Hybrid search in the context of art historical Wikipedia data refers to a search methodology that combines different search techniques—typically
semantic search (using embeddings or meaning-based retrieval) and
traditional keyword search—to enable more accurate and relevant information retrieval. This is particularly useful for complex datasets like Wikipedia articles on art history, which often contain nuanced information about styles, periods, artists, and techniques.
- Traditional Search Techniques
- Keyword-based retrieval: Searching for exact matches of terms or phrases (e.g., "Impressionism" or "Vincent van Gogh").
- Boolean operators: Using AND, OR, and NOT to refine searches, e.g., "Cubism AND Picasso" or "Baroque NOT architecture."
- Metadata-based filtering: Leveraging structured fields like article categories, publication dates, or linked content.
- Semantic Search Techniques
- Vector-based retrieval: Articles or paragraphs are converted into embeddings using natural language processing (NLP) models (like OpenAI’s GPT or BERT) to capture the semantic meaning. For example:
- A query like "paintings with dramatic light and shadow" might retrieve articles related to Caravaggio or chiaroscuro techniques, even if those exact terms aren't in the query.
- Refine search results by shifting results towards concepts
- Hybrid Search Architecture
- Hybrid search combines scores from both approaches.
- You can balance the importance of vector-based and keyword-based results through configurable weights.
In hybrid search, the alpha parameter determines the balance between keyword-based (BM25) and vector-based search components. Setting alpha to 1 results in a pure vector search, while setting it to 0 results in a pure keyword search. Values between 0 and 1 blend the two methods, allowing you to fine-tune the search behavior to your specific needs.
Wikipedia sections are structured headings used to organize content within an article. They break the article into logical, easy-to-navigate parts.