Complex Reasoning Engine
COMPLEX REASONING ENGINE
Complex Reasoning Engine
The complex reasoning engine accumulates knowledge extracted from structured and unstructured documents in the form of knowledge graphs, and generates knowledge by searching and inferring new facts based on given rules or relationships between different sets of knowledge. Especially, logic rule-based deductive reasoning and machine learning-based inductive reasoning, provided by AI Suite’s complex reasoning engine, combine together to derive new relationships from existing knowledge and use them as new knowledge.
- Ultra-capacity knowledge reasoning
The amount of computation in reasoning increases exponentially depending on the size of the knowledge to be used in reasoning and the complexity of the relationship between sets of knowledge. Saltlux’s complex reasoning engine is capable of deriving new knowledge in large quantities even in a large knowledge graph of more than 10 billion triples.
- Ultra-speed knowledge reasoning
The complex reasoning engine supports in-memory distributed reasoning based on Apache Spark, in which reasoning speed is about eight times faster than in conventional reasoning techniques. In addition, not only the batch method inference with the built-in knowledge is provided, but also dynamic incremental indexing according to the original data change and automatic real-time instantiation of the knowledge graph are provided. Thus, real-time processing of newly entered knowledge is possible.
- Various methods of complex reasoning
The complex reasoning engine supports the feature of complex multi-perspective reasoning. It supports both semantic web standards, such as RDFS and OWL-DL-level logical reasoning, and user-defined rule-based reasoning such as Prolog, SWRL, and F-Logic. And the system structure is flexible enough to optionally load a necessary knowledge base storage engine.
Main features and specifications
The complex reasoning engine provides various reasoning functions such as large-scale semantic reasoning, empirical rule-based reasoning, and spatiotemporal reasoning.
In addition, our capabilities keep expanding through relevant research such as confidence value-based probability/uncertainty inference and default inference. In addition, it integrates the knowledge base engine and applies the real-time distributed processing environment, making it possible to obtain the results inferred from ultra large amounts of data at high speed.
< Complex reasoning engine block diagram >
- Large-scale semantic reasoning
The complex reasoning engine supports RDF, RDFS, OWL, and OWL2 as a knowledge expression language that describes the knowledge data to be reasoned. Therefore, the results we provide are within the range of Description Logic supported by each knowledge expression language. DL-based reasoning mainly infers on knowledge subsumption, equivalence, consistency, satisfiability, querying, and so on, to generate new relationship information.
- Heuristic rule-based reasoning
The complex reasoning engine supports heuristic rule-based reasoning. Rule-based reasoning can infer new relationship knowledge by expressing and defining conditional constraints on relationships or attributes between classes. Typical rule-based inferences include F-logic and SWRL reasoning.
- Spatial Reasoning
The complex reasoning engine provides spatial knowledge reasoning function in which relational information between spatial information objects, including positional coordinates, are extracted to extend relational information. Positional coordinates of objects with spatial information are compared to extract the topological and directional relations between the objects and spatial relations are inferred between the objects based on the extracted topological information to generate knowledge data.