Temporal Knowledge Graph Disambiguation for Ambiguous Terms
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Solution Overview
Problem
Conventional systems fail to accurately disambiguate ambiguous terms in natural language text due to their reliance on static knowledge graphs that do not capture the temporal dimension of changing associations between entities, leading to incorrect assignments of meanings and reduced efficiency in natural language processing.
Innovation Solution
The use of context terms and both static and temporal knowledge graphs to identify the meaning of ambiguous terms by determining a time stamp and accessing a temporal knowledge graph associated with the context term to capture relevant associations at the time of the text segment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a static knowledge graph is used to disambiguate terms, then the system structure is simple, but the disambiguation accuracy deteriorates due to inability to capture temporal associations
Solution Approach 1:
The patent transforms the static knowledge graph into a dynamic temporal knowledge graph where associations between entities change over time. The system maintains multiple versions of the knowledge graph across different time periods, allowing the disambiguation process to select context-appropriate associations. This dynamic approach resolves the contradiction by making the system structure adaptable to temporal contexts while maintaining accuracy.
Solution Approach 2:
The patent adds a temporal dimension to the traditional static knowledge graph structure. By incorporating time as an additional dimension, the system creates a multi-dimensional knowledge representation that captures how associations evolve. This allows the system to maintain structural organization while significantly improving disambiguation accuracy through temporal context.
2Measurement precision
If a temporal knowledge graph is used to capture changing associations, then the disambiguation accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent segments the temporal knowledge graph into discrete time-period-specific graphs. Instead of maintaining one complex evolving graph, the system divides it into multiple manageable static snapshots, each representing associations valid during a specific time period. This segmentation reduces the complexity of managing temporal data while preserving the accuracy benefits of temporal awareness.
Solution Approach 2:
The system pre-processes and organizes knowledge graph data into time-period-specific versions before the disambiguation process. By preparing these temporal snapshots in advance, the system avoids the complexity of real-time temporal reasoning during query processing, thereby improving efficiency while maintaining accuracy.
3Ease of operation
If only static knowledge graphs are used, then the system is easier to operate, but the productivity of natural language processing decreases due to incorrect term assignments
Solution Approach 1:
The patent introduces a temporal context analysis intermediary layer between the knowledge graph and the disambiguation process. This intermediary automatically determines the relevant time period based on the query context and selects the appropriate temporal knowledge graph version. This maintains ease of operation by hiding the temporal complexity from users while improving productivity through accurate context-aware disambiguation.
4Measurement precision
If temporal dimensions are incorporated into knowledge graphs, then the accuracy of term meaning assignment is improved, but the loss of time for processing increases
Solution Approach 1:
The patent merges the temporal selection process with the existing disambiguation workflow by integrating temporal context analysis into the term processing pipeline. Rather than adding a separate time-consuming temporal analysis step, the system combines temporal graph selection with the standard disambiguation process, maintaining accuracy while minimizing additional processing time.
Data Source
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AI summary
Systems and methods provided herein for a determining a meaning of an ambiguous term in a text segment based on a context term, a static knowledge graph and a temporal knowledge graph. These systems and methods access a first knowledge graph associated with the context term to determine a potential term that is the meaning of the unknown term. Upon determining that there are multiple potential terms in the first knowledge graph that could be the meaning of the unknown term, the systems and methods take into account the temporal dimension of relationships between entities for disambiguating the meaning of the unknown term. The systems and methods achieve this by determining a time stamp of the text segment and accessing a second knowledge graph associated with the first context term and related to the time stamp to determine the potential term that is the meaning of the first unknown term.