105 results about "Knowledge area" patented technology

Systems and methods for dynamic knowledge integration in LLM systems including receiving multimodal input data comprising text, image, audio, video, and / or code data, extracting information by processing the multimodal input data through a document processor, storing the extracted information in a dynamic knowledge base, receiving a user query at a query processor, identifying knowledge domains related to the user query using domain-specific agents, retrieving real-time information from the dynamic knowledge base responsive to the identified knowledge domains, integrating the real-time information into the processing of an LLM by a dynamic knowledge integrator, and generating a response using the LLM with the real-time information.

The invention provides a multi-agent-based decision-making method and device, equipment and a medium, and the method comprises the steps: collecting data of a plurality of data sources, and constructing a plurality of knowledge bases in different knowledge fields; aiming at different task requirements contained in the target social measure, adopting a corresponding decision model and a knowledge base of a corresponding knowledge field to construct a plurality of sub-agents; and controlling the plurality of sub-agents to formulate respective preliminary social measure implementation schemes for the target social measure, and performing debate aiming at the plurality of preliminary social measure implementation schemes to obtain a final social measure implementation scheme. According to the method, decision making is carried out by integrating the multiple sub-agents representing different perspectives, multi-field and multi-dimensional insights can be formed, then social measure implementation schemes of the multiple sub-agents are fused through a debate mechanism of the multiple sub-agents, the agents can generate more comprehensive and diversified decision making results, and the decision making efficiency is improved. And complex and changeable decision problems can be effectively solved.

The invention provides an intelligent data analysis and knowledge question and answer method and system based on a multi-modal large model. The method comprises the following steps: receiving a multi-modal query request, performing semantic analysis and intention recognition technologies, determining a question core and a required knowledge field, and generating a personalized question understanding framework; performing manifold embedding processing by applying a manifold learning enhancement algorithm, revealing an internal geometric structure, constructing a shared representation space by adopting a multi-task learning technology, training a plurality of sub-tasks at the same time, and generating cross-modal unified representation; performing recurrent processing by using a recurrent neural network algorithm, recursively updating an internal state, constructing an organized information structure by using a hierarchical clustering method, and generating an ordered information structure chart; a context awareness feedback mechanism is introduced, instant response and environment change are monitored in real time, an answer strategy is dynamically adjusted, instant feedback is recorded, and an intelligent data analysis and knowledge question and answer scheme is generated. According to the technical scheme provided by the invention, the accuracy of intelligent data analysis and knowledge questions and answers and the user experience are improved.

The invention provides an intelligent question answering system construction method and device based on a lightweight large model, and the method comprises the steps: carrying out the analysis of a received natural language question, and obtaining a query intention of a user; performing positioning processing on a pre-constructed knowledge graph to obtain a knowledge node set related to the natural language problem; performing dynamic adjustment processing on internal parameter configuration of the lightweight large model to generate an optimized lightweight large model; performing answer generation processing to obtain an answer text corresponding to the natural language question; and for the answer text which does not reach the preset quality standard, triggering a secondary query process, or prompting a user to modify question description. According to the technical scheme, the response speed and accuracy of the question answering system and the flexibility of adapting to different knowledge fields can be improved.

The present application provides a multi-domain knowledge fusion method for intelligent customer service, which relates to the technical field of data processing. The method includes: determining multi-domain data sources based on big data and performing knowledge extraction, connecting multi-domain knowledge according to association mapping rules, retrieving the target needs of users, formulating a fusion strategy, screening according to the target needs, performing knowledge fusion according to the fusion strategy, complementing knowledge for the fused data according to knowledge associations, then performing knowledge domain adaptation, and optimizing according to feedback information. Through the present application, the limitations existing in the prior art in semantic understanding can be solved. For complex or ambiguous problems, the intention of the user cannot be accurately understood. The user's questions can be understood more comprehensively, the accuracy and comprehensiveness of the answers can be improved, and it can be made closer to the level of human customer service.

The invention relates to the technical field of intelligent teaching, and discloses an intelligent teaching system and method based on a large language model. The system comprises a teaching intention analysis module, a knowledge graph adaptation module, a dynamic reasoning engine module, a teaching strategy generation module and a feedback optimization module. The teaching intention analysis module is used for disassembling the teaching interaction instruction into a knowledge domain label and a teaching behavior sequence through a semantic segmentation engine; and the knowledge graph adaptation module is used for matching the subject knowledge graph and extracting an associated knowledge node set. The dynamic reasoning engine module inputs the node set into a large language model to complete multi-hop reasoning, and an intermediate state vector containing a reasoning path is generated; and the teaching strategy generation module generates a hierarchical teaching strategy in combination with the vector path weight and the behavior sequence timestamp. The feedback optimization module collects user behavior data, updates knowledge graph matching rules through incremental learning, and adapts to diversified teaching requirements.

A scalable expert foundry system enables creation, management, and coordination of multiple specialized expert domains, each developing autonomous cognitive capabilities through geometric manifold formation while maintaining hierarchical oversight and cross-domain knowledge transfer. The system utilizes a Persistent Cognitive Machine architecture with hierarchical supervisory networks that provide multi-layered coordination, conflict resolution, and quality management across distributed expert domains. Cross-domain coordinators orchestrate communication and knowledge sharing between domains through geometric abstraction and manifold projection techniques that preserve semantic integrity while enabling beneficial knowledge propagation. Executive manifold supervisors implement second-order control architectures managing meta-cognitive capabilities and system-wide reasoning strategies. The system supports enterprise deployment across multiple geographic regions with distributed computing resources. Expert domains achieve operational readiness through statistical observables monitoring including cache hit rates, distance distribution shifts, and trajectory coherence measurements that validate manifold maturity. The architecture enables scalable expert-level performance across diverse knowledge domains while maintaining coordination effectiveness and quality standards.

Existing methods for implementing artificial intelligence (AI) models for given training data heavily depend on AI expertise, meaning that organizations need to hire experts to implement the AI model. The present invention discloses a method and process for a platform that eliminates the need to be proficient in AI and coding expertise by automating much of the process. The platform automatically evaluates and selects the best algorithms and methods for each presented problem and dynamically generates tailored AI models and workflows that meet the specific requirements of the particular training data and knowledge domains. The invention may also be configured to learn from experience and continuously improve over multiple sessions.

System and method for using knowledge fabric based on knowledge ontology, designed for deep language understanding and mechanistic causal reasoning, and meta-knowledge repository for auditable question answering. The method includes receiving an input text from a user, building a knowledge graph that represents real world facts and associations in the form of contextually tagged and weighted knowledge propositions, in multiple knowledge domains. The knowledge graph in combination with causal path knowledge and metadata describing digital sources containing answers constitutes the knowledge fabric. The method includes resolving ambiguity and determining actual intent of the user for the input text, from a plurality of interpretations of intent for sentences using the knowledge graph in conjunction with logical inference to achieve deep natural language understanding. The method includes finding / delivering response to the input request as to why / how unknown factors resulted in known outcome, or what outcomes are likely given known causal factors.

Knowledge-based audio-text modeling via automatic multimodal graph construction is performed. An audio dataset is received, the audio dataset including clips of audio data, wherein each of the clips of the audio data is paired with corresponding metadata descriptive of the audio contents of the respective clip of the audio data. Graph nodes of interest are identified from a sematic network, the graph nodes being descriptive of semantics of the knowledge domain of the contents of the audio dataset. A large language model (LLM) is utilized for categorizing the metadata into the graph nodes and for inferring supplemental data for the graph nodes for which there is no metadata, producing an extracted knowledge graph. The extracted knowledge graph is validated utilizing the LLM to perform relation verification of edges between the graph nodes of the extracted knowledge graph, thereby mitigating hallucination effects in the categorizing and inferring of the supplemental data.

The invention relates to a document question-answer pair generation method and device, a question-answer method and device, computer equipment and a readable storage medium, and relates to the technical field of data generation, and the document question-answer pair generation method comprises the steps of obtaining a plurality of text slices of a document; inputting the text slices into a pre-trained knowledge graph model, and indicating the knowledge graph model to output at least one target question corresponding to the text slices based on format features and text types of the text slices; a knowledge graph associated with the knowledge field of the document is embedded in the knowledge graph model; inputting the same target question into a pre-trained question and answer model for multiple times to obtain multiple to-be-selected answers output by the question and answer model for multiple times; determining the to-be-selected answer with the maximum occurrence probability in the plurality of to-be-selected answers as a target answer matched with the corresponding target question; generating a plurality of question and answer pairs of the document based on each matched target question and target answer; and the matching accuracy of each question and answer pair is improved.

Techniques disclosed herein relate generally to constructing a customized knowledge graph. In one embodiment, entities and relations among entities are extracted from a user dataset based on certain rules to generate a seed graph. Large-scale knowledge graphs are then traversed using a finite state machine to identify candidate entities and / or relations to add to the seed graph. A priority function is used to select entities and / or relations from the candidate entities and / or relations. The selected entities and / or relations are then added to the seed graph to generate the customized knowledge graph.

The invention relates to a multi-agent collaboratively-driven power marketing question and answer method and device, computer equipment, a computer readable storage medium and a computer program product, and can be applied to the technical field of artificial intelligence. The method comprises the following steps: constructing a plurality of problem sequences according to historical interaction information; performing keyword extraction on the plurality of question sequences to obtain a first keyword set and a second keyword set; labeling the first keyword set and the second keyword set to obtain a problem data set; determining a target problem sequence; constructing a portrait vector, and constructing a category labeling sequence; inputting the portrait vector and the category labeling sequence into a knowledge domain supplementary category prediction model for processing to obtain knowledge domain supplementary category information; distributing the question task of the user to the intelligent agent of the corresponding knowledge field for processing, and obtaining a plurality of pieces of reply information; and carrying out fusion processing on the multiple pieces of reply information to obtain a query result of the user. The question and answer accuracy can be improved by adopting the method.

Context-aware content filtering adapted for a knowledge domain is provided. In certain examples, a classification confidence score by a classifier indicates a level of confidence that a prompt from a user is associated with the knowledge domain. The classification confidence score is compared with a threshold. When the score is below a threshold, a violation notice is provided to the user without submitting the prompt to a generative artificial intelligence (GAI) model. When the classification confidence score is above the threshold, the prompt is further processed to determine, according to rules, whether the prompt should be submitted to the GAI model. In various examples, the rules are applied to contextual information, safety score information, and intent information derived from the prompt.

The invention provides a question and answer model training method and apparatus, a device, a medium and a product. The method comprises the steps of obtaining a first dialogue record of a target knowledge field; wherein the first dialogue record comprises multiple rounds of dialogues that the user role and the customer service role perform questions and answers for a target question in the target knowledge field, and a target reply of the customer service role for the target question in the first dialogue record comprises target knowledge information in the target knowledge field; based on the first dialogue record, executing N rounds of sample generation processes to obtain training data; and training the target question and answer model by using the training data. According to the method, the question-asking ability of the question-answering model in multiple rounds of dialogues can be improved, and the question-answering performance of the question-answering model in the target knowledge field is enhanced.

The invention discloses a method for realizing knowledge questions and answers, which comprises the following steps of: aiming at a query statement input by a user, inputting the query statement into a trained language model, and predicting a lexical element set corresponding to a next lexical element output aiming at the query statement by utilizing the language model. And if the lexical element set comprises the first lexical element and the text corresponding to the first lexical element is the text really existing in the knowledge base corresponding to the language model, it is indicated that really related contents exist in the recall result, and an answer is output based on the text corresponding to the first lexical element included in the lexical element set. Wherein the language model is used for answering query statements for the knowledge field corresponding to the knowledge base. Visibly, through the language model provided by the application, it can be ensured that the output answer is the content really existing in the knowledge base and related to the query statement, and the accuracy of knowledge questions and answers is improved. For example, in the IT field, the reply accuracy of an IT intelligent assistant can be improved, and the efficiency is improved.

The invention provides a method for creating a knowledge base for an LLM, and the knowledge base created for the LLM comprises a knowledge graph constructed based on knowledge documents uploaded by a user. Comprising the steps of obtaining a knowledge document uploaded to a target knowledge base by a user; the user carries out user-defined modification on a first cue word template corresponding to a preset segmentation rule to obtain a first cue word corresponding to the knowledge field to which the knowledge document belongs; wherein the first prompt word is used for describing a segmentation rule customized by the user for a knowledge field to which the knowledge document belongs; sending the knowledge document and the first cue word to an LLM, so that the LLM segments the knowledge document into a plurality of document fragments based on a segmentation rule described by the first cue word; and extracting a knowledge triple from the document fragments, creating sub-knowledge maps respectively corresponding to the plurality of document fragments based on the extracted knowledge triple, and storing the sub-knowledge maps in the knowledge base.

A computer-implemented method and a smart-learning and knowledge retrieval system (SLKRS) are provided for imparting adaptive and personalized e-learning based on continually artificially learned unique characteristics of a knowledge seeker. A knowledge concept graph is generated for the knowledge seeker continually based on each of the received query and the received feedback by the smart-learning and knowledge retrieval system, thereby artificially learning unique characteristics of the knowledge seeker for measuring an ability of the knowledge seeker to learn and to show continued interest in an e-learning course. The knowledge concept graph is a cognitive blueprint of the knowledge seeker in a domain of knowledge at a point in time. The knowledge concept graph displays levels of granularity comprising one or more of interconnected concepts, categories of concepts, sub-categories of concepts, granular concepts, micro concepts, and macro concepts.

The invention relates to a human resource master data management input method based on a multi-mode large language model. The method comprises the steps that text conversion and data cleaning processing are conducted on an obtained PDF text through the optical character recognition (OCR) technology; performing category division on the acquired text data by using a four-color prototype method so as to create a knowledge domain model for subsequent analysis processing; performing information extraction processing according to information category definition logic, and constructing and deploying an open source large language model; performing compliance verification on the acquired data according to a preset requirement; analyzing the data meeting the requirements, and integrating the main data into a unified table to serve as a data warehouse for storage and analysis; performing data security management by adopting an authentication encryption technology; and the data service platform is used for human resource professionals to access and process and analyze the main data. By adopting the method provided by the invention, the optimal practice of data governance is carried out in combination with knowledge modeling, and efficient, accurate and safe management of the human resource main data is realized.

The invention discloses an intelligent AI interactive question-answering method and system applied to science popularization and a storage medium, and the method comprises the steps: constructing a science popularization knowledge spectrogram based on multi-source science popularization knowledge; extracting key feature data based on a voice or text question input by a user, and generating a question feature vector; similarity matching is carried out on the basis of the feature vectors and a node embedding space of the science popularization knowledge spectrogram, and accurate knowledge sub-graphs related to questions are extracted; after completing the response of the current question, recording a storage positioning index of the knowledge sub-graph corresponding to the question in the popular science knowledge spectrogram, and generating knowledge sub-graph index sequence data; and based on the next question of the user, carrying out correlation analysis, including questions of the same and different knowledge types, and carrying out sub-graph matching or full-graph matching, and if a plurality of knowledge fields are involved, carrying out sub-graph matching and full-graph matching. The method has the advantages that efficient knowledge matching is realized by fusing multi-source popular science knowledge and feature extraction, user questions are quickly responded, and the matching precision is continuously optimized.

The invention belongs to the technical field of computers and text data processing, and particularly discloses a medical inquiry text information processing method and device and electronic device.The method comprises the steps that a target medical inquiry text of a target object is obtained; key terms are matched in the target medical inquiry text through term knowledge bases in different knowledge fields; based on the key terms of the target medical inquiry text in different knowledge fields, constructing a field knowledge graph of the target medical inquiry text; based on the target medical inquiry text and a domain knowledge graph of the target medical inquiry text, determining a physiological obstacle level and a psychological obstacle level of the target object through a pre-trained medical inquiry text information processing model, the disorder level is used for representing the severity of the physiological disorder or the psychological disorder. Through the technical scheme provided by the invention, the accuracy of processing the medical inquiry text information can be improved.

Systems and methods for implementing domain-specific agent networks including configuring specialized agents optimized for retrieving information from a respective specific knowledge domain, receiving a user query, analyzing the user query to identify relevant knowledge domains, activating a subset of the specialized agents corresponding to the relevant knowledge domains, retrieving information by the subset of specialized agents, aggregating the retrieved information including information from a plurality of knowledge domains, providing the aggregated information to one or more h-LLMs, receiving a plurality of responses from the one or more h-LLMs, and generating a comprehensive response from the plurality of responses, the comprehensive response incorporating information from the plurality of knowledge domains.

The invention provides a model training method. The method comprises the steps of obtaining at least one question and answer pair; training the initial language model by using the at least one first question and answer pair to obtain a first language model; obtaining at least one second question text; for each second question text in the at least one second question text, replying the second question text by utilizing a first language model to obtain a plurality of candidate reply texts of the second question text; determining a second reply text for replying a quality characterization positive example sample and a third reply text for replying a quality characterization negative example sample from the plurality of candidate reply texts; according to the second question text, the second reply text and the third reply text, constructing a partial order pair of the second question text; and training the first language model by utilizing the partial order pairs corresponding to the at least one second question text to obtain a second language model. The method can improve the reply ability of the language model in the target knowledge field.

The invention provides an information processing method and device, electronic equipment, a storage medium and a program product. The method comprises the following steps: identifying a target knowledge field to which to-be-processed request information belongs; based on the request information, querying from a first database to obtain a plurality of analysis strategies of the target knowledge field; determining the matching degree between each analysis strategy and the request information; determining at least one target analysis strategy based on the matching degree between each analysis strategy and the request information and the plurality of analysis strategies; and generating reply information of the request information based on the request information and the target analysis strategy. Through the method and the device, the efficiency and the accuracy of replying the request information can be improved.

The embodiment of the invention provides a knowledge query method and device based on a large language model, equipment and a storage medium. Comprising the steps of obtaining a to-be-queried statement associated with a target knowledge field and inputting the to-be-queried statement into a target large language model to obtain a knowledge query result; wherein the target large language model is obtained by performing difference minimization training on a pre-trained large language model based on a prediction sample query result and a real sample query result, and the prediction sample query result is generated by the pre-trained large language model based on a sample embedding vector and a sample indication statement; the sample embedding vector is obtained by aggregating the first embedding vector and the second embedding vector; the sample target path is determined according to the similarity between the sample to-be-queried statement and the multiple sample candidate paths; each sample candidate path is obtained by matching entities contained in the domain knowledge graph and association relationships among the entities according to a plurality of sample element paths. Therefore, the accuracy of the query result generated by the large language model can be improved.

The embodiment of the invention discloses a knowledge graph creation method and device, electronic equipment, a storage medium and a program.The method comprises the steps that in response to a triggering operation of a target knowledge graph element, a configuration interface area of the target knowledge graph element is displayed in a target area in a knowledge graph editing interface; wherein the target knowledge graph elements comprise entity nodes or connecting lines; obtaining element configuration data input based on a configuration interface area of the target knowledge graph element, and determining graph attribute data of the target knowledge graph element according to the element configuration data; and generating the knowledge graph of the target knowledge domain according to each knowledge graph element in the knowledge graph editing interface and the graph attribute data of each knowledge graph element. According to the technical scheme, the flexibility and interactivity of the knowledge graph creating process can be improved, and then the user interaction experience is improved.

The invention provides a text analysis method and device, electronic equipment, a storage medium and a product. The method comprises the following steps: segmenting a to-be-processed text to obtain a plurality of text segments; clustering the word vectors in all the text segments, determining clustering centers to which the word vectors belong, and determining a first knowledge field of the word vector corresponding to each clustering center; the first knowledge field is used for indicating a knowledge category to which a word vector belongs; determining a vector difference between any two word vectors, and reserving the word vectors of which the vector differences are greater than a preset difference threshold in each text segment to obtain an updated text segment; based on the first knowledge field to which the word vector in each updated text segment belongs, determining a second knowledge field associated with each updated text segment; and determining the proportion of the word vectors in the second knowledge field in the to-be-processed text, and determining a field distribution result of the to-be-processed text according to the proportion.

The invention provides a text processing method and device, electronic equipment, a computer program product and a computer readable storage medium. The embodiment of the invention can be applied to scenes for carrying out standardization processing on non-standard texts in the fields of medicine, chemistry, geographic information systems and the like; the method comprises the steps of querying a plurality of candidate texts from a term library of a target knowledge domain based on a to-be-converted text of the target knowledge domain; generating a task prompt instruction based on a to-be-converted text and the plurality of candidate texts; obtaining a positive task example and a negative task example based on the to-be-converted text; and based on the positive task example, the negative task example and the task prompt instruction, converting the to-be-converted text to obtain a converted text including the term of the target knowledge domain. Through the method and the device, the accuracy of the converted text can be improved.

The application provides a kind of multi-modal knowledge retrieval method and system for construction image, the method comprises: obtaining construction image, and the relevant construction image is generated by data enhancement processing to construction image;Intelligent large model is used to analyze construction image to extract construction subject, knowledge field and application scene of construction image, and determine the knowledge base theme involved in construction image;The vectorization processing of relevant construction image is carried out using multi-modal coding model to generate semantic vector to be used as retrieval vector to recall matched first knowledge item set;The construction subject of construction image is used as query statement to recall matched second knowledge item set;Intelligent sorting recommendation is carried out to knowledge item in knowledge item set;Select top K knowledge item, according to the analysis content of construction image, construction image and top K knowledge item, build prompt project, and then use intelligent large model to analyze task again, finally output accurate and context-related answer.

An improved knowledge graph for augmenting queries in a retrieval augmented generation system for generative artificial intelligence is constructed using entity data comprising information regarding a first entity of a first entity type and a second entity of a second entity type, the first and second entity types being defined by a domain-specific ontology for a knowledge domain. Relationship data for the knowledge graph comprises information regarding relationships between the first and second entities using relationship definitions from the domain-specific ontology. The knowledge graph is constructed by adding nodes corresponding to the entities and edges corresponding to the relationships. In some examples, the graph is used to identify cybersecurity threats applicable to a specific context.