Active Ontology Search System for Semantic Query Resolution
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Solution Overview
Problem
Conventional database search systems using keywords fail to capture synonyms and determine the high-level intent of user queries, leading to incomplete search results, particularly in advertising where advertisers rely on accurate ad visibility.
Innovation Solution
The method employs an active ontology to generate a semantic representation of search strings, using nodes and links to interpret user intent and search databases based on semantically meaningful attributes, rather than just keywords, allowing for improved advertisement indexing and prediction of relevant content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keyword-based searching is used, then the search system is simple and fast, but it fails to capture synonyms and user intent leading to incomplete results
Solution Approach 1:
The patent introduces an ontology as an intermediary layer between the user's search query and the database content. The ontology contains structured knowledge about entities, their attributes, and relationships, allowing the system to translate keywords into semantic concepts. This intermediary enables synonym recognition and intent understanding without requiring the entire system to become overly complex.
Solution Approach 2:
The search system is segmented into distinct functional components: a query processing module that generates semantic representations, an ontology module that stores domain knowledge, and a retrieval module that searches based on semantic matching. This segmentation allows each component to specialize in one aspect of the problem, improving overall accuracy while managing complexity through modular design.
2Loss of information
If semantic representation with ontology is used, then user intent and synonyms are captured improving search completeness, but the system complexity increases
Solution Approach 1:
The ontology is pre-built and populated with domain knowledge, entities, attributes, and relationships before the search process begins. This preliminary action allows the system to have ready-made semantic structures for matching, rather than constructing semantic representations in real-time during each search operation, thus reducing the complexity of the actual search process.
Solution Approach 2:
The system creates simplified semantic representations of user queries by mapping them to the pre-defined ontology structure. Instead of dealing with the full complexity of natural language understanding, the system copies and adapts the query into a standardized semantic format that matches the ontology's structure, making processing more manageable.
3Reliability
If keyword tagging by content creators is used, then content can be retrieved by exact keywords, but synonyms and related terms are not captured
Solution Approach 1:
The ontology serves multiple functions simultaneously: it acts as a thesaurus for synonym matching, a knowledge base for understanding entity relationships, and a structured framework for querying. This multi-functionality allows the system to handle exact keyword matches, synonym recognition, and intent-based searching all through the same underlying structure, improving both reliability and adaptability.
Solution Approach 2:
The search system combines multiple approaches into a composite solution: traditional keyword matching is combined with semantic concept matching through the ontology. This composite approach allows the system to leverage both the simplicity of keyword searching and the intelligence of semantic understanding, creating a more versatile and reliable search system.
Data Source
AI summary
Embodiments of the present invention provide a method and apparatus for searching using an active ontology. One embodiment of a method for searching a database includes receiving a search string, where the search string comprises one or more words, generating a semantic representation of the search string in accordance with an ontology, searching the database using the semantic representation, and outputting a result of the searching.