GraphDB Reasoning Engine
Optimal knowledge expression, reasoning, and graph data storing in GraphDB Suite allow new facts to be discovered and stored from converted data. In particular, it provides schema axiom and instance axiom for high-level and low-level reasoning, relationship reasoning, and concepts in the data model.
GraphDB Suite supports W3C‘s graph models, such as RDFS, OWL1, and OWL2. It also stores and queries the (Labeled) Property Graph model. Users can selectively use two data models, depending on their purpose.
Introduction
The GraphDB reasoning engine provides two reasoning strategies (forward-chaining and backward-chaining) for rule-based reasoning. The GraphDB reasoning engine provides the forward-chaining strategy and uses the backward-chaining one when necessary. However, the forward-chaining reasoning may be slow when uploading, storing, adding, or deleting new facts due to the inferred fact expansion after data transactions in the repository. In the GraphDB reasoning engine, reasoning begins when we input data. The forward-chaining reasoning generates and stores reasoning results for all data in advance to provide fast query and search performance. Generally, the backward-chaining reasoning method creates reasoning during query or search, when complicated deductive reasoning, conformance testing, or other reasoning is likely to occur and affect the performance.
Main Features
Graph data storing and analysis provide various analysis functions for data integration and graph data through the support of powerful knowledge expression and multiple data models. Below are the main features of graph data storing and analysis:
Main Functions and Specifications
- Reasoning function based on various rules
The graph data reasoning engine provides optimized rule sets according to the data model. It also offers RDF, OWL-Horst, OWL2-QL, and OWL2-RL. Users can optimize the rules to use these functions.
- Schema update transaction function
GraphDB can modify the schema (a namespace used to group tables that share a certain characteristic to manage them easier) if there is an incorrectly entered schema. This function is performed inside the engine to promptly reflect reasoning for the existing data according to schema changes.
- Consistency verification function
The graph data reasoning engine can check if the graph data is configured according to a schema axiom.
- Re-reasoning function
This function deletes previously or incorrectly reasoned data and re-creates new ones to ensure the consistency of graph data.
Main engine screen
