Semantic Query Matching for Social Network Expert Identification
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Solution Overview
Problem
Traditional search engines are limited in identifying expert users within social networks due to unstructured data, leading to tedious and unreliable searches, and they cannot access information stored in users' minds, resulting in a time-consuming burden for users to find relevant answers.
Innovation Solution
A system that semantically matches queries to structured datasets created from unstructured user-generated content and user reactions on social networks, establishing interactive communication sessions with expert users, enabling direct communication and improving search efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search engines are used to search for expert users in social networks, then search coverage is broad, but search accuracy and reliability deteriorate due to unstructured data
Solution Approach 1:
The system performs preliminary action by pre-processing and structuring user profile data from social networks before search queries are executed. User profiles are parsed, normalized, and organized into structured formats with defined schemas, making expert identification more accurate and reliable when queries are submitted.
Solution Approach 2:
The patent introduces an intermediary component that acts as a bridge between unstructured social network data and search queries. This intermediary layer processes and structures the raw data, transforming it into a format suitable for accurate matching with user queries while maintaining the benefits of comprehensive data coverage.
2Productivity
If users manually search through unstructured social network data to find experts, then no additional processing infrastructure is needed, but time consumption and user effort increase significantly
Solution Approach 1:
The system implements self-service by automatically processing user profiles and maintaining structured databases without requiring user intervention. The infrastructure continuously parses, structures, and updates user data in the background, enabling rapid expert identification when queries are submitted without burdening users with manual data processing.
Solution Approach 2:
The system performs preliminary action by pre-processing and structuring user profile data from social networks before search queries are executed. User profiles are parsed, normalized, and organized into structured formats with defined schemas, making expert identification more accurate and reliable when queries are submitted.
3Reliability
If structured datasets are created from unstructured user-generated content, then search accuracy improves, but data processing complexity and computational resources increase
Solution Approach 1:
The patent applies segmentation by dividing the complex task of structuring user-generated content into manageable components. Different modules handle specific aspects such as profile parsing, content extraction, relationship mapping, and data normalization, reducing overall system complexity while maintaining high reliability in search results.
Solution Approach 2:
The patent introduces an intermediary component that acts as a bridge between unstructured social network data and search queries. This intermediary layer processes and structures the raw data, transforming it into a format suitable for accurate matching with user queries while maintaining the benefits of comprehensive data coverage.
4Ease of operation
If traditional search engines return lists of search results, then users can browse multiple options, but users cannot directly access expert knowledge and must spend time filtering results
Solution Approach 1:
The system implements self-service by automatically processing user profiles and maintaining structured databases without requiring user intervention. The infrastructure continuously parses, structures, and updates user data in the background, enabling rapid expert identification when queries are submitted without burdening users with manual data processing.
Data Source
AI summary
There is provided a method of setting up an interactive communication session between a querying client terminal and target client terminal(s), comprising: receiving a query from the querying client terminal, semantically matching the query to a structured dataset storing structured data created from unstructured user generated content, and unstructured user reactions, extracted from posted profiles of user credentials of a social network, selecting matched user credentials of users of the social network according to an analysis of the matched structured data, distributing a request for joining an interactive communication session to matched client terminals of the matched user credentials, receiving at least one response to the request from responding client terminal(s) of at least one responding user credentials, and establishing an interactive communication session between the querying client terminal and the responding client terminal(s) of the at least one responding user credentials.