Virtual Document Search Indexing for Related Documents
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Solution Overview
Problem
Conventional document search systems fail to efficiently consider related documents during searches, leading to inefficient memory usage, index space consumption, and time consumption, and often result in incomplete or irrelevant search results due to the lack of a composite or virtual document representation.
Innovation Solution
The system treats related documents as a single 'virtual document' and calculates relevance scores based on term frequency, inverse document frequency, and nearness calculations across the entire virtual document, rather than individual physical documents, using an inverted index to store document identifiers and expansion identifiers that connect related documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a parent document physically includes all its related documents and is indexed, then expanded search functionality is provided, but index space is consumed and duplication occurs
Solution Approach 1:
The patent merges the parent document with its related documents (attachments, referenced documents) to form a virtual composite document for search purposes. This allows the search system to treat related documents as a unified entity without physically duplicating content in the index, thereby providing expanded search functionality while avoiding index space consumption and duplication.
Solution Approach 2:
The patent introduces a virtual document concept as an intermediary between the physical document storage system and the search system. This virtual document acts as a mediator that logically combines parent and related documents for search operations without requiring physical inclusion or duplication in the index structure.
2Adaptability or versatility
If a parent document physically includes all its related documents, then expanded search functionality is provided, but the system becomes unwieldy requiring recreation and re-indexing when related documents change
Solution Approach 1:
The patent implements a dynamic virtual document model where the composition of related documents can change without requiring recreation of the entire document structure. The virtual document dynamically references current related documents through identifiers, allowing the system to adapt to changes in attachments or referenced documents without manual re-indexing or structural recreation.
Solution Approach 2:
The patent establishes the virtual document structure in advance with references to related documents using identifiers rather than physical content. This preliminary setup allows for efficient updates when related documents change, as the system only needs to update references rather than recreate and re-index entire document structures.
3Ease of manufacture
If documents are indexed separately without considering relationships, then indexing is simple, but no single search can provide expanded search of related documents
Solution Approach 1:
The patent creates a universal search mechanism that works with both individually indexed documents and virtually combined documents. The search system can operate on simple individual document indexes while also supporting expanded searches across parent-document-attachment relationships through the virtual document concept, providing multi-functionality without sacrificing indexing simplicity.
Data Source
AI summary
Systems, methods, and other embodiments associated with logically expanding a document and determining the relevance of the logically expanded document to a query are described. One method embodiment includes searching an index to locate a document identifier for a document in which a query term appears. The method includes determining whether the index entry includes an expansion identifier, and, if so, producing a logically expanded document. The logically expanded document may include both a document associated with the document identifier and a document associated with the expansion identifier. The method may then determine a relevance value of the logically expanded document with respect to the query and may provide a signal corresponding to the relevance value.