Multilingual Data Querying via Word Ontology Translation
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Solution Overview
Problem
Translating words across languages can be challenging due to differences in meaning and the absence of equivalent words, making it difficult for users with limited language knowledge to search for information in multilingual databases.
Innovation Solution
A method and system for multilingual data querying that determines the ontology of a base word, generates a set of words representing its meaning, translates these words into and from different languages, and uses an indexed database to search for data, incorporating a user interface, word ontology system, translator, and processor to facilitate effective querying across languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If direct translation of words is used from source language to target language, then translation speed is improved, but translation accuracy deteriorates due to multiple meanings and absence of equivalent words
Solution Approach 1:
The system performs preliminary actions by generating a set of candidate words representing different meanings of the base word before translation. This allows the translation process to consider multiple potential meanings upfront, selecting the most appropriate translation based on context rather than relying on direct one-to-one translation, thus improving accuracy without significantly compromising speed.
Solution Approach 2:
The system changes the parameter of word representation from a single base word to a set of candidate words with different meanings. By expanding the search space to include multiple semantic variations, the system can select the most accurate translation for the intended meaning, resolving the contradiction between speed and accuracy.
2Measurement precision
If comprehensive word ontology is generated to capture all meanings, then translation accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the comprehensive word ontology into a manageable set of candidate words representing distinct meanings of the base word. Rather than processing the entire ontology, the system divides it into relevant segments that capture the essential meanings, reducing complexity while maintaining translation accuracy.
Solution Approach 2:
The system extracts only the necessary candidate words from the complete word ontology that are relevant to the base word's meanings. By taking out only the essential elements needed for accurate translation rather than processing the full ontology, the system reduces complexity while preserving translation quality.
3Measurement precision
If multiple candidate words are generated and translated, then translation accuracy is improved, but processing time increases
Solution Approach 1:
The system performs partial action by generating and translating only a subset of candidate words that are most relevant to the base word's meanings, rather than processing all possible translations. This selective approach maintains translation accuracy for the intended meaning while reducing overall processing time by avoiding unnecessary translations of less relevant candidates.
Data Source
AI summary
In one aspect, a method for multilingual data querying, includes determining a word ontology of a base word in a source language, generating a set of words representing the word ontology of the base word, translating at least a subset of the set of words into a target language and translating the at least a subset of the set of words from the target language into the source language of the base word.