Entity Linking via Confidence Scores for Social Network Queries
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Solution Overview
Problem
Social networking systems face challenges in accurately disambiguating ambiguous terms in search queries due to the multiple possible references they can have in social graphs, leading to inefficiencies in entity linking and content retrieval.
Innovation Solution
The system employs entity linking by identifying n-grams in search queries, calculating confidence scores using Segmental Conditional Random Field (CRF) modeling, and assigning entities based on these scores to improve the accuracy and efficiency of parsing queries and retrieving relevant content objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If entity linking is performed using traditional methods, then the system can process search queries, but the accuracy of disambiguating ambiguous terms is low
Solution Approach 1:
The patent segments the search query into multiple n-grams of different lengths (bigrams, trigrams, etc.) and processes each segment independently to identify potential entity mentions. This segmentation allows the system to capture ambiguous terms at different granularities, improving entity linking accuracy while maintaining efficient processing through parallel evaluation of multiple segments.
Solution Approach 2:
The patent performs preliminary action by pre-calculating and storing entity features, type features, and context features in the social networking system before query processing. When a query arrives, the system retrieves these pre-computed features and uses them to quickly calculate confidence scores, avoiding time-consuming real-time computations and thus reducing query processing time while maintaining high accuracy.
2Measurement precision
If the system analyzes context to disambiguate terms, then entity linking accuracy improves, but system complexity increases
Solution Approach 1:
The patent introduces multiple dimensional features for entity candidates including entity features (intrinsic properties), type features (categorical information), and context features (surrounding text and user profile data). By analyzing entities across these multiple dimensions simultaneously, the system achieves high disambiguation accuracy while managing complexity through structured feature organization and confidence score aggregation.
Solution Approach 2:
The patent uses confidence scores as an intermediary mechanism to bridge the complex multi-feature analysis and the final entity selection. The confidence score aggregates information from entity features, type features, and context features into a single quantitative measure, simplifying the decision-making process while maintaining the benefits of comprehensive contextual analysis.
3Measurement precision
If the system retrieves and analyzes multiple entity candidates, then entity linking accuracy improves, but computational resources increase
Solution Approach 1:
The patent retrieves and analyzes multiple entity candidates beyond what a single-best approach would provide, generating a ranked list of candidates with confidence scores. This partial excessive action ensures that the correct entity is captured even when ambiguity is high, improving accuracy while the system can truncate the candidate list at an appropriate threshold to manage computational resources.
Solution Approach 2:
The patent dynamically adjusts the number of entity candidates retrieved and the depth of feature analysis based on query characteristics, user profiles, and system state. By changing parameters such as the candidate threshold and feature computation depth, the system optimizes the balance between accuracy and computational energy consumption for different search scenarios.
Data Source
AI summary
In one embodiment, a method includes identifying one or more entity candidates matching one or more n-grams in a character string, wherein each entity candidate corresponds to an entity associated with the online social network, each entity candidate being associated with one or more entity features and one or more type features, and wherein each entity candidate is of a particular entity type; calculating, for each entity candidate, a confidence score representing a likelihood that the entity candidate is intended to match the corresponding n-gram, wherein the confidence score is based on: an entity-score based on the one or more entity features and a type-score based on the one or more type features; and assigning an entity candidate to one or more of the n-grams based on the calculated confidence scores corresponding to the n-grams.