Multi-Representation Indexing

Traditionally, information retrieval systems rely on a single representation of documents, often text or keywords. However, this approach can be limiting. Text alone may not capture the full meaning of a document, especially for complex topics or those requiring different analysis techniques. Multi-representation indexing addresses this limitation by utilizing multiple representations of documents during the indexing process.

Here’s why multi-representation indexing is beneficial:

• Improved retrieval accuracy: By incorporating different representations, the system can capture various aspects of the document content, leading to more relevant results for diverse queries.

• Contextual understanding: Multi-representation indexing enhances the system’s ability to understand the context in which terms are used. Semantic representations, such as embeddings from language models, can capture the nuances and relationships between terms, leading to more contextually relevant search results.

• Diverse query handling: The system can effectively process and respond to various queries, including natural language questions, keyword searches, and structured queries.

• Enhanced flexibility: Utilizing different representations allows the system to adapt to various document types, such as PDFs, web pages, and databases, as well as varying user needs.

• Handling complex information: Multi-representation indexing can be particularly helpful for documents containing complex information, like scientific papers or code, where textual analysis alone might not be sufficient.

What is multi-representation indexing?

Multi-representation indexing involves creating and storing multiple representations of each document within the retrieval system. These representations can be derived from different techniques, such as:

• Textual analysis: Extracting keywords, named entities, or using topic modeling algorithms.

• Semantic embeddings: Utilizing pre-trained LLMs to capture the semantic meaning of the text.

• Visual features: Processing images or diagrams associated with the document.