Topic-Based Search System for Sparse Media Content
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Solution Overview
Problem
Conventional keyword-based search engines require users to know exact keywords in advance and can exclude relevant results that do not contain the keyword, making them inefficient for media content searches where textual information is sparse.
Innovation Solution
A topic-based search system that maps queries to domain topics using conditional probabilities, allowing for the retrieval of relevant results based on topic similarity rather than exact keyword matching, by calculating the probability of a domain topic given a query (P(T|Q) and the probability of a result given a domain topic (P(R|T)).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keyword-based search is used, then search precision is improved, but search completeness deteriorates because relevant results without exact keywords are excluded
Solution Approach 1:
The patent introduces topic models as an intermediary layer between keywords and search results. Instead of directly matching keywords to results, the system first maps keywords to topics, then retrieves results associated with those topics. This intermediary approach allows the system to maintain keyword-based precision while achieving topic-based completeness, solving the contradiction between exact matching and comprehensive retrieval.
Solution Approach 2:
The patent transitions from one-dimensional keyword matching to a two-dimensional search space by introducing topic semantics as an additional dimension. Results are retrieved based on their topical relevance to the query, which exists in a separate semantic dimension from direct keyword occurrence. This dimensional expansion allows the system to capture relevant results that don't share exact keywords but share topical meaning.
2Reliability
If exact keyword matching is required, then search reliability is improved, but adaptability deteriorates because users must know specific keywords in advance
Solution Approach 1:
The patent performs preliminary action by pre-computing topic distributions for both queries and results using topic models before the actual search occurs. This preprocessing creates a semantic mapping framework that allows the system to adapt to different user queries without requiring users to know specific keywords. The topic models are built in advance, enabling flexible adaptation while maintaining reliable topic-based matching.
Solution Approach 2:
The patent changes the search parameter from exact keyword presence to topic probability thresholds. Instead of requiring exact keyword matches, the system retrieves results based on whether their topic distribution meets certain probability criteria relative to the query. This parameter transformation maintains search reliability through probabilistic modeling while dramatically improving adaptability to user intent.
3Loss of information
If topic-based search is implemented, then information loss is reduced, but computational complexity increases due to probability calculations
Solution Approach 1:
The patent segments the complex topic-based search problem into three distinct computational stages: (1) query topic distribution calculation, (2) result topic distribution calculation, and (3) probability-based filtering. By dividing the overall process into separate modular components, the system reduces the computational complexity of each individual stage while collectively achieving comprehensive information retention through topic-based retrieval.
Solution Approach 2:
The patent uses copying by creating simplified topic probability representations of both queries and results. Instead of performing complex semantic analysis during the actual search, the system uses pre-computed topic distributions as compact copies of the full semantic content. These copied probability vectors enable efficient comparison and filtering while preserving the essential topical information needed for accurate retrieval.
Data Source
AI summary
Systems and methods for returning results to a query based upon topic are disclosed herein. Aspects disclosed can be particularly useful when searching for videos or other media content for which associated textual information are generally relatively sparse compared to other types of content. Text associated with the query can be semantically associated with various domain topics by mapping one or more words included in the query to one or more domain topics based upon a conditional probability of the domain topic given the query. A set of results can be identified based upon a conditional probability of the result given the domain topic.