10 results about "Reasoning rule" patented technology

The application relates to a knowledge graph-based automatic attack and defense strategy generation method, device and equipment. The method comprises the following steps: constructing a multi-layer knowledge graph fusing network security knowledge; based on a preset reasoning rule set and the multi-layer knowledge graph, deducing an attack path through a graph reasoning engine to generate a candidate defense strategy; constructing a double-layer game model of an attacker and a defender, and applying a multi-objective optimization algorithm to search and optimize the candidate defense strategy to generate a defense strategy plan containing operation instructions; verifying the effectiveness of the defense strategy plan through a simulation verification environment, and deploying the defense strategy plan that passes the verification to a production environment; collecting execution feedback data of the defense strategy plan in the production environment, and dynamically adjusting the multi-layer knowledge graph and the reasoning rule set. By using the method, attack semantic understanding, intelligent path deduction, dynamic strategy generation and continuous evolution can be realized.

The application discloses a kind of LLM medical ethics compliance verification system and method based on multi-agent and RAG, including hierarchical knowledge reasoning framework, knowledge reasoning library module, multi-agent cooperation module, reasoning control module, review optimization module and iterative self-learning module, hierarchical knowledge reasoning framework constructs rule reasoning, case reasoning, scene reasoning three-level reasoning logic, knowledge reasoning library module provides knowledge and reasoning rule support for hierarchical knowledge reasoning framework, each agent of multi-agent cooperation module is embedded hierarchical knowledge reasoning sub-framework, configuration reasoning logic driven customized RAG search strategy, reasoning control module is built-in reasoning rule set version management engine, adjusts reasoning trigger threshold and calculation complexity, review optimization module supports professional review optimization reasoning decision result and reasoning rule, generates reasoning optimization data, iterative self-learning module is based on optimization data, optimizes the reasoning rule and agent search strategy of hierarchical knowledge reasoning framework.

The present application relates to the technical field of knowledge graph reasoning, and particularly relates to a knowledge reasoning method and system, comprising: receiving a user query sentence containing a reasoning rule identifier, identifying the reasoning rule identifier according to a preset rule, if the reasoning rule identifier exists, based on the reasoning rule identifier, calling rule definition information containing reasoning condition information and a top rule type from a rule database; if the reasoning condition information contains a nested reasoning rule identifier, converting it into a basic triple pattern set layer by layer through recursive analysis, and then structurally processing the set according to the top rule type to obtain intermediate query elements, combining the original query sentence of the user to generate a query sentence, and executing to obtain a reasoning result. The fixed architecture of the traditional reasoning engine is difficult to adapt to dynamics, resulting in the need to redeploy the engine after updating the rules, and the need to modify the reasoning logic when the data structure changes, thereby limiting the application flexibility and expansion capability of the reasoning engine. The present application has the effect of improving the flexibility and expansion of knowledge reasoning.

The invention discloses a business reasoning method, device and equipment, a medium and a program product. The invention relates to the technical field of computers. The method comprises the following steps: in response to a service reasoning request initiated by a user side, obtaining a target entity identifier contained in the service reasoning request; searching a target entity network matched with the target entity identifier from the knowledge graph instance according to the target entity identifier; loading a predefined SWRL inference rule set, and adopting an inference engine to execute logical inference operation in the target entity network based on the SWRL inference rule set to generate an inference result associated with the target entity identifier; optimizing and updating the knowledge graph instance based on the new entity attribute and the new entity relationship contained in the reasoning result; and a reasoning result is fed back to the user side through the application programming interface. According to the technical scheme of the invention, the accuracy and timeliness of business reasoning decision can be improved.

The invention discloses a knowledge completion method and device, equipment, a storage medium and a product, and relates to the technical field of artificial intelligence, and the knowledge completion method comprises the steps: determining indirect association information between target entities in a knowledge graph; retrieving in a preset inference rule base based on the indirect association information to obtain candidate direct relationship information corresponding to the target entity, the preset inference rule base including indirect relationships and direct relationships between entities in the knowledge graph; and inputting the candidate direct relationship information into a preset language model to obtain direct relationship information between the target entities, and complementing the knowledge graph based on the direct relationship information. Compared with the existing mode of determining the missing entity relationship in the knowledge graph by using the symmetry of the relationship, the mode of the application performs detection enhancement according to the preset inference rule base, can accurately predict the direct relationship information between the target entities, and improves the knowledge completion accuracy.

The invention discloses a space-time multi-factor fused elevator safety risk knowledge graph dynamic construction method and system. The method comprises the steps that space-time reference unification processing is conducted on multi-source heterogeneous space-time multi-factor data, attribute normalization reconstruction is completed, a standardized data set is generated, and data dimension alignment and integration are achieved. And elevator safety domain entities are extracted based on the standardized data set, space-time and domain attributes are extracted, space-time and domain association relationships among entities are constructed, and an entity-relationship system fusing space-time multiple factors is formed. Based on the system, a heterogeneous information network is built, an inference rule base is embedded, a knowledge graph ontology framework is built, concept hierarchies, attribute constraints and semantic association are defined, and building of a basic elevator safety risk knowledge graph is completed. And establishing a space-time dynamic updating mechanism based on the basic graph, and iteratively optimizing entity attributes, association relationships and inference rules according to real-time data to realize dynamic iterative construction of the knowledge graph.

The application discloses a financial risk reasoning method and system based on positive and negative association, an electronic device and a computer readable storage medium. The method comprises the following steps: acquiring a financial extension graph containing positive association information and negative association information, and a financial reasoning rule set containing a graph pattern, an antecedent predicate set and a consequent predicate; matching the rule graph pattern in the financial extension graph to obtain a matching instance; checking whether the matching instance meets the antecedent predicate, instantiating the matching instance that passes the check according to the consequent predicate to generate a candidate reasoning result including a positive or negative conclusion; performing conflict detection and resolution on the candidate reasoning result to obtain a target risk reasoning result; and outputting the target risk reasoning result and reasoning explanation information. The positive and negative evidence fusion reasoning is realized, the false positive rate is reduced through conflict detection and multi-dimensional priority resolution, and auditable explanation information is outputted, so that the fine risk control and strong supervision scene demand are met.

The invention relates to the technical field of knowledge graph reasoning, in particular to a knowledge reasoning method and system.The method comprises the steps that a user query statement containing a reasoning rule identifier is received, and the reasoning rule identifier is recognized according to a preset rule; if yes, rule definition information containing reasoning condition information and a top-layer rule type is called from a rule database based on the reasoning rule identifier; if the inference condition information contains a nested inference rule identifier, converting the inference condition information into a basic triple mode set layer by layer through recursive analysis, then performing structured processing on the set according to a top-layer rule type to obtain an intermediate query element, generating a query statement in combination with an original query statement of a user, and performing execution to obtain an inference result. The problems that due to the fact that a fixed framework of a traditional inference engine is difficult to adapt to dynamics, the engine needs to be redeployed after a rule is updated, inference logic needs to be modified when a data structure changes, and the application flexibility and expansion capacity of the inference engine are limited are solved. The method has the effect of improving the flexibility and expansibility of knowledge reasoning.

The application provides a steel ladle matching method based on rule reasoning in a steel plant, and belongs to the field of metallurgy.The method comprises the following steps: constructing a rule base of reasoning rules based on actual steel ladle matching rules;obtaining a current steelmaking production schedule and an information table of all available steel ladles, and constructing a fact base based on known facts; a reasoning engine performs forward reasoning based on the formed rule base and fact base, generates new facts, and updates the fact base until no new facts are generated; and based on the final fact base, the optimal matching steel ladle based on the rules in the rule base is reasoned out. By using the application, the steel ladle matching problem in the steel plant can be solved by the method of rule reasoning, and the application has high flexibility and high accuracy.

This invention discloses a method and apparatus for comprehensive analysis of spatial big data and knowledge graphs for natural resources, belonging to the interdisciplinary field of natural resource management and artificial intelligence. The method includes: acquiring multi-source data and preprocessing it to form a standardized dataset; constructing a natural resource domain knowledge graph containing land parcel entities (with vector boundary coordinate sequences), simplifying coordinates and automatically discovering spatial relationships using the Douglas-Puk algorithm; utilizing a hybrid reasoning engine integrating symbolic reasoning, uncertainty reasoning, and spatiotemporal reasoning, outputting conclusion confidence levels through a weighted fusion formula; performing scenario-driven deduction and outputting conclusions and influence chains; and dynamically adjusting the knowledge graph confidence level and reasoning rule weights based on feedback. This invention solves the problems of insufficient spatial and semantic fusion and single reasoning modality in existing technologies, improving the scientific rigor, foresight, and adaptability of natural resource analysis, and providing reliable decision support for scenarios such as resource planning and conflict detection.