Abstract: The present disclosure discloses a video classification method, an electronic device and a storage medium, and relates to the field of computer technologies, and particularly to the field of artificial intelligence technologies, such as knowledge graph technologies, computer vision technologies, deep learning technologies, or the like. The video classification method includes: extracting a keyword in a video according to multi-modal information of the video; acquiring background knowledge corresponding to the keyword, and determining a text to be recognized according to the keyword and the background knowledge; and classifying the text to be recognized to obtain a class of the video.

Abstract: Technical solutions for video event recognition relate to the fields of knowledge graphs, deep learning and computer vision. A video event graph is constructed, and each event in the video event graph includes: M argument roles of the event and respective arguments of the argument roles, with M being a positive integer greater than one. For a to-be-recognized video, respective arguments of the M argument roles of a to-be-recognized event corresponding to the video are acquired. According to the arguments acquired, an event is selected from the video event graph as a recognized event corresponding to the video.

Abstract: A method for an industry text increment, as well as an electronic device and a computer readable storage medium for the same are provided. The method may include: acquiring an original industry text in a target industry field, an order of magnitude of a number of the original industry text being smaller than a preset first order of magnitude; and performing a sample incremental processing on the original industry text by using a distant supervision method, to obtain increased industry texts, an order of magnitude of a number of the increased industry texts is greater than a preset second order of magnitude, wherein the preset second order of magnitude is not smaller than the preset first order of magnitude.

Abstract: A video processing method, an electronic device and a storage medium are provided, and relate to the field of artificial intelligence, and particularly relates to the fields of deep learning, model training, knowledge mapping, video processing and the like. The method includes: acquiring a plurality of first video frames, and performing fine-grained splitting on the plurality of first video frames to obtain a plurality of second video frames; performing feature encoding on the plurality of second video frames according to multi-mode information related to the plurality of second video frames, to obtain feature fusion information for characterizing fusion of the multi-mode information; and performing similarity matching on the plurality of second video frames according to the feature fusion information, and obtaining a target video according to a result of the similarity matching.

Abstract: The present disclosure provides a medical data verification method, apparatus and an electronic device, related to a field of artificial intelligence technologies, such as AI (artificial intelligence) medical treatment, deep learning, knowledge graphs, natural language processing. A specific implementation is: obtaining medical data to be verified and a candidate document; obtaining feature vectors respectively corresponding to the medical data to be verified and the candidate document by processing the medical data to be verified and the candidate document by using a nature language processing model; obtaining N correlation vectors by calculating correlation between the medical data to be verified and the candidate document based on the feature vectors by using N methods, N being a positive integer greater than 1; and determining a confidence degree of the medical data to be verified to the candidate document by performing fusion calculation on the N correlation vectors.

Abstract: A method, apparatus, device, and storage medium for linking an entity, relates to the technical fields of knowledge graph and deep learning are provided. The method may include: acquiring a target text; determining at least one entity mention included in the target text and a candidate entity corresponding to each entity mention; determining an embedding vector of each candidate entity based on the each candidate entity and a preset entity embedding vector determination model; determining context semantic information of the target text based on the target text and each embedding vector; determining type information of the at least one entity mention; and determining an entity linking result of the at least one entity mention, based on the each embedding vector, the context semantic information, and each type information.

Abstract: A medical fact verification method and apparatus, an electronic device, and a storage medium are provided. The medical fact verification method comprises: acquiring a medical fact to be verified and candidate evidence, wherein the medical fact to be verified includes a target entity, a target attribute and a target attribute value; inputting the target entity, the target attribute value and the candidate evidence into an attribute decision model to obtain a decision attribute; inputting the target entity, the target attribute value and the candidate evidence into a relevancy decision model to obtain a relevancy of the candidate evidence in a case that the target attribute and the decision attribute are the same; and determining that the medical fact to be verified is correct in a case that the relevancy of the candidate evidence accords with a preset condition.

Abstract: A method, apparatus, device, and storage medium for constructing a knowledge graph, relates to the field of data processing, and specifically to artificial intelligence technology is provided. The method may include: determining a scene and a scene element of the scene; determining a target tag from attribute tags based on an association relationship between an entity and the scene element, and an association relationship between the entity and each of the attribute tags; and establishing an edge between a scene node and a target tag node, to obtain a knowledge graph including scene information.

Abstract: The present disclosure provides a method, a device, an equipment and a storage medium for mining a topic concept. The method includes: acquiring a plurality of candidate topic concepts based on a query; performing word segmentation on the plurality of candidate topic concepts and performing part-of-speech tagging on words obtained after performing the word segmentation, to obtain a part-of-speech sequence of each of the plurality of candidate topic concepts; and filtering the plurality of candidate topic concepts based on the part-of-speech sequence, to filter out a topic concept corresponding to a target part-of-speech sequence among the plurality of candidate topic concepts, in which a proportion of accurate topic concepts in the target part-of-speech sequence is lower than or equal to a first preset threshold, or a proportion of inaccurate topic concepts in the target part-of-speech sequence is higher than or equal to a second preset threshold.

Abstract: Provided by the present disclosure is a new category tag mining method, involving the field of knowledge graph technology, and including: obtaining a plurality of queries during a current preset time period; labeling a category tag on each query of the plurality of queries, by using a pre-trained sequence labeling model, to extract the category tag currently corresponding to the query from the query; and removing a category tag already existing in a preset current category tag library from category tags currently corresponding to all the queries, and determining a remaining category tag as a new category tag. The present disclosure also provides an electronic device and a non-transitory computer-readable storage medium.

Abstract: A theme classification method based on multimodality is related to a field of a knowledge map. The method includes obtaining text information and non-text information of an object to be classified. The non-text information includes at least one of visual information and audio information. The method also includes determining an entity set of the text information based on a pre-established knowledge base, and then extracting a text feature of the object based on the text information and the entity set. The method also includes determining a theme classification of the object based on the text feature and a non-text feature of the object.

Abstract: The disclosure provides a method for processing a video, an electronic device, and a computer storage medium. The method includes: determining a plurality of first identifiers related to a first object based on a plurality of frames including the first object in a target video; determining a plurality of attribute values associated with the plurality of first identifiers based on a knowledge base related to the first object; determining a set of frames from the plurality of frames, in which one or more attribute values associated with one or more first identifiers determined from each one of the set of frames are predetermined values; and splitting the target video into a plurality of video clips based on positions of the set of frames in the plurality of frames.

Abstract: A method, apparatus, device, and storage medium for entity linking is disclosed. The method includes: acquiring a target text; determining at least one entity mention included in the target text; determining a candidate entity corresponding to each of the entity mention based on a preset knowledge base; determining a reference text of each of the candidate entity and determining additional feature information of each of the candidate entity; and determining an entity linking result based on the target text, each of the reference text, and each piece of the additional feature information.

Abstract: A method, electronic device and storage medium for generating information are disclosed. The method includes: acquiring a plurality of tag entity words from a target video, the tag entity words including a person entity word, a work entity word, a video category entity word, and a video core entity word, the video core entity word including an entity word for characterizing a content related to the target video; linking, for a tag entity word among the plurality of tag entity words, the tag entity word to a node of a preset knowledge graph; determining semantic information of the target video based on a linking result of each of the tag entity words; and structuring the semantic information of the target video based on a relationship between the node and an edge of the knowledge graph, to obtain structured semantic information of the target video.