Weighted Query Matching in Multi-Field Knowledge Base
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Solution Overview
Problem
Expert systems face challenges in providing accurate and efficient responses to natural language queries across multiple fields of knowledge, as existing systems lack effective methods to prioritize answers based on the significance of query words and often fail to account for the context and frequency of words in different domains.
Innovation Solution
A multi-field expert system utilizing a search index with weighted words and a dynamic storehouse to evaluate the significance of query words, reordering them based on their relevance, and selecting the best matching answer items by traversing indexed lists, while considering the influence of less significant words and employing traversal strategies to optimize search efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a search index uses simple word matching to retrieve answers, then the retrieval speed is fast, but the relevance and accuracy of answers deteriorates
Solution Approach 1:
The patent applies parameter changes by assigning different weights to words based on their significance in the query context. Instead of treating all words equally, the system adjusts the weight parameters of words according to their importance, allowing faster retrieval while improving relevance through weighted matching algorithms.
Solution Approach 2:
The patent implements local quality by differentiating the treatment of individual words within the query. Each word is assigned a specific weight based on its local significance in the query context, enabling the system to prioritize certain words over others during matching, thus improving answer relevance without sacrificing retrieval speed.
2Measurement precision
If the system considers the significance of each query word to improve answer relevance, then the accuracy of matches improves, but the processing time and computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing word weights in a dictionary or index structure before actual query processing. This allows the system to quickly retrieve pre-computed weights during query execution without performing complex calculations in real-time, thus improving match accuracy while minimizing processing time overhead.
Solution Approach 2:
The patent segments the query processing into distinct phases: word extraction, weight retrieval from pre-computed structures, and weighted matching. This segmentation allows the system to handle complexity systematically, improving accuracy through detailed word-level analysis while managing processing time through efficient phase execution.
3Measurement precision
If the knowledge base stores detailed information about word significance and context, then the quality of answer matching improves, but the storage requirements and system complexity increases
Solution Approach 1:
The patent applies copying by creating a simplified representation of word significance data in the form of weight dictionaries or lookup tables. Instead of storing complex contextual analyses, the system copies essential weight information into efficient data structures that can be quickly accessed during query processing, reducing storage complexity while maintaining evaluation quality.
Solution Approach 2:
The patent extracts the essential information needed for word significance evaluation and separates it from the main knowledge base. By taking out weight data and storing it in dedicated structures, the system reduces overall complexity while preserving the ability to perform accurate word significance evaluation during queries.
Data Source
AI summary
A system for providing answers to questions presented in the form of electronic signals representing natural language words conveyed to said system by way of a network connected to a computer. The system includes a plurality of search indexes relating to a field of knowledge, each in a specific natural language. A store-house of natural words is associated with each one of the search indexes, and in which a list of natural words is maintained in an order reflecting the usage frequency of said words in that list. In addition, a language storehouse of natural words common to each of the search indexes associated each with a specific natural language. The search index includes a list of score ordered keywords, indexed answer items each associated with an internal list of references (ILOR) pointing to it, and a list of ordered numerical references associated with each of the ordered keywords. Each such reference represents quantitatively an association between the keyword and an indexed answer item.