Set Expansion by Iterative Similarity Aggregation
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Solution Overview
Problem
Existing set expansion techniques struggle with noisy web data, particularly when the number of seeds is limited, leading to low precision and recall, and require costly and time-consuming seed-specific data extraction processes.
Innovation Solution
A set expansion system using general-purpose web data that integrates relevance and coherence measurements through an iterative process, referred to as Set Expansion by Iterative Similarity Aggregation (SEISA), to rank candidate entities and provide a reliable expansion of seed sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If seed-specific data extraction processes are used to reduce candidate sets, then precision is improved, but system complexity and processing time increase
Solution Approach 1:
The patent extracts and removes noisy candidates from the candidate set using statistical noise detection methods. By identifying and eliminating noise based on frequency analysis and statistical thresholds, the system achieves high precision without requiring complex seed-specific data extraction processes, thus resolving the contradiction between precision and system complexity.
Solution Approach 2:
The patent replaces the mechanical process of seed-specific data extraction with a computational statistical analysis approach. Instead of using complex wrappers and extraction mechanisms tailored to each seed, the system uses automated statistical noise detection and filtering algorithms that work universally across different seeds, reducing system complexity while maintaining precision.
2Productivity
If random walk based ranking techniques are used on general web data, then processing speed is improved, but precision and recall deteriorate due to noise
Solution Approach 1:
The patent applies preliminary noise detection and filtering actions before the random walk ranking process. By pre-processing the general web data to remove noisy candidates through statistical analysis, the subsequent random walk technique operates on a cleaner candidate set, maintaining processing speed while significantly improving precision and recall.
Solution Approach 2:
The patent maintains the continuous and efficient random walk process while continuously filtering out noise through statistical methods. The system preserves the beneficial continuous nature of random walk ranking while eliminating the harmful effect of noise through ongoing statistical validation, thus maintaining productivity while improving precision and recall.
3Reliability
If seed-specific wrappers are used to extract candidates, then noise is reduced, but overall system responsiveness decreases
Solution Approach 1:
The patent develops a universal statistical noise detection method that works across all seeds and data sources without requiring custom seed-specific wrappers. This multi-functional approach achieves effective noise reduction while maintaining system responsiveness, as the same statistical framework can be applied universally without the overhead of multiple specialized extraction processes.
Solution Approach 2:
The patent uses lightweight statistical filtering mechanisms that are computationally inexpensive and can be quickly applied and discarded. Instead of maintaining complex, long-lived seed-specific wrapper systems, the system employs simple, fast statistical tests that provide effective noise reduction with minimal processing overhead, preserving system responsiveness.
Data Source
AI summary
A set expansion system is described herein that uses general-purpose web data to expand a set of seed entities. The system includes a simple yet effective quality metric to measure the expanded set, and includes two iterative thresholding processes to rank candidate entities. The system models web data sources and integrates relevance and coherence measurements to evaluate potential set candidates using an iterative process. The system uses general-purpose web data that is not specific to the given seeds. The system defines quality of the result set as the sum of two component scores: the relevance of a set of entities that measures their similarity with the given seeds, and the coherence of the set of entities produced which is how closely the entities in the set are related to each other. Based on this quality measure, the system develops a class of iterative set expansion processes.