Building a Trie data structure to support typo head suggestion in Search Box

 Building a Trie data structure to support typo head suggestions is a comprehensive task. We'll start with creating a Trie data structure and then implement a search algorithm that tolerates typos.

First, we will create a TrieNode class represents each node in the Trie: ```java public class TrieNode { public TrieNode[] children; public boolean isEndOfWord; public TrieNode() { this.isEndOfWord = false; this.children = new TrieNode[26]; } } ``` Then we create the Trie class and two methods: insert and search: ```java public class Trie { private TrieNode root; public Trie() { root = new TrieNode(); } public void insert(String word) { TrieNode node = root; for (int i = 0; i < word.length(); i++) { char c = word.charAt(i); if (node.children[c - 'a'] == null) { node.children[c - 'a'] = new TrieNode(); } node = node.children[c - 'a']; } node.isEndOfWord = true; } public boolean search(String word) { return match(word, root, 0); } private boolean match(String word, TrieNode node, int start) { if (start == word.length()) { return node.isEndOfWord; } char c = word.charAt(start); if (node.children[c - 'a'] != null) { if (start == word.length() - 1 && node.children[c - 'a'].isEndOfWord) { return true; } return match(word, node.children[c - 'a'], start + 1); } else { for (int i = 0; i < 26; i++) { if (node.children[i] != null) { if (match(word, node.children[i], start + 1)) { return true; } } } } return false; } } ``` In this implementation, the search method uses a helper method called match. This match method checks for potential matches for the input word in the Trie. If a character does not match, it recursively checks all children nodes for a potential match. This is a basic implementation and may not work perfectly for all types of typos, but it gives an idea of how to implement a typo-tolerant search in a Trie data structure. For a more advanced typo-tolerant mechanism, you might want to use more sophisticated algorithms like the Levenshtein distance or BK-trees.