Probabilistic Tree-Structured Learning for Contact Data Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In multi-tenant database systems, maintaining accurate and up-to-date contact information across shared resources is challenging due to inconsistencies and inaccuracies within organizations, as conventional approaches rely on individual maintenance.
Innovation Solution
A probabilistic tree-structured learning system is employed to extract contact data from quotes by assigning probabilistic scores and using linguistic cues to parse input strings, creating or updating records with primary and secondary entities, and utilizing training sets to determine probabilities for accurate entity identification and alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional database approaches rely on individuals to maintain their own contact information, then each individual can manage their own data, but the contact information becomes inconsistent and inaccurate across the organization
Solution Approach 1:
The system enables automatic self-updating of contact information by having individuals quote themselves in documents. The quote extraction system automatically identifies and updates contact details from quoted material, eliminating manual maintenance while ensuring accuracy through automated verification of quoted sources.
2Productivity
If a multi-tenant database system shares data resources among multiple customers, then data accessibility and collaboration improve, but maintaining accurate and up-to-date contact information becomes more challenging
Solution Approach 1:
The system implements a feedback mechanism where contact information is continuously updated based on new quoted material found in documents. The quote extraction system periodically searches for new quotes, validates them against existing contact records, and updates the database accordingly, ensuring consistency across the multi-tenant environment through automated verification loops.
3Measurement precision
If probabilistic techniques are used to extract entities from input strings, then accurate contact data can be extracted from quoted material, but the system complexity increases due to probabilistic scoring and parsing requirements
Solution Approach 1:
The system introduces a probabilistic scoring mechanism as an intermediary layer between the raw quoted material and the final contact data extraction. This scoring system evaluates multiple possible entity interpretations and selects the most probable one, mediating between ambiguous input and precise output without requiring complex deterministic parsing rules.
Data Source
AI summary
Systems and methods for updating data stored in a database, such as contact information. An input string is obtained through a search for timely material associated with the stored contact. The input string is parsed using probabilistic tendencies to extract entities corresponding to those stored with the contact. Secondary entities are used to assist in the identification of the primary entities. The contact is then updated (or added if new) using the extracted primary entities.