Ontology Directory Service for Semantic Web Content
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Solution Overview
Problem
Current search engines face challenges in efficiently retrieving relevant information from vast amounts of data due to their keyword-based search methods, often returning unrelated results, making it difficult for users to find the best information, especially with the unorganized nature of Semantic Markup Language (SML) content on the web.
Innovation Solution
An ontology directory service tool automatically discovers and manages categories from semantic web pages by preprocessing semantic data files, using a category discovery unit to identify domains and classify ontology files into inherent categories, leveraging lexical databases and natural language processing to filter and normalize keyword senses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If keyword-based search methods are used, then search engine simplicity is maintained, but information retrieval relevance deteriorates
Solution Approach 1:
The patent introduces semantic markup language (SML) as an intermediary layer between raw web content and search queries. SML tags wrap semantic units with meaningful labels (e.g., <person>, <organization>, <event>), enabling the search engine to understand contextual relationships rather than merely matching keywords. This intermediary structure bridges the gap between simple keyword search and sophisticated semantic understanding.
Solution Approach 2:
The patent transforms the search parameter from simple keyword matching to semantic unit matching. Instead of searching for exact word matches, the system searches for matches within semantically labeled units, changing the fundamental parameter of what constitutes a meaningful search unit. This allows the same keyword to be searched differently based on its semantic context.
2Measurement precision
If semantic markup language is used to organize information, then information retrieval accuracy is improved, but data organization complexity increases
Solution Approach 1:
The patent segments web content into discrete semantic units wrapped in SML tags. Each semantic unit represents a meaningful concept (person, organization, event, etc.) extracted from the text. This segmentation breaks down complex unstructured content into manageable, labeled components that can be independently processed and searched, reducing the complexity of organizing entire documents while maintaining high retrieval accuracy.
Solution Approach 2:
The patent creates a universal SML tagging system that can be applied across diverse content types and domains. The same set of semantic tags (<person>, <organization>, <event>, etc.) can label entities regardless of the source domain, making the organization system universally applicable. This multi-functionality reduces complexity by using a single standardized approach rather than domain-specific organization methods.
3Measurement precision
If manual categorization of semantic content is performed, then category accuracy is improved, but processing time increases
Solution Approach 1:
The patent implements self-service categorization where the system automatically generates categories and organizes semantic units without human intervention. The SML tags inherently contain categorical information (e.g., <person> tags automatically indicate the semantic category), allowing the system to self-organize content based on the embedded semantic structure. This eliminates time-consuming manual categorization while maintaining accuracy through the structured semantic labels.
Solution Approach 2:
The patent performs preliminary categorization by pre-tagging content with SML semantic labels during the indexing phase. Categories are established in advance through automated semantic analysis, so that when search queries are executed, the categorization work is already complete. This preliminary action eliminates the need for real-time manual categorization during search operations.
Data Source
AI summary
An ontology directory service tool, computer program product and method of automatically discovering ontology file categories. A web search unit searches a network (e.g., the Internet) for semantic data files, e.g., semantic web pages. A preprocessing unit generates an ontology file from the content of each identified semantic data file. A category discovery unit identifies a domain for each ontology file and provides training sets for training ontology file classification. A classification unit trained using the training sets, classifies ontology file instances into inherent ontology categories.