Numeric Information Retrieval Unit Auto-Detection
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Solution Overview
Problem
Information retrieval systems face challenges in determining the appropriate unit of measurement for numeric data when users enter keyword-only queries without specifying the unit, leading to inefficient search results in unstructured content sources.
Innovation Solution
A computer-based method that automatically determines the relevant unit of measure by analyzing keyword occurrences within electronic source documents using a searchable index, scoring algorithms, and natural language processing techniques, allowing for refined keyword queries and graphical output to assist users in selecting appropriate units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If keyword-only queries are used without requiring unit specification, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The system automatically determines the relevant unit of measure by analyzing the electronic source documents themselves, without requiring the user to provide unit information. The computer identifies units by examining occurrences of keywords and associated units within the document corpus, scoring these occurrences to determine the most relevant unit for the query.
Solution Approach 2:
The system pre-processes electronic source documents to create a searchable index that includes associations between keywords and units of measure. This index is built beforehand, containing extracted numeric values and their associated units, enabling rapid unit determination when a query is received without requiring real-time analysis of all documents.
2Ease of operation
If automatic unit determination is implemented, then ease of operation is improved, but device complexity increases
Solution Approach 1:
A searchable index serves as an intermediary data structure that pre-stores associations between keywords and units of measure extracted from electronic source documents. This index acts as a mediator between the raw document corpus and the query processing system, enabling efficient unit determination without requiring complex real-time analysis of all documents.
Solution Approach 2:
The system replaces manual unit specification by users with automated computer-based unit determination using natural language processing techniques and scoring algorithms. The mechanical process of users manually identifying and specifying units is substituted with automated text analysis and unit extraction from the document corpus.
3Measurement precision
If detailed analysis of keyword-unit proximity is performed, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The searchable index is constructed in advance by extracting and storing associations between keywords and units of measure from electronic source documents. This pre-processing step creates a ready-to-use data structure that enables rapid query processing without requiring time-consuming analysis of all documents when a query is received.
Solution Approach 2:
The system scores keyword-unit occurrences based on proximity and relevance, but does not require exhaustive analysis of all possible associations. By focusing on scored occurrences that meet certain thresholds and using the pre-built index, the system achieves sufficient accuracy without performing excessive analysis that would consume excessive time.
Data Source
AI summary
The present invention is related to the task of retrieving numeric information in response to a textual keyword-based query by automatically associating a unit to the type of data being retrieved. An information retrieval system is presented which suggests a unit for data exploration by leveraging the local environment of numeric data across the corpus. This local environment is parsed, including through natural language processing and proximity-based techniques, to determine units relevant to particular keyword phrases. The system also relies on knowledge of semantically and scientifically related units to optimize their binning for suggested unit scoring.