Context Engine Semantic Framework for Event Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current information analysis and knowledge management systems fail to effectively provide contextual meaning and insights from large volumes of digital data, lacking an underlying framework to manage the semantic and logical structure of information, which limits their ability to deliver true opportunities for discovery, verification, association, and prediction.
Innovation Solution
A semantic framework for an information context engine is introduced, utilizing a 'Five W' definition of event dimensions (Who, Where, When, What, Why) to establish a logical semantic structure that integrates with bi-directional HTML and URL links, enabling the discovery, exploration, and analysis of information through a circumstance-event lens and various algorithmic mechanisms for relationship establishment and validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional information analysis systems process large volumes of data, then data volume handling capability is improved, but ability to provide contextual meaning and insights deteriorates
Solution Approach 1:
The patent introduces an event lens as an intermediary layer between raw data and information retrieval systems. This event lens captures contextual information about events (who, what, when, where, why) and uses it to mediate between the user's information need and the underlying data sources, thereby preserving contextual meaning while handling large data volumes.
Solution Approach 2:
The patent adds a new dimension of event context to traditional information retrieval. By organizing information around events with multiple dimensions (actor, action, object, time, place, reason), the system transforms flat data into multi-dimensional contextual knowledge, enabling insights that go beyond simple data volume processing.
2Reliability
If existing knowledge management systems capture human learning and understanding, then community knowledge preservation is improved, but ability to analyze and interpret information for new knowledge deteriorates
Solution Approach 1:
The event lens system incorporates feedback mechanisms where retrieved events and their contexts are fed back into the system to refine future information retrieval. This creates a continuous loop where preserved knowledge is actively used to generate new insights, combining reliability of preservation with productivity of new knowledge creation.
Solution Approach 2:
The system enables self-service knowledge generation by automatically analyzing event patterns and relationships without requiring manual knowledge curation. The event lens autonomously interprets information and generates insights from captured knowledge, transforming static knowledge preservation into dynamic new knowledge production.
3Productivity
If data warehousing and data mining systems use analytic tools to uncover new knowledge, then knowledge discovery capability is improved, but difficulty in building logical models that reflect operational reality deteriorates
Solution Approach 1:
The patent segments complex data models into discrete event units with standardized contextual attributes. By breaking down operational reality into individual events (each with who, what, when, where, why components), the system simplifies logical modeling while maintaining the ability to discover patterns across segmented event data.
4Speed
If search engines provide real-time access to information using proprietary algorithms, then information access speed is improved, but ability to provide deeper meaning, logic, or insights deteriorates
Solution Approach 1:
The event lens performs preliminary action by pre-structuring information around events with explicit contextual attributes before retrieval occurs. This pre-organization of data with semantic meaning allows fast access while preserving deeper insights, as the contextual framework is already in place rather than being generated during the retrieval process.
Data Source
AI summary
The disclosure relates to a context engine that can be used to identify, align, associate, validate, anticipate, and analyze new knowledge, insights and intelligence from large amounts of digital data. The essential foundation of knowledge about events is the clear and specific definition and understanding of the circumstances of those events. The present disclosure integrates modern technological capabilities including relational database structures and processing algorithms with an underlying semantic foundation to provide the opportunity for contextual understanding, analysis and insight. In various embodiments, the context engine may be based on “Five W” event circumstances, including “Who”, “Where”, “When”, “What”, and “Why” circumstances that provide a logical semantic foundation for knowledge and understanding about modeled events, enabling users to associate those circumstances with existing information. Accordingly, the context engine may provide a substantive, intuitive, scalable, and searchable layer of context to the universe of available data and information.