Semantic P2P Document Routing via Concept Similarity Metrics
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Solution Overview
Problem
Structured P2P networks face limitations in scalability and efficiency for multi-keyword or semantic-based searches, with existing systems like pSearch and GridVine experiencing issues with vector space models, latent semantic indexing, and load-balancing, leading to inefficiencies in information retrieval.
Innovation Solution
A method is introduced that computes concept similarity metrics between words in a hierarchical structured collection, generates hash values for documents, and routes them to proximally located peers in a structured P2P network, enabling efficient storage and retrieval of semantically similar documents using a Chord ring architecture with successor, predecessor, and finger tables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If structured P2P networks use traditional routing schemes (such as Chord ring), then scalability is improved, but semantic-based search efficiency deteriorates
Solution Approach 1:
The patent introduces a semantic dimension to the traditional P2P network structure by computing semantic similarity scores between documents and routing documents based on both their hash values and semantic proximity. This creates a dual-dimensional routing space that maintains the scalability of traditional P2P networks while enabling efficient semantic-based searches.
Solution Approach 2:
The patent introduces semantic similarity scores as an intermediary mechanism between traditional P2P routing and semantic-based search. By computing and utilizing these similarity scores, the system bridges the gap between structural routing efficiency and semantic search relevance, allowing documents to be routed to peers based on both location and semantic proximity.
2Loss of time
If P2P networks place semantically similar documents on proximal peers, then search efficiency is improved, but network complexity increases
Solution Approach 1:
The patent changes the routing parameter from purely hash-based to a combination of hash value and semantic similarity score. This parameter transformation enables the network to place semantically similar documents on proximal peers without fundamentally altering the underlying P2P architecture, thus improving search efficiency while controlling complexity.
3Adaptability or versatility
If existing systems use vector space models or latent semantic indexing, then semantic search capability is improved, but system complexity and computational overhead increase
Solution Approach 1:
The patent extracts the essential semantic similarity computation from complex vector space models and latent semantic indexing systems. By focusing on computing similarity scores between document terms and query terms using a simplified hierarchical collection structure, the system maintains semantic search capability while reducing computational overhead and system complexity.
Data Source
AI summary
A method of storing semantically similar documents on proximally located peers in a structured peer to peer overlay network, where each peer is assigned a unique identifier and each document includes one or more words belonging to at least one hierarchical structured collection of words. A method of searching and retrieving documents, corresponding to a search query, from a structured peer to peer overlay network is also provided.