Semantic Search System for Online Form Population
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Solution Overview
Problem
Traditional search engines fail to provide contextually relevant results due to unstructured data across websites, lacking the ability to understand user context and location-based queries, leading to inaccurate retrieval of data.
Innovation Solution
The implementation of a semantic search system using knowledge graphs, which integrates user knowledge graphs, IoT data, and ontologies to process search queries, translating unstructured data into a standardized format (S-P-O) and filtering results based on user context and location, utilizing digital agents and APIs to access relevant data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search engines are used to retrieve webpages, then a list of pages is returned, but the results lack contextual relevance and accuracy due to unstructured data
Solution Approach 1:
The system performs preliminary actions by pre-processing and structuring data from multiple sources into knowledge graphs before search queries are executed. User profiles, location data, and web content are organized into structured knowledge representations in advance, enabling accurate contextual matching during search without requiring complex real-time processing
Solution Approach 2:
Knowledge graphs serve as an intermediary layer between unstructured data sources and search queries. The system uses knowledge graphs to mediate between raw data (webpages, user profiles, location data) and search requests, transforming unstructured information into structured semantic representations that enable accurate contextual matching
2Adaptability or versatility
If unstructured data from multiple sources is integrated, then comprehensive data coverage is achieved, but data structure and harmony deteriorate
Solution Approach 1:
The system applies parameter changes by transforming data from various sources into a standardized structure using ontologies and schemas. Different data types (webpages, user profiles, location data) are converted into unified knowledge graph representations with consistent properties and relationships, maintaining structural stability while accommodating diverse data sources
Solution Approach 2:
The system segments data from multiple sources into distinct knowledge graph components (entities, properties, relationships) that can be independently processed and structured. By dividing complex unstructured data into manageable semantic units, the system maintains overall structure consistency while integrating diverse information sources
3Measurement precision
If user context and location data are incorporated, then search relevance is improved, but system complexity increases
Solution Approach 1:
The system merges user profiles, location data, and search queries into a unified knowledge graph framework. By combining these elements into a single structured representation, the system improves search relevance through contextual understanding while avoiding the complexity of managing separate processing systems for each data type
Data Source
AI summary
Disclosed herein are systems and methods for population of online forms based on semantic and context search. In an embodiment, a system receives a query associated with a user. The system uses a first portion of text elements in the query to select a semantic ontology and to determine a requested output type of semantic object. The system identifies semantic objects of the requested output type in a data store. The system uses a second portion of the text elements in the query to identify contextual information associated with the requested output type, and uses the contextual information to retrieve knowledge-graph data associated with the user. The system filters the identified semantic objects based on the knowledge-graph data, and presents the results. The system receives a selection of a presented result, and enters the retrieved knowledge-graph data according to the selected semantic ontology into an associated fillable online form.