Construction Knowledge Graph for Location-Based Data Linking
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Solution Overview
Problem
The construction industry faces inefficiencies due to siloed data structures, leading to loss and difficulty in locating interconnected information across disparate data sources, which are not addressed by existing solutions like common data environments.
Innovation Solution
A computing platform utilizing machine-learning models to associate construction data with physical locations, creating a construction knowledge graph that unifies data assets by location, enhancing connectivity and accessibility through predictive analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If construction data is stored in siloed structures across disparate data sources, then data can be maintained in existing systems, but information becomes difficult to locate and connect across different data sources
Solution Approach 1:
The patent merges disparate construction data from multiple siloed sources into a unified knowledge graph structure. This graph integrates data from different systems while preserving their individual characteristics, enabling connected information retrieval without consolidating all data into a single homogeneous repository.
Solution Approach 2:
The knowledge graph acts as an intermediary layer between existing siloed data sources and users. It connects disparate data sources through semantic relationships without requiring changes to the underlying data systems, enabling information connectivity while maintaining system independence.
2Ease of operation
If a common data environment is implemented to centralize construction data, then data accessibility improves, but the complexity of integrating and maintaining unified data structures increases
Solution Approach 1:
The knowledge graph serves as an intermediary that provides unified data accessibility without requiring full centralization. It connects to existing data sources through standardized interfaces, enabling easy data retrieval while avoiding the complexity of complete system consolidation.
Solution Approach 2:
The knowledge graph provides universal access to diverse construction data types through a single interface. It handles multiple data formats and sources simultaneously, enabling broad data accessibility without requiring separate systems for different data types.
3Extent of automation
If machine-learning models are used to create knowledge graphs, then predictive analytics and automated notifications are enabled, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing construction data and pre-computing relationships during data ingestion. This prepares the knowledge graph in advance, reducing the computational resources needed during runtime when generating automated notifications and predictive analytics.
Solution Approach 2:
The knowledge graph system dynamically adjusts its processing based on query complexity and data volume. It uses adaptive machine learning models that can scale computational resources according to实际需求, optimizing the balance between automation capability and resource consumption.
Data Source
AI summary
An example computing platform is configured to (i) receive a data asset related to a construction project; (ii) determine, via a first machine-learning algorithm, at least one physical location within the construction project to which the received data asset is related; (iii) associate the received data asset with the determined physical location; (iv) based on the determined physical location, determine, via a second machine-learning algorithm, a respective relationship between the received data asset and one or more other data assets related to the construction project; and (v) add the received data asset to a construction knowledge graph as a node that is connected to one or more other respective nodes that represent the one or more other data assets.