38 results about "Traditional knowledge" patented technology

The invention provides a knowledge graph embedding method based on a diverse graph attention mechanism, and the method comprises the following steps: 1, carrying out the preprocessing of knowledge graph data, and enabling a traditional knowledge graph to be preprocessed into structured data according to model requirements; 2, establishing an entity attention mechanism, wherein the entity attentionmechanism establishing mode is that the vector representation of an entity is learned on an adjacent graph by using n attention heads and self-attention through using a diverse graph attention mechanism; Step 3, establishing a relation attention mechanism in which the vector representation of the entity on the relation graph is learned by using n attention heads and self-attention by using a diverse graph attention mechanism; Step 4, carrying out modeling of multi-relation data in the relation graph; And 5, carrying out model training to obtain vectorized representations of all relationshipsin the knowledge graph.

The invention provides an entity alignment method and device. The method and device aim to solve the problem that traditional knowledge alignment method usually requires cumbersome manual labor or elaborate manual construction. The method comprises the steps of acquiring vector representations of entities in a first knowledge graph and vector representations of the entities in a second knowledge graph separately, binding vector representations, with the same meanings, of the entities in the first knowledge graph and the second knowledge graph according to an input alignment seed entity pair set so as to acquire a third knowledge graph, performing iterative calculation of the similarity among the vector representations of the entities according to the third knowledge graph, the vector representations of the entities in the first knowledge graph and the vector representations of the entities in the second knowledge graph, in the process of iterative calculation, if the distance between a certain pair of vector representations of the entities is less than a threshold, adding the entity corresponding to the pair of vector representations to the third knowledge graph until the number of entities in the third knowledge graph is no longer increased. The entity alignment method and device are good in practicality.

The invention provides a feature tensor-based Chinese knowledge graph representation learning method. The method comprises the following steps: carrying out data preparation, establishing a data structure, constructing an entity feature vector matrix, definiing relation vectors and distance formulas of marked triples, obtaining a training set, training a knowledge graph representation learning model, updating model parameters and iterative training, and carrying out relationship prediction on the unmarked triad by using the model, and carrying out iterative training again until a new unmarkedtriad cannot be learned. According to the method, feature tensors are formed by using Chinese pinyin, character information, word information and description information and are converted into featurevectors, so that a method for randomly initializing entity vectors in traditional knowledge representation learning is replaced, and Chinese features are fully utilized. Besides, a double-layer iteration mode is adopted to supplement the training corpus, so that the relation matrix can be continuously corrected, and the precision and convergence speed of the knowledge graph representation learning model are improved.

The invention relates to the technical field of computers, and discloses a topic map generation method and device suitable for text analysis or data mining, equipment and a computer storage medium. Inthis way, the invention provides a new method for enabling the application of the probability topic model LDA to reach a brand new level, so each theme can become a node in a semantic network; the complex semantic association among the topics is reflected more deep; the knowledge graph and the traditional knowledge graph are mutually corresponding and complemented; the theme map having an independent application value is achieved; Therefore, the efficiency and accuracy of traditional search and recommendation can be effectively improved, the defects of depth and visualization of traditional data mining or text analysis can be overcome, potential semantic association difficult to find by a traditional method can be mined out, real scientific discovery is achieved, and particularly, the method has a huge potential value in data mining.

The invention discloses a knowledge service innovation party driven by a polymorphic knowledge graph. The knowledge service innovation party comprises the following steps: 1) constructing a polymorphic domain knowledge graph; constructing a knowledge model, extracting and storing polymorphic knowledge, and providing a candidate scheme set for knowledge services; and 2) with an enterprise production problem task as a drive, modeling the process of solving the engineering problem of the knowledge service innovation method template from three aspects of a problem solving activity flow, a knowledge flow and a process decision flow by means of a scientific workflow model so as to support and guide the improvement activity of an enterprise in a template application layer. According to the method, a theoretical basis is provided for application of a knowledge service innovation method, and the problems of high subjectivity and high experience dependence in traditional knowledge service application are solved to a certain extent; the multi-knowledge service flexible template can be configured through the method, and a systematic innovative solving thought is provided for enterprises for complex and changeable personalized problems.

The invention discloses a knowledge distillation standing field detection method based on similarity maintenance, and belongs to the technical field of text emotion detection. The method aims to solve the problems that an existing vertical field detection method is large in computing resource and computing time expenditure and the recognition accuracy needs to be improved in the existing method. According to the method, the Text-CNN network model is utilized to carry out vertical field detection on a to-be-detected vertical field text; a Text-CNN network is determined based on a knowledge distillation mode, namely, a BERT model is used as a teacher model, and implicit knowledge learned from the teacher model is introduced into a student model text; and a loss function based on similarity maintenance is provided on the basis of a traditional knowledge distillation mode, so that the performance of the student model obtained on the basis of the knowledge distillation mode is further improved. The method is mainly used for text standing field detection.

The invention discloses a named entity recognition method based on a knowledge graph; the method carries out the reasoning of a relation indicator r and a tail entity t based on entity relation triples (h, r, t) in an existing knowledge graph. On the basis, a new model is provided to be used for representation learning among triple entity relationships extracted from an open domain knowledge base,so that relationship mining and knowledge reasoning in a knowledge graph are realized, and the effects of optimizing association search and personalized recommendation effects are achieved. Comparedwith a traditional knowledge base, the open domain knowledge base uses the relation indicator words to replace the relation types, entities are richer, and the granularity is finer and smoother.

The invention relates to the field of education big data mining, graph neural network and learner behavior modeling, and provides a knowledge tracking method based on test question heterogeneous graph representation and learner embedding, the method adopts a heterogeneous graph neural network technology in the deep learning field to represent multi-dimensional features of test questions, and meanwhile, complex learner characteristics are modeled in combination with a project reaction theory, and ability characteristics of learners are captured by adopting a clustering mode and the like; finally, the test question and learner mixed features are fused to a traditional knowledge tracking model, and knowledge tracking and learner performance prediction for different learner groups are achieved. According to the invention, the learning condition of the learner can be scientifically and comprehensively predicted, and the purpose of assisting the teacher in precise teaching is achieved.

The invention relates to a cause map with time nodes, which comprises a time axis composed of a plurality of time nodes, and event nodes respectively and correspondingly connected with the plurality of time nodes on the time axis, and the event nodes are connected with one another according to corresponding constraint conditions. The cause takes time as a node in the map rather than as an attribute of an event. Compared with a traditional knowledge graph or event graph, the cause map has obvious expandability and the calculation performance is remarkably improved, and the application effect isgood.

The invention belongs to the field of knowledge tracking, and discloses a deep embedded knowledge tracking method based on exercise difficulty and student ability, which comprises the following steps: S1, constructing a deep knowledge tracking model: integrating exercise difficulty features and student ability features into the deep knowledge tracking model; and S2, predicting student performance: fusing an attention mechanism, and predicting the student performance by using the deep knowledge tracking model constructed in the step 1. Compared with a traditional knowledge tracking method, the method has the advantages that the influence of different exercise difficulty and student ability on the answering condition of the student is considered, the model is enabled to focus more attention on answering records with similar exercise difficulty and student ability through an attention mechanism, and the prediction accuracy is improved. According to the method, the accuracy of knowledge tracking is improved, the method can be used in subsequent exercise recommendation application, and the application effect is improved.

The invention provides a disinfection antibacterial agent, which is characterized by being prepared from the following components in parts by weight: 20-40 g of safflower, 10-20 g of cymbopogon distans, 20-30 g of lithospermum, 20-30 g of acanthopanax, 10-20 g of khasi nightshade fruit and 1000 mL of alcohol. On the basis of clinical practice and summarization and analysis of traditional knowledge of traditional Chinese medicinal plants, the pure natural medicinal plant extract is successfully researched by creatively selecting safflower, cymbopogon distans and other traditional Chinese medicinal plants as main raw materials and combining modern natural product extraction technique and bioengineering fermentation technique; and the disinfection antibacterial agent has good treatment effect or rehabilitation effect in antibacterial disinfection, wound healing, skin ulceration repairing, pain alleviation, resistance against tumors and other diseases or other fields.

The invention discloses a knowledge graph question-answering system for the professional field. The system comprises a knowledge extraction module and an answer generation module, the processing process mainly comprises the following steps of extracting cross-modal collaborative professional field knowledge: performing knowledge extraction by utilizing text modal collaborative image and video modal data so as to solve the problem that a traditional knowledge extraction method is difficult to be directly applied to a professional field due to multi-source isomerism and multi-modality; questions and answers of multi-source knowledge are fused, and multi-source candidate answers are obtained through semantic matching and answer generation by utilizing professional domain knowledge information; and semantic fusion of the multi-source candidate answers, and generation of a final answer based on answer confidence reordering and answer key point reorganization of the multi-source knowledge. The knowledge graph question-answering system for the professional field solves the knowledge question-answering of multi-source heterogeneous and multi-modal data in the professional field, and is suitable for the multi-modal knowledge question-answering in other fields.

The invention provides a robot knowledge base construction system and method based on synonymous multi-context analysis, which realize the scheme from seven aspects of traditional knowledge base arrangement, question bank word segmentation, response bank word segmentation, synonymous expansion, context analysis, knowledge base indexing and new knowledge base construction. The method comprises: firstly, arranging an original knowledge base; performing word segmentation processing on the questioning sentences and the answering sentences in the knowledge base, and extracting keywords; performingsimilarity calculation and synonymy expansion on the keywords; determining adaptation scores of the keywords in the statement at the same time through context analysis, and constructing a multi-way tree index keyword at the same time; and finally, constructing a new knowledge base, and flexibly and randomly replacing synonyms in the response words by realizing similarity calculation of the synonyms, thereby realizing construction of the knowledge base with unchanged response theme content and diversified response modes.

The invention provides Kung fu noodles with tough and chewy taste and unique workmanship. The noodles are prepared from 1000 g of flour, 100 g of eggs, 15 g of salt, 50 g of white sugar and 440 g of water. The manufacturing method of the front part of India flying cakes is connected and extended, and a manufacturing method for the complete and brand new noodle variety is created. The noodles are tougher and chewier compared with most noodles; compared with traditional noodles, the noodles are stretched for many times, are light and thin and have the higher ornamental value.

The invention discloses a knowledge extraction method of a case-related property knowledge base. The method comprises the following steps: S10, constructing three knowledge extraction templates with different dimensions; S20, extracting a quintuple relationship set from the law and regulation text by utilizing the knowledge extraction templates; and S30, calculating confidence coefficients of extraction results of the three knowledge extraction templates by applying fuzzy logic, and enabling the three knowledge extraction templates to compete with one another to obtain a final extraction result. The three knowledge extraction templates are designed in different dimensions, the quintuple relationship is automatically extracted from law and regulation texts, and the confidence coefficient of the extraction result of each template is calculated by applying fuzzy logic, so that the three templates compete with one another, and compared with traditional knowledge extraction based on template matching, the accuracy of the extraction result is further improved.

The invention provides a knowledge system for HPV diagnosis. The knowledge system comprises a first knowledge base, a second knowledge base and a third knowledge base, wherein the first knowledge base is used for storing conclusion knowledge Ai; the second knowledge base is used for storing phenomenon type knowledge Bj, and the phenomenon type knowledge Bj can be arranged and combined into a plurality of phenomenon type knowledge combinations Ck; and the third knowledge base is used for storing a mapping relation Fk from the phenomenon type knowledge combination Ck to the conclusion type knowledge Ai. The knowledge system has the beneficial effects that the knowledge system abandons an information storage electronization mode of a traditional knowledge base. A classification mode of medical diagnosis and treatment guide information is provided, under the classification mode, a judgment mode which is originally formed by independent thinking of experts and field participation between phenomenon type knowledge and conclusion type knowledge is presented as a diagnosis and treatment mapping relation by computer logic, the links in which experts participate in HPV diagnosis and treatment can be copied and run efficiently through a computer program, and copying of top-level experts is achieved to a certain extent.

The invention discloses a deep knowledge-project joint tracking-based learner portraying method and application thereof. The method comprises the following steps: receiving learning behavior data from a learner, and extracting learner information and item information in the learning behavior data, including a learner identity identifier, an item identifier, an item state, a learner answer result and an original learner portrait; and enabling the extracted information to pass through a deep knowledge-project joint tracking network, and updating the learner portrait of the learner in real time. According to the invention, an integrated framework of knowledge tracking and a conceptual graph is constructed, and inaccurate user portraits caused by local missing of learning data are avoided; besides, the deep knowledge-project joint tracking in the invention overcomes the uninterpretable property of the hidden state of the traditional knowledge tracking algorithm, and integrates the course knowledge point inline state in the model, thereby further improving the accuracy of generating the learner portrait; in addition, the invention can be applied to a learning platform application scene with dynamically increased items.

The invention discloses the technical field of target detection, and particularly relates to a data self-distillation method based on batch processing knowledge ensemble (BAKE), which comprises the following steps: S1, giving a small batch of samples and a network in training, and storing similar samples; s2, avoiding self-cognition enhancement, and normalizing the affinity matrix A; s3, predicting samples in one batch, and then obtaining and forming a soft learning target; s4, realizing complete integration of knowledge. Compared with an existing knowledge integration method, the problem that traditional knowledge integration needs to be processed through an additional network or branch is solved, the needed calculation amount and memory overhead are saved, BAKE is applied to a network architecture, the classification performance of the network architecture can be improved, and wrong supervision caused in the processing process is avoided.

The invention relates to a toy schoolbag comprising a hollow schoolbag body. A plurality of shell fabrics are connected to the surface of the schoolbag body and are internally provided with branch bags, and the branch bags are connected to the schoolbag body. In the toy schoolbag disclosed by the invention, the branch bags are arranged in the shell fabrics and the schoolbag body, and the shell fabrics are used for hiding the branch bags, thus persons are not easy to find out the branch bags; furthermore, various live patterns are drawn on the surface of the shell fabrics of the toy schoolbag, thus persons pay attention to the delicate patterns, and the branch bags are further hidden with extremely strong confusion property; in addition, the bottom of the toy schoolbag is provided with a slide fastener, an interlayer is arranged in the schoolbag body, the persons are not easy to find out the slide fastener due to traditional knowledge, and other persons can not find out a lower cavity further due to the arrangement manner of the interlayer, thus the confusion property is extremely strong.