Dynamic Knowledge Graph for Inference
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Solution Overview
Problem
Current search engines and data mining systems fail to provide meaningful answers for complex knowledge queries, as they lack the ability to perform structured inference and knowledge processing, and rely on manual ontology construction, which is cumbersome and static, limiting their ability to identify unknown correlations and hidden connections between concepts across diverse sources.
Innovation Solution
A machine-implemented method that processes natural language data to build a weighted knowledge graph, using stochastic algorithms and probabilistic crawlers to detect correlations and infer connections between concepts, allowing for dynamic analysis and identification of previously unknown relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual ontology construction is used to organize knowledge, then knowledge structure can be established, but the process is cumbersome and static, limiting the ability to identify unknown correlations
Solution Approach 1:
The system automatically constructs and updates the knowledge graph by processing natural language data sources itself, without requiring manual ontology construction. The stochastic algorithms and probabilistic crawlers enable the system to self-organize knowledge structures dynamically, resolving the contradiction between reliable knowledge organization and ease of construction
Solution Approach 2:
The patent transitions from static manual ontologies to dynamic automatic knowledge graphs that continuously evolve as new data is processed. The system adapts its knowledge structure automatically through stochastic algorithms, making the knowledge organization both reliable and easily maintainable
2Loss of information
If traditional search engines are used to search for information, then document retrieval is provided, but they lack the ability to perform structured inference and identify hidden connections between concepts
Solution Approach 1:
The patent introduces a knowledge graph as an intermediary structure between traditional search engines and complex inference systems. This graph serves as a mediator that enables structured inference and connection discovery while maintaining the simplicity of query interfaces, thus improving information retrieval without requiring excessive system complexity
Solution Approach 2:
The system adds a new dimension of semantic relationships by constructing a knowledge graph that captures connections between concepts across multiple data sources. This dimensional enhancement allows the system to perform structured inference and identify hidden connections that traditional flat search engines cannot detect
3Quantity of substance
If data grows beyond centralized supervision, then data coverage increases, but it becomes difficult to find specific information and evaluate its reliability
Solution Approach 1:
The knowledge graph acts as an intermediary layer that organizes vast amounts of distributed data into a structured format with explicit relationships. This mediator enables efficient searching and reliability evaluation by providing a unified view of connections across diverse data sources, resolving the contradiction between data coverage and search difficulty
4Adaptability or versatility
If stochastic algorithms and probabilistic crawlers are used to build the knowledge graph, then dynamic analysis and unknown correlations can be identified, but the system complexity increases
Solution Approach 1:
The system uses self-organizing stochastic algorithms that automatically adapt to the data being processed without requiring complex manual configuration. The probabilistic crawlers autonomously explore and structure knowledge, providing dynamic analysis capability while keeping the system architecture relatively simple through self-service mechanisms
Data Source
AI summary
A machine-implemented method of relaying information nodes in an information network, comprising the steps of: processing a plurality of data objects according to a predefined dictionary containing a plurality of information units and a plurality of correlation-indicating elements to defect in the plurality of data objects the presence of a correlation between respective information units; establishing an information network with a plurality of information nodes and links between the information nodes, said information nodes being related to said information units and said links being related to said detected correlations; and analyzing a link connectivity state of said information network to find a path across information nodes that represent an inference or a set of inferences being input by a query searched by a user.