Building a Smart Search Engine with PHP (Simplified Approach)

This guide outlines a basic structure for a search engine using PHP that leverages Natural Language Processing (NLP) techniques for a more user-friendly experience. Due to the complexities of AI, we’ll focus on a simplified approach using pre-built libraries and sample data.

Key Functionalities:

• User Input: Users enter their search query.
• Named Entity Recognition (NER): The system identifies and classifies relevant entities (like people, locations) within the query.
• Keyword Expansion: The system expands the search query based on synonyms and related terms to improve search comprehensiveness.
• Intent Classification (Basic): The system attempts to categorize the user’s search intent (informational, transactional, etc.) using simple techniques.
• Ranked Results: Search results are retrieved from a sample data source (replace with actual database integration) and ranked based on relevance to the processed query and intent.

Disclaimer: This is a simplified example and doesn’t cover functionalities like complex AI algorithms, user accounts, or large-scale data retrieval.

Requirements:

• PHP 7.2 or higher
• Composer (for managing dependencies)

Libraries:

• Stanford CoreNLP (requires Java): https://github.com/stanfordnlp/CoreNLP (Powerful NLP toolkit)
• Synonym API (optional): (e.g., Wordnik API https://www.wordnik.com/)

Sample Data:

• We’ll use a basic array to represent a sample search data source (replace with database integration).

Steps:

1. Project Setup:
   • Create a project directory and initialize Composer with composer init.
   • Download Stanford CoreNLP following the instructions on their website.
2. Code Implementation:

<?php

// Sample search query
$query = "Where is the Eiffel Tower located?";

// Sample search data source (replace with database integration)
$dataSource = [
  'Eiffel Tower' => [
      'Description' => ' wrought-iron lattice tower on the Champ de Mars in Paris',
      'Location' => 'Paris, France'
  ],
  'The Louvre Museum' => [
      'Description' => 'world\'s largest museum',
      'Location' => 'Paris, France'
  ],
  'Great Wall of China' => [
      'Description' => 'historical fortification made of stone, brick, wood, and earth',
      'Location' => 'China'
  ],
];

// Function to process and perform search
function smartSearch($query, $dataSource) {
  // Use Stanford CoreNLP for Named Entity Recognition (NER)
  putenv('CLASSPATH=/path/to/stanford-corenlp-full-2023-10-05/stanford-corenlp-4.3.0.jar'); // Replace with your CoreNLP path
  $nlp = new StanfordCoreNLP_Load('tokenize,ssplit,pos,ner');
  $annotation = $nlp->annotate($query, ['outputFormat' => 'json']);
  $entities = json_decode($annotation, true);
  $targetEntity = null;
  
  // Identify location entity
  foreach ($entities['sentences'][0]['tokens'] as $token) {
    if ($token['ner'] == 'LOCATION') {
      $targetEntity = $token['originalText'];
      break;
    }
  }
  
  // Expand search query with synonyms (optional, replace with API call)
  $expandedQuery = $query;
  if (isset($targetEntity)) {
    $synonyms = file_get_contents('https://api.wordnik.com/v0.4/word.json/' . $targetEntity . '/synonyms?api_key=YOUR_WORDNIK_API_KEY'); // Replace with your API call and key
    $synonymsData = json_decode($synonyms, true);
    if (isset($synonymsData[0])) {
      $expandedQuery .= ' OR ' . implode(' OR ', $synonymsData[0]);
    }
  }
  
  // Basic intent classification (informational in this example)
  $intent = 'informational';
  
  // Search data source based on processed query
  $searchResults = [];
  foreach ($dataSource as $title => $details) {
    if (stripos($title, $expandedQuery) !== false || stripos(implode(' ', $details), $expandedQuery) !== false) {
      $searchResults[$title] = $details;
    }
  }
  
  // Rank results based on relevance (replace with a more comprehensive ranking

Code Explanation:

1. Setting Up:

• The code defines a sample search query and a sample search data source (replace these with user input and database integration).
• Stanford CoreNLP is assumed to be downloaded and configured. Make sure to replace the path to the JAR file (CLASSPATH) with the actual location on your system.

2. NLP with Stanford CoreNLP:

• The smartSearch function takes the query and data source as arguments.
• It sets the classpath environment variable to point to the Stanford CoreNLP JAR file (stanford-corenlp-full-2023-10-05.jar). Replace the version number with the one you downloaded.
• It creates a StanfordCoreNLP_Load object specifying the required annotations (tokenize, ssplit, pos, ner).
• The annotate method is called on the NLP object with the query and an output format (json) to get results in JSON format.
• The JSON-encoded annotation is decoded into a PHP array ($entities).

3. Named Entity Recognition (NER):

• The code iterates through the tokens in the first sentence (entities['sentences'][0]['tokens']).
• Inside the loop, it checks if the token’s NER tag ($token['ner']) is ‘LOCATION’.
• If a location entity is found ($targetEntity), the loop breaks.

4. Keyword Expansion (Optional):

• The $expandedQuery variable is initialized with the original query.
• If a location entity is found:
  • The code (commented out) simulates a Wordnik API call to retrieve synonyms for the entity. Replace this with your actual API integration and key.
  • If synonyms are found ($synonymsData), they are added to the expanded query using implode.

5. Basic Intent Classification (Informational):

• A simple assumption is made here that the user’s intent is informational for this example. In a real application, you might use more sophisticated techniques to categorize intent (navigational, transactional, etc.).

6. Search and Ranking:

• The code iterates through the data source ($dataSource).
• Inside the loop, it checks if the title or description of the data item matches the expanded query (using stripos for case-insensitive search).
• If there’s a match, the data item is added to the $searchResults array.

7. Explanation for Missing Parts:

• Ranking results based on relevance is commented out (// Rank results based on relevance...). A more comprehensive ranking algorithm would consider factors like entity matching, query keywords, and data source relevance.
• Functionality for displaying search results is also omitted for brevity. You can implement logic to display titles, descriptions, and other retrieved information.

Remember:

• This is a simplified example. Real-world implementations would involve:
  • More sophisticated NLP techniques for intent classification and query understanding.
  • Integration with a thesaurus or synonym API for keyword expansion.
  • A more comprehensive ranking algorithm considering various factors.
  • User accounts and login functionalities (if applicable).
  • Database integration for storing and retrieving search data.