Topical Search System Context Vector Disambiguation
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Solution Overview
Problem
Traditional search engines struggle to disambiguate topics with similar names and provide accurate search results, as they rely solely on keyword matching, missing relevant information when the exact topic name is not present in the text.
Innovation Solution
A topical search system generates context vectors for each topic based on co-occurrence relationships and syntactical relationships in text data, allowing for the differentiation and disambiguation of topics with similar names by computing confidence values and ordering them by relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional keyword matching is used for search, then the search process is simple and fast, but the search accuracy deteriorates when the exact topic name is not present in the text
Solution Approach 1:
The system performs preliminary topic identification and context vector generation during the indexing phase, before actual search queries are processed. This pre-computation of topic relationships and confidence values enables accurate search without requiring complex real-time analysis, thus improving search accuracy while maintaining operational simplicity.
Solution Approach 2:
The patent introduces context vectors as an intermediary representation between keywords and topics. These vectors encode co-occurrence relationships and syntactical patterns, serving as a mediator that enables accurate topic identification even when exact keywords are absent, thereby improving search accuracy without directly increasing query processing complexity.
2Measurement precision
If context vectors with co-occurrence relationships are computed for each topic, then topic disambiguation accuracy is improved, but the processing time and computational resources increase
Solution Approach 1:
Context vectors and co-occurrence relationships are pre-computed during the indexing phase and stored for later retrieval. This preliminary computation avoids repeating expensive analyses during search operations, thus improving topic disambiguation accuracy while minimizing the time loss during actual query processing.
Solution Approach 2:
The system computes context vectors only for topics that are relevant to the query, rather than processing all topics in the database. This partial computation approach maintains high disambiguation accuracy for relevant topics while reducing overall processing time and computational resource consumption.
3Adaptability or versatility
If the system identifies topics based on co-occurrence relationships across multiple sentences, then the ability to find relevant information is improved, but the complexity of text analysis increases
Solution Approach 1:
The patent uses context vectors as an intermediary structure that captures co-occurrence relationships between topics across multiple sentences. This intermediary representation simplifies the complex task of multi-sentence analysis by pre-processing and storing relationship patterns, thereby improving information retrieval capability while reducing the apparent complexity of text analysis during search operations.
Solution Approach 2:
The system performs preliminary analysis of co-occurrence relationships across sentences during the indexing phase, storing these patterns in context vectors. This preliminary action enables the system to leverage multi-sentence context information without requiring complex real-time analysis, thus improving retrieval capability while managing analysis complexity.
Data Source
AI summary
A topical search computer system identifies topics from various definitional (i.e., data) sources. The system generates a catalog of different topics from the data sources. Topics with similar names are differentiated by the system based on the context in which each topic is used. The context for a topic is represented by a context vector, which describes the co-occurrence relationships between the topic and other topics derived from the data sources. Because the system has computed a context for each topic, the system can provide improved search results responsive to user queries for information.