System and method for analysis of graph databases using intelligent reasoning systems
A system for analyzing graph databases using intelligent reasoning systems including scalable collection of, and transformation of, graph data into facts suitable for use with programming logic languages doing deductive reasoning. A graph analyzer ingests disparate graph data from across the Internet and transforms the graph data into a fact table. In order to reduce latency and processing congestion, a stream processing engine and sharding strategy are employed to ensure scalability through parallelized processing of programming logic queries. Transformed graph data, now relational data, is utilized with programming logic languages that allow for hypothetical queries whereby an inference engine can deduce new information to satisfy such a query. Furthermore, the self-contained nature of inputs, outputs, and transformations of the system means strict data provenance can be observed and adhered to.
1 . A system for analyzing graph data using intelligent reasoning systems including scalable collection of, and transformation of, graph data into facts for use with programming logic languages for deductive reasoning, comprising:
a computing device comprising a memory, a processor, and a non-volatile data storage device;
a stream processing engine comprising programming instructions that, when executed by the processor, enable distributed processing of ontology-mediated queries to reduce latency and processing congestion;
a translation service comprising a first plurality of programming instructions stored in the memory of, and operating on the processor of, the computing device, wherein the first plurality of programming instructions, when operating on the processor, cause the computing device to:
receive a plurality of graph data from a graph-based information storage service related to an ontology-mediated query, wherein the graph data represents nodes and relationships between nodes;
for the graph data received from the graph-based information storage service, identify a storage technology and data model associated with the graph data by analyzing graph structure and data format of the graph data;
convert each the graph data to facts suitable for use with programming logic languages for deductive reasoning by translating the nodes and relationships between nodes to relational data comprising a set of facts, a bank of procedures, and a set of rules for inference, wherein the conversion is based on the identified storage technology and data model associated with the graph data to enable deductive reasoning capabilities that are not natively supported by the graph data; and
send the facts suitable for use with programming logic languages for deductive reasoning to a sharding service;
the sharding service comprising a second plurality of programming instructions stored in the memory of, and operating on the processor of, the computing device, wherein the second plurality of programming instructions, when operating on the processor, cause the computing device to:
break up the facts suitable for use with programming logic languages for deductive reasoning into chunks that are spread across multiple servers to form a fact table; and
a semantic reasoner comprising a third plurality of programming instructions stored in the memory of, and operating on the processor of, the computing device, wherein the third plurality of programming instructions, when operating on the processor, cause the computing device to:
apply the set of rules for inference to the fact table using forwards and backwards chaining techniques to deduce new information not explicitly present in the received plurality of graph data;
satisfy the ontology-mediated query by analyzing the fact table using deductive logic; and
output a result of the ontology-mediated query, wherein the result comprises the deduced new information.
2 . The system of claim 1 , further comprising an event log, wherein the event log provides a means for data provenance.
3 . The system of claim 1 , wherein the stream processing engine uses distributed computing to process the ontology-mediated query.
4 . The system of claim 1 , wherein the deduced new information is integrated into the fact table.
5 . The system of claim 1 , wherein the semantic reasoner uses the fact table to satisfy a hypothetical query.
6 . A method for analyzing graph data using intelligent reasoning systems including scalable collection of, and transformation of, graph data into facts for use with programming logic languages for deductive reasoning, comprising the steps of:
receiving a plurality of graph data from a graph-based information storage service related to an ontology-mediated query, wherein the graph data represents nodes and relationships between nodes;
managing the ontology-mediated query using a stream processing engine that enables distributed processing to reduce latency and processing congestion;
for the graph data received from the graph-based information storage service, identifying a storage technology and data model for associated with the graph data by analyzing graph structure and data format of the graph data;
converting the graph data to facts suitable for use with programming logic languages for deductive reasoning by translating the nodes and relationships between nodes to relational data comprising a set of facts, a bank of procedures, and a set of rules for inference, wherein the conversion is based on the identified storage technology and data model associated with the graph data to enable deductive reasoning capabilities that are not natively supported by the graph data;
breaking up the facts suitable for use with programming logic languages for deductive reasoning into chunks that are spread across multiple servers to form a fact table; and
applying the set of rules for inference to the fact table using forwards and backwards chaining techniques to deduce new information not explicitly present in the received plurality of graph data;
satisfying the ontology-mediated query by analyzing the fact table using deductive logic; and
outputting a result of the ontology-mediated query, wherein the result comprises the deduced new information.
7 . The method of claim 6 , further comprising an event log, wherein the event log provides a means for data provenance.
8 . The method of claim 6 , wherein the ontology-mediated query is processed by the stream processing engine using distributed computing.
9 . The method of claim 6 , wherein the deduced new information is integrated into the fact table.
10 . The method of claim 6 , wherein the fact table is used to satisfy a hypothetical query.