Semantic Query Enforcement via Ontology Validation
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Solution Overview
Problem
Existing programming tools that enforce only syntactic correctness of data queries do not provide a complete solution for business intelligence (BI) tasks, as they fail to ensure that the operations provide meaningful or useful results, especially when dealing with different units of measurement or time bases.
Innovation Solution
The solution involves receiving a data query, constraining its operations to enforce semantic correctness by determining the semantic information associated with each data entity, and based on this information, allowing or preventing the combination of data entities through joining, aggregating, or grouping, using an ontology and set of axioms to ensure semantic validity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If graphical query builders are used to ensure syntactic correctness, then query execution reliability is improved, but semantic correctness cannot be guaranteed leading to meaningless results
Solution Approach 1:
The system performs preliminary semantic analysis and validation of data entities before executing the query. It determines semantic information associated with each data entity, checks compatibility between entities (e.g., currency units, time bases), and prevents combination of incompatible entities before the query runs, thereby avoiding meaningless results
Solution Approach 2:
The system introduces an intermediary semantic validation layer between the query builder and the query execution engine. This intermediary component analyzes the semantic compatibility of data entities and either permits or prevents their combination, acting as a mediator that ensures semantic correctness without blocking syntactically valid queries
2Ease of operation
If data entities are freely combined in queries, then query flexibility and ease of operation are improved, but semantic correctness deteriorates allowing incompatible data combinations
Solution Approach 1:
The system provides feedback to the user during query construction by checking semantic compatibility of selected data entities. When incompatible entities are detected (e.g., different currency units or time bases), the system prevents their combination and can guide the user to select compatible entities, maintaining both ease of operation and semantic correctness
Data Source
AI summary
Solutions for enforcing both syntactic and semantic correctness of domain-specific data queries include: receiving a data query; constraining an operation of the data query to enforce semantic correctness, wherein enforcing semantic correctness comprises: determining semantic information associated with each of a plurality of data entities; based on at least the semantic information, determining whether combining the two data entities of the plurality of data entities is allowed or is not allowed; based on at least determining that combining the two data entities is allowed, permitting combining the two data entities in the data query; and based on at least determining that combining the two data entities is not allowed, preventing combining the two data entities in the data query; and executing the data query. Some examples further include basing the permission on (at least) an ontology between the semantic information of the two data entities and a set of axioms.