Semantic Data Elevation Platform Ontology Selection
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Solution Overview
Problem
Current information processing technologies on the semantic Web lack efficiency in handling large volumes of data, requiring manual entry and not being well-adapted for considerable data flows, especially in terms of linked data processing.
Innovation Solution
A platform and method for elevating heterogeneous data sources into interconnected semantic data through a five-stage process involving ontology selection, data conversion, interconnection, and publication, utilizing a modular architecture with extensible modules for data processing, including ontology search, conversion, interconnection, and exploitation, with automatic calculation of key identifiers and metadata management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data entry processes are used for semantic Web data processing, then data accuracy can be maintained, but processing efficiency deteriorates when handling large volumes of data
Solution Approach 1:
The system automatically performs data extraction, conversion to RDF format, and ontology mapping without requiring manual intervention. The automated agents independently navigate the Web, extract semantic data, and publish it to the semantic Web, enabling the system to serve itself in the data processing pipeline.
Solution Approach 2:
Manual mechanical data entry operations are replaced with automated computational processes. Software agents automatically perform tasks that would otherwise require human operators to manually enter data into structured formats, substituting mechanical human action with automated digital processing.
2Adaptability or versatility
If existing ontology construction tools are used, then semantic processing capability is provided, but adaptability to linked data characteristics deteriorates
Solution Approach 1:
The data processing system is divided into distinct modular components: Web navigation agents, data extraction modules, RDF conversion engines, ontology mapping services, and publication mechanisms. Each module handles a specific aspect of the processing pipeline, making the overall system adaptable to different linked data characteristics while managing complexity through functional separation.
Solution Approach 2:
The system employs dynamic ontology selection and adaptation mechanisms that allow the processing pipeline to adjust to different types of linked data sources. The ontology construction and mapping processes are flexible and can be configured based on the specific characteristics of the data being processed, enabling adaptability without requiring a completely different system for each data type.
3Productivity
If data is converted to RDF format using existing tools, then semantic interoperability is achieved, but processing speed deteriorates for large data flows
Solution Approach 1:
The system performs preliminary data validation, structure analysis, and ontology selection before the actual RDF conversion process. By preparing the processing pipeline in advance and pre-configuring appropriate ontologies and mapping rules, the system reduces the time required during the actual conversion of large data flows while maintaining semantic interoperability standards.
Solution Approach 2:
The automated agents continuously navigate, extract, convert, and publish data without interruption or manual intervention. The processing pipeline operates continuously, with multiple agents working in parallel to maintain steady throughput, ensuring that large data flows are processed at high speed while consistently applying semantic interoperability rules throughout the conversion process.
Data Source
AI summary
The present invention relates to a platform and a method for the “elevation” of sources of heterogeneous data into interconnected semantic data, the platform comprising at least one “ontology selection” module for generating semantic data, said module being formed by at least one “ontology search” layer, one “ontology quality metrics” layer and one “ontological similarity measurements” layer, a “data conversion” module for converting the format of the semantic data produced by the selection module into RDF format, a “data interconnection” module for creating links between the semantic data converted to RDF format by means of a tool set, said platform containing at least one hardware and computer architecture for executing the data “elevation” process via executable instructions.