Self-Improving Semantic Search

By Tom Arnett, Bournemouth University

Desktop: Scroll to zoom, click and drag to pan. Mobile: Pinch to zoom.

How does it work?

Traditional keyword search matches exact words, often missing the true intent behind a query. Semantic search, however, uses advanced language models to understand the meaning and context of both queries and documents. By converting text into vector embeddings, mathematical representations of meaning, the system can find relevant results even if the exact words don’t match.

Technical Details

  • Text Segmentation: Content is divided into meaningful chunks.
  • Embedding Generation: Each chunk is transformed into a dense vector using models like BERT or OpenAI embeddings.
  • Vector Indexing: Vectors are stored in a database optimized for similarity search.
  • Query Processing: User queries are embedded and compared to the index to find the most relevant matches.

Benefits Over Keyword Search

  • Understands synonyms and paraphrasing.
  • Handles ambiguous or complex queries.
  • Reduces the need for users to know specific terminology.
  • Continuously improves with user feedback.

Challenges and Limitations

  • Computational complexity of generating and storing vector embeddings.
  • Requires significant computational resources.
  • Difficulty in interpreting and explaining the model's decisions.
  • May inherit biases from training data.
  • Needs ongoing tuning and evaluation.

The Problem

Key word search requires Domain Knowledge to get accurate results.

The Solution

  • Ask questions in plain English.
  • Get accurate, site-specific answers.
  • Continuously Improves over time.
  • Site-specific tuning for higher accuracy.

Semantic Search Implementation

  • Domain content segmented into semantic chunks.
  • Chunks encoded into dense vector embeddings.
  • Stored in a vector index for efficient similarity search.

Self-Improvement Mechanism

  • User interactions and feedback collected.
  • Feedback used to fine-tune the model.
  • Model updates deployed to improve search accuracy.

References

  1. Cai, Y., Bi, K., Fan, Y., Guo, J., Chen, W., & Cheng, X. (2023). L^2R: Lifelong learning for first-stage retrieval with backward-compatible representations. [Conference/Journal details].
  2. Karri, N., & Jangam, S. K. (2024). Semantic search with AI vector search. International Journal of AI, BigData, Computational and Management Studies.
  3. Lassner, D., Brandl, S., Baillot, A., & Nakajima, S. (2023). Domain-specific word embeddings with structure prediction. Transactions of the Association for Computational Linguistics.
  4. The value of Google result positioning. (n.d.). [Publisher].