Table Completion Search Scoring Coverage Refinity
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Solution Overview
Problem
Users face inefficiencies when populating tables with multiple columns and rows, as they must conduct multiple web searches to input values, and existing search methods do not account for variations in related cell entries, leading to irrelevant results.
Innovation Solution
A system that receives an input column and search keyword, calculates coverage, refinity, and search scores for potential table columns, and provides suggested columns based on these scores, allowing for efficient table completion by identifying relevant data from a database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If users conduct multiple web searches to populate table cells, then data completeness is improved, but time consumption increases
Solution Approach 1:
The system performs preliminary actions by pre-calculating coverage scores, refinity scores, and search keyword scores for potential table columns before the user needs the data. The search engine pre-processes and ranks potential matching columns based on these scores, so when a user queries for table completion, the results are already prepared and ranked, eliminating the need for users to conduct multiple sequential web searches.
Solution Approach 2:
The system creates a virtual copy of the table completion process by generating synthetic search results through score calculations. Instead of actually performing multiple web searches, the system copies the essential functionality by computing coverage scores (matching query values to column values), refinity scores (frequency of value occurrences), and search keyword scores, then combining these to produce ranked results that mimic what would be obtained from actual web searches.
2Measurement precision
If search results are based specifically on related cell entries, then search precision is improved, but coverage of variations decreases
Solution Approach 1:
The system applies local quality by making different parts of the search process serve different functions. The coverage score component handles adaptability by matching query values against column values to ensure broad coverage of variations. The refinity score component handles precision by calculating the frequency of value occurrences within the column. The search keyword score handles relevance by incorporating the search keyword. By combining these different scoring mechanisms, the system achieves both precision and coverage of variations simultaneously.
Data Source
AI summary
Systems and techniques are provided for receiving an input column and a search keyword and providing one or more suggested columns with which to merge the input column. A coverage score and a refinity score are calculated for potential columns based on the input column as well as a search score based on the search keyword. The one or more suggested columns may be determined based on the coverage score, refinity score, and/or the search score. The input column and/or a potential column may be modified based on a function and the modification may result in a plurality of modified input and/or potential columns. Coverage, refinity, and search scores may be calculated based on the modified columns.