / questions!
For the last couple of years, Neo4j has been increasingly popular as the technology of choice for people building real-time recommendation engines. Having been at the forefront of the graph movement through clientengagements and open-source software development, we have identified the next step in the natural evolution of graph-based recommendationengines. We call it Graph-Aided Search.