Forward Index for Efficient Document Ranking
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Solution Overview
Problem
Search engines face inefficiencies in data extraction and storage due to complex ranking functions and reverse indexes, which are time-consuming and costly, especially when dealing with large volumes of indexed documents.
Innovation Solution
Implementing a forward index as a final stage in the ranking process, which includes entries associated with documents, containing token streams, context information, and static features, to efficiently rank documents based on search queries, reducing the computational load by focusing on previously identified relevant documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex ranking functions are applied to all indexed documents, then ranking precision is improved, but computational time and cost increase significantly
Solution Approach 1:
The ranking process is divided into two distinct stages: a preliminary stage using reverse index for broad filtering and a final stage using forward index for precise ranking. This segmentation allows the system to apply complex ranking functions only to a subset of documents that have passed preliminary filtering, thereby reducing overall computational time while maintaining ranking precision for relevant documents.
Solution Approach 2:
The reverse index performs preliminary identification of potentially relevant documents before the final ranking stage. By pre-filtering documents based on basic relevance criteria stored in the reverse index, the system reduces the number of documents that require expensive complex ranking calculations, thus improving efficiency without sacrificing the quality of final rankings.
2Measurement precision
If complex ranking functions are applied to all indexed documents, then ranking precision is improved, but computational cost increases significantly
Solution Approach 1:
The ranking process is divided into two distinct stages: a preliminary stage using reverse index for broad filtering and a final stage using forward index for precise ranking. This segmentation allows the system to apply complex ranking functions only to a subset of documents that have passed preliminary filtering, thereby reducing overall computational cost while maintaining ranking precision for relevant documents.
Solution Approach 2:
The reverse index performs preliminary identification of potentially relevant documents before the final ranking stage. By pre-filtering documents based on basic relevance criteria stored in the reverse index, the system reduces the number of documents that require expensive complex ranking calculations, thus improving efficiency without sacrificing the quality of final rankings.
3Productivity
If data is stored in reverse indexes, then search query matching is improved, but data extraction efficiency deteriorates
Solution Approach 1:
The indexing system is segmented into two complementary indexes: a reverse index optimized for query matching and a forward index optimized for data extraction. The reverse index stores document information organized by query terms for efficient matching, while the forward index stores extracted features and contextual data for efficient retrieval during the final ranking stage, thus resolving the contradiction between matching efficiency and extraction efficiency.
Solution Approach 2:
The forward index acts as an intermediary structure that bridges the reverse index and the ranking functions. It receives document identifiers from the reverse index, extracts relevant features and contextual data, and prepares them for ranking calculations. This intermediary structure enables efficient data extraction without compromising the query matching capabilities of the reverse index.
Data Source
AI summary
Methods and computer storage media are provided for generating entries for documents in a forward index. A document and its document identification are received, in addition to static features that are query-independent. The document is parsed into tokens to form a token stream corresponding to the document. Relevant data used to calculate rankings of document is identified and a position of the data is determined. The entry is then generated from the document identification, the token stream of the document, the static features, and the positional information of the relevant data. The entry is stored in the forward index.