Geotemporal Knowledge Graph Entity Proximity via Hash Prefixes
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Solution Overview
Problem
Current knowledge graph systems lack efficient methods to determine transitive geotemporal proximity between entity mentions, as they rely on explicit matching of temporal and geospatial data, failing to identify overlapping entities within a threshold proximity when data does not explicitly match.
Innovation Solution
A method is introduced to encode entity mentions in a knowledge graph with prefix-based geotemporal values, using geospatial and temporal hash prefixes to identify entities within a specified geotemporal proximity, enabling transitive proximity-based searches by mapping geospatial and temporal thresholds to hash prefixes and sorting mentions to find proximate entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If explicit matching of temporal and geospatial data is used, then data accuracy is maintained, but the ability to identify entities within threshold proximity is lost when data does not explicitly match
Solution Approach 1:
The patent transforms temporal and geospatial data into hash values with varying precision levels. By adjusting the hash prefix length, the system can dynamically change between exact matching (full hash length) and approximate matching (shorter prefix length), allowing flexible threshold proximity searches while maintaining data accuracy when needed
Solution Approach 2:
The patent introduces hash values as an intermediary representation between the original temporal and geospatial data and the proximity search query. These hash values serve as a mediator that enables efficient approximation searches without requiring direct comparison of the original precise data, thus bridging the gap between accuracy and adaptability
2Productivity
If prefix-based geotemporal encoding is implemented, then transitive proximity-based searches become efficient, but system complexity increases due to hash computation and mapping requirements
Solution Approach 1:
The patent pre-computes hash values for temporal and geospatial data during data ingestion and storage phases. This preliminary action transforms the complex data into a searchable format in advance, so that during query execution, the system only needs to perform simple hash prefix comparisons rather than complex temporal and spatial calculations, thereby improving search efficiency while managing complexity
Solution Approach 2:
The patent divides the geotemporal data into separate temporal and geospatial components, each processed independently through hash functions. This segmentation allows the system to handle each dimension separately, reducing the overall computational complexity compared to processing combined geotemporal data as a single unit, while still enabling efficient multi-dimensional proximity searches
Data Source
AI summary
A controller generating a knowledge graph of entries, each entry comprising a separate entity identifier and a separate entity mention identifier within a separate document of a corpus of documents with a located relationship and one or more computed prefix-based geotemporal values determined from geotemporal information associated with the separate entity mention identifier within the separate document. The controller, in response to receiving an input comprising a particular entity and a threshold value, mapping the threshold value to a geospatial hash prefix type and a temporal hash prefix type. The controller applying geospatial hash prefix type and the temporal hash prefix type to the entries in the knowledge graph to determine a response to the input indicating one or more geotemporal proximate entities identified within a degree of geotemporal proximity to the particular entity set by the threshold value.