14 results about "Encyclopedia" patented technology

The application provides a kind of construction method of intelligent question answering system based on computer discipline knowledge graph, first constructs computer discipline ontology, then utilizes crawler technology to collect unstructured data such as electronic books and baidu encyclopedia, completes knowledge processing based on Colabeler tool, BiLSTM-CRF, BiLSTM-ATT, stores the extracted entity, relationship and other data to Neo4j, and finally establishes intelligent question answering system based on backend Flask framework and front-end React framework. First, the keywords of the question are extracted, and the template is matched. If no answer is matched, the relevance of the question is analyzed, and the question is stored in the relational database. The teacher answers, extracts the effective information of the teacher's answer using the knowledge extraction model, and supplements it to the knowledge graph to update the knowledge graph. The application has three characteristics of data structuring, storage refinement and intelligent question answering, which solves the problems of scattered and irregular computer discipline data, coarse discipline knowledge granularity and inaccurate search results.

The invention discloses a large language model adaptive content generation method based on a multilayer knowledge graph and preference perception learning. The method comprises the steps of constructing a hierarchical data set according to questions q and answers in an encyclopedia book, constructing a training data set according to a preset selection instruction p and the hierarchical data set, constructing an encyclopedia knowledge natural language model, training the encyclopedia knowledge natural language model according to the training data set to obtain a trained encyclopedia knowledge natural language model, and obtaining a knowledge point graph G according to the question and answer data set E, and obtaining a reasoning answer according to the knowledge point graph G, the cognitive level of the questioner, the trained encyclopedic knowledge natural language model and the question q'of the questioner. According to the method, the hierarchical data set and the multi-layer knowledge graph with the aligned cognitive levels are constructed, so that the requirements of learners with different cognitive levels are effectively met, and the problem of cognitive dislocation of a current large language model in education application is solved.

The invention discloses an interactive encyclopedia implementation method and device based on artificial intelligence, and a terminal, and belongs to the technical field of artificial intelligence education. The method comprises the following steps: constructing an encyclopedia knowledge framework in advance according to a preset classification system, collecting parent-child education videos and setting a home page display mode; accessing an artificial intelligence video understanding interface into an encyclopedia knowledge framework, and carrying out content understanding and segmentation on parent-child education videos; a user operation instruction is received to play the video, a midfield interaction link is added in the playing process, each segment node is controlled to correspond to one knowledge point, and an interaction problem is displayed; receiving feedback information of the user for the interaction problem and reminding whether the feedback information is correct or not; and collecting user behavior data, constructing a user portrait, and carrying out personalized sorting and content recommendation on encyclopedia videos in combination with a knowledge graph. The interaction of parent-child education videos is enhanced through the artificial intelligence technology, the learning effect is improved, personalized learning experience is achieved, and the learning interest and knowledge absorption efficiency of children are effectively improved.

The application provides a social user location prediction method based on a pre-trained language model and a knowledge graph, which comprises the following steps: obtaining a valid text set and a valid entity set published by a first social user; using a pre-trained language model to encode the valid text and the valid entity respectively to obtain semantic representations of the valid text and the valid entity; calculating an anonymous relationship representation between the valid entity and the corresponding valid text according to the semantic representations of the valid text and the valid entity; extracting a k-hop subgraph of the valid entity from a geographic encyclopedia knowledge graph; constructing a joint graph of the first social user according to the entity, the valid text, the valid entity, the anonymous relationship representation and the k-hop subgraph of the first social user; and predicting the location of the first social user by using a progressive relationship graph neural network according to the joint graph. The application can mine the potential correlation between the user and the location, has strong generalization, and can perform interpretable reasoning.

The present invention provides a server device, a learning assistance method, and a recording medium, which can immediately and easily confirm and effectively learn to what extent a sentence set by oneself as a learning object is searched by other users. When a user attribute of a summary object is set in a communication terminal, a user specifies a sentence and instructs "sentence search", a learning assistance server (10) searches the specified sentence with an encyclopedia, and determines whether the specified sentence is included in the top of a search ranking and whether the specified sentence is included in the top of an average search frequency ranking based on search history data of the summary object. When the specified sentence is included in the top of the search ranking, the server (10) displays a message (M1) notifying of the top of the search ranking in the summary object together with a search result of the specified sentence in the communication terminal (20), and when the specified sentence is included in the top of the average search frequency ranking, a message (M2) notifying of the top of the average search frequency ranking in the summary object is displayed.

The invention relates to the technical field of artificial intelligence and electronic commerce, and particularly discloses an encyclopedia information analysis and product matching system based on questions and answers. The system comprises a question and answer interface module, an encyclopedia information analysis module, a product matching module and an output module. The question and answer interface module is responsible for receiving and analyzing natural language questions of a user; the encyclopedia information analysis module extracts key knowledge points from the multi-source encyclopedia data; the product matching module generates a product combination scheme based on knowledge point matching; and the output module presents the result in a structured form. According to the system, accurate knowledge point extraction is realized through semantic vector calculation, a multi-dimensional evaluation system is adopted to generate a matching scheme, and personalized services are provided in combination with user portraits. According to the method, the knowledge query and the product recommendation are organically combined, the information processing efficiency and the recommendation accuracy are improved, and stable operation in a high-concurrency scene is supported. The system can be widely applied to the fields of electronic commerce, intelligent customer service and the like, and has important practical value.

The invention provides an encyclopedia corpus extraction method and device, electronic equipment, a chip and a medium, and relates to the technical field of knowledge extraction and representation. The encyclopedia corpus extraction method comprises the following steps: determining a target label through a target webpage source code; obtaining a corpus type of character information corresponding to the target tag, wherein the corpus type comprises unstructured data and structured data; if the corpus type is unstructured data, screening text content in the character information as a text of the unstructured data; and if the corpus type is structured data, extracting character information and encyclopedia information associated with the character information, and constructing a key value pair as the content of the structured data. Through the technical scheme provided by the invention, the problems of inaccuracy and low quality of the acquired encyclopedia corpus in the related technology are solved, and the accuracy and the quality of acquiring the encyclopedia corpus are improved.

The present disclosure provides an information display method, device, computer apparatus and storage medium, wherein the method comprises: receiving an access request for book encyclopedia information of a target book; acquiring the book encyclopedia information of the target book, wherein, the book encyclopedia information comprises a plurality of information modules, each information module corresponding to at least one book attribute dimension, and the book encyclopedia information belonging to each book attribute dimension is determined according to user's original innovative information and / or information obtained by automatically identifying book-related content of the target book; acquiring and displaying a book encyclopedia page matching book category of the target book, and displaying the book encyclopedia information of each information module in the book encyclopedia page.

This invention relates to a time-weighted difference-aware response generation method and system based on auto-step learning. The method includes the following steps: Step A: Collect natural, open-domain dialogues from social media software, and use a retrieval system to search for relevant background knowledge of the dialogue in an encyclopedia, and label real responses and real knowledge to construct a training dataset. DE Step B: Using the training dataset DE Step C involves training a time-weighted difference-aware deep learning network model M based on a self-synchronizing learning strategy. This model selects necessary background knowledge and generates responses based on the selected background knowledge. Step C involves inputting the dialogue context and background knowledge base into the trained deep learning network model M to generate corresponding responses. This method and system help improve the accuracy of generated responses.

This invention provides an optimization method for a temporally dependent data prediction model based on agent trajectory feedback, applicable to tasks such as webpage evidence text collection, open encyclopedia question answering, enterprise knowledge base question answering, or retrieval enhancement generation. The method collects multi-round execution trajectories formed by the agent's interaction with the retrieval system during task execution. Initial supervised samples are constructed based on the candidate document set returned by the search action and subsequent browsing actions. Positive samples are filtered using post-browsing inference text and an inference-evidence consistency judgment model. Relevance strength weights are estimated based on inference length, evidence citation count, number of fact entries, and changes in subsequent actions. The retrieval model is trained using temporally gated coding and a weighted contrastive learning objective function. The optimized retrieval model is then deployed back to the agent system for continuous closed-loop updates, improving the execution efficiency of webpage evidence collection, evidence retrieval, and complex question answering tasks.

This invention discloses a method and apparatus for generating negative samples based on lexical and semantic similarity in the Chinese domain. The method includes: extracting entities and all relations within one hop of each entity from a Chinese encyclopedia knowledge graph; concatenating the relations to form descriptive text for the entities; using Word2Vec trained on Chinese corpora to perform vector representation of the entities and descriptive text; performing similarity matching between the target matching entity and all entities, returning entities that meet the criteria as a set of semantic similarity candidate entities; calculating the edit distance between the target matching entity and all entities again, returning entities that meet the criteria as a set of lexical similarity candidate entities; and selecting high-confusion negative samples based on the semantic similarity candidate entity set and the lexical similarity candidate entity set. This invention combines lexical similarity and semantic similarity for negative sample selection, which can accelerate the convergence speed of the model and enable the model to train a good decision boundary.

The present application belongs to the technical field of knowledge mining, and particularly relates to a knowledge mining method based on cross-model and cross-language knowledge modeling. In the implementation process of the mining method, multi-language news knowledge is mined, different knowledge models constructed by the method have certain isolation and certain correlation, for different source language materials and knowledge models, a combination of a supervised knowledge extraction method and an unsupervised knowledge extraction method is adopted for knowledge extraction, and the knowledge obtained in the knowledge extraction process is mapped with the knowledge models, at the cross-language knowledge fusion level, a cross-language knowledge base is constructed based on an open source encyclopedia and an open source knowledge base, and knowledge fusion alignment is performed based on cross-language linking. Finally, cross-model and cross-language knowledge modeling and knowledge mining are completed, and a required cross-language news knowledge base is obtained.

The invention relates to the technical field of electric power intelligentization, and particularly provides an ontology recognition method and device in an electric power scene, and the method comprises the steps: carrying out the entity recognition of an input text through an entity recognition model, and obtaining a recognition result; performing encyclopedia search on the recognition result by adopting a search tool to obtain related information corresponding to the recognition result; combining the recognition result and the corresponding related information, and taking the combined result as the input of a pre-trained large language model to obtain a power ontology output by the pre-trained large language model; wherein the power ontology comprises an entity name, an entity attribute, an entity relationship, an event and an entity constraint. According to the technical scheme provided by the invention, the samples are expanded, so that the sample information is enriched, and the ontology recognition accuracy is further improved.

The present invention provides a method and system for constructing a common sense graph through human-computer fusion, comprising: Step S1: collecting relevant news data and encyclopedia data using crawler technology and integrating it with existing open-source graph data; Step S2: Based on human-computer fusion, the collected news text is annotated with entities, relationships, and rules, and the annotated data is used to jointly train a relationship extraction network and a rule matching network; Step S3: Based on the annotated data, entities and relationships are extracted from the data and a primary common sense graph is constructed; Step S4: Using crowdsourcing, the primary common sense graph is fine-tuned and modified to obtain a high-quality common sense graph; Step S5: Maintaining and evolving the high-quality common sense graph. This invention fully utilizes humans' cognitive ability for abstract knowledge rules and the rapid computing power of computers to improve the efficiency of common sense extraction and the quality of common sense information.