Simpplr Semantic Search

Table of Contents

Overview

Why Did We Build a New Search Engine?

How Does It Work?

Key Features

What's Going On Behind the Scenes? 

FAQ

Best Practices 

Overview

Simpplr’s new semantic search feature leverages cutting-edge AI technology to deliver a faster, smarter, and more relevant search experience for your intranet platform. Our search engine has been designed from the ground up to understand context, recognize relationships between concepts, and provide you with the information you need—no matter how it's phrased.

Why Did We Build a New Search Engine?

Traditional search engines rely on keyword-based searches, which match exact words and phrases but don’t fully understand their meaning or context. This can lead to frustrating results when searching for complex topics or related concepts. For example, a keyword search would struggle to differentiate between "Apple" the company and "apple" the fruit.

Simpplr’s new AI-powered semantic search shifts the paradigm from keyword-based searches to intent-driven, context-aware searches. It understands your search intent, recognizes entities and relationships, and brings you the right information at the right time.

How Does Semantic Search Work?

While keyword-based search matches exact terms, semantic search goes beyond words to analyze the meaning behind them. Here's a comparison:

Feature	Keyword-Based Search (Lexical)	Semantic Search (Embedding-Based)
How It Works	Matches query terms with document text	Converts queries & documents into vector embeddings to find semantic similarities
Search Algorithm	BM25, TF-IDF	Vector search with similarity
Handles Synonyms & Paraphrasing	❌ No (Needs explicit synonyms)	✅ Yes (Understands similar meanings)
Understands Context	❌ No (Matches exact words)	✅ Yes (Understands intent & topic relationships)
Handles Misspellings	❌ Limited (Requires fuzzy search)	✅ Yes (Embeddings capture spelling variations)
Ranks by Conceptual Relevance	❌ No (Relies on keyword frequency)	✅ Yes (Ranks by semantic similarity)

 

Real-World Example:

• Keyword-based search: Searching for “Remote Work Policy” might show irrelevant results like “Work From Home Benefits” or “Hybrid Work Guidelines,” simply because these terms don’t match exactly.

• Semantic search: When you search for “Remote Work Policy,” it also brings up documents like “Hybrid Work Guidelines” and “Work From Home Benefits” because it understands the context and the conceptual connection between the terms.
  

Key Features

Smart answer & Gen AI summary: Smart Answer provides a quick, insightful summary of the topic related to your search. Powered by advanced language models, it understands natural language, contextual queries, and even follow-up questions, delivering relevant results in real-time.

1. Context-aware filters: Refine your search results with dynamic filters such as date, author, and content type. These filters are context-aware, meaning they adapt to your search query for more personalized results. For example, if you’re searching for content related to “HR policies,” the “Author” filter will only show relevant HR team members.

2. Personalized search results: Personalization is key to improving search relevance. Based on factors like your location, department, and past interactions, Simpplr tailors your search results to be more relevant to you.

3. Unified search interface: Simpplr integrates content from various sources, including your intranet and external platforms like Google Drive, Confluence, and more. You get a unified, easy-to-navigate search interface for all your data.

4. Understanding intranet objects: Every piece of content on your intranet—whether it’s a document, video, or employee profile—has embedded attributes that help search understand its meaning and relevance. For example, employee profiles include details like department and expertise, helping search surface the right people for specific queries.

What's Going On Behind the Scenes?

Simpplr's semantic search engine has been rigorously tested and achieved outstanding results in terms of search relevance:

• nDCG (Normalized Discounted Cumulative Gain): 95%

• Precision at Rank 3: 85%

• Recall at Rank 3: 91%

These numbers show that Simpplr’s new search is an industry leader, offering accurate and relevant search results.

FAQ

1. How does Simpplr’s semantic search determine search relevance? It factors in user intent, content recency, engagement levels, popularity, and personalization based on user location, role, and department.

2. How can I improve the ranking of important content? Optimize metadata, encourage user engagement, and ensure accurate categorization to improve content visibility.

3. Why are some irrelevant results ranking higher? This can happen due to outdated content, ambiguous search terms, or permissions issues. Managing content freshness and refining your search terms can help mitigate this.

4. How does personalization affect search results? Search results are tailored to your specific location, role, department, and past interactions to provide more relevant and actionable results.

5. What can I do if important content isn’t ranking well? Improve metadata, encourage more engagement with the content, and ensure correct permissions are set for the intended audience.

6. How can I measure and improve search effectiveness? Analyze search logs to identify trends, optimize frequently searched queries, and encourage user engagement to improve ranking.

Best Practices

• People Searches: Ensure detailed user profiles with up-to-date titles, departments, and expertise.

• Site Visibility: Use clear site names, descriptions, and categories to improve search rankings.

• Content & Files: Regularly update content with relevant topics, descriptive titles, and fresh metadata to keep them ranking high.