Abstract: A tricyclic compound having the structure represented by general formula (I), a pharmaceutical composition thereof, a preparation method therefor and the use thereof in the prevention and treatment of a variety of diseases caused by toxic aldehydes.

Abstract: A method and a system for recommending a target garment matching an inputted garment. The method includes: extracting attributes from text description and image of the inputted garment to obtain extracted attributes; querying a knowledge graph using the extracted attributes to obtain matched attributes; retrieving candidate products from a garment pool using the matched attributes; extracting features from the inputted garment and the candidate products; determining the target garment from the candidate products based on grading scores between the features of the inputted garment and the features of the candidate products; and recommending the target garment. The knowledge graph includes nodes corresponding to type of clothes, category of clothes, attribute keys, values of attribute keys, context keys, values of context keys, combination of the values of the attribute keys and the type of clothes, and combination of the value of the attribute keys and the category of clothes.

Abstract: A system for knowledge graph construction. The system includes a computing device. The computing device has a processor and a storage device storing computer executable code. The computer executable code, when executed at the processor, is configured to: define entities and relations of the knowledge graph; provide documents having sentences; convert the sentences into fix length sentence embeddings and regard the sentence embeddings as primary capsule layers; use a set transformer to learn entity capsules and relation capsules from the primary capsule layers; for each triple, project head and tail entities from entity space to the specific relation space, and determine the relation exists when the sum of the projected head entity vector and the relation vector substantially equals to the projected tail entity vector; and construct the knowledge graph using the head entity, the tail entity, and the determined relation.

Abstract: A system and a method for multimedia data analysis. The system includes a computing device. The computer device includes a processor and a storage device storing computer executable code. The computer executable code, when executed at the processor, is configured to: encode task description and task meta info into concatenated task vectors; encode context text and context meta info into concatenated context text vectors; encode context image and the context meta info into concatenated context image vectors; perform dual coattention on the concatenated task vectors and the concatenated context text and image vectors to obtain attended task vectors and attended context vectors; perform BiLSTM on the attended task vectors and the attended context vectors to obtain task encoding and context encoding; and decode the task encoding and the context encoding to obtain an answer to the task.

Abstract: A method and a system for recommending a target garment matching an inputted garment. The method includes: extracting attributes from text description and image of the inputted garment to obtain extracted attributes; querying a knowledge graph using the extracted attributes to obtain matched attributes; retrieving candidate products from a garment pool using the matched attributes; extracting features from the inputted garment and the candidate products; determining the target garment from the candidate products based on grading scores between the features of the inputted garment and the features of the candidate products; and recommending the target garment. The knowledge graph includes nodes corresponding to type of clothes, category of clothes, attribute keys, values of attribute keys, context keys, values of context keys, combination of the values of the attribute keys and the type of clothes, and combination of the value of the attribute keys and the category of clothes.

Abstract: A system for knowledge graph construction. The system includes a computing device. The computing device has a processor and a storage device storing computer executable code. The computer executable code, when executed at the processor, is configured to: define entities and relations of the knowledge graph; provide documents having sentences; convert the sentences into fix length sentence embeddings and regard the sentence embeddings as primary capsule layers; use a set transformer to learn entity capsules and relation capsules from the primary capsule layers; for each triple, project head and tail entities from entity space to the specific relation space, and determine the relation exists when the sum of the projected head entity vector and the relation vector substantially equals to the projected tail entity vector; and construct the knowledge graph using the head entity, the tail entity, and the determined relation.

Abstract: A system and a method for predicting punctuations for a text. The text is in a logographic language having characters. Most of the characters contain sub-characters representing meaning of the characters. The text lacks punctuations. The method includes receiving the text; providing a sub-character encoding based on a sub-character input editor (IME), such that each character in the logographic language corresponds to a specific sub-character code; encoding the text using the sub-characters encoding to obtain sub-character codes; generating sentence pieces from the sub-character codes; representing the sentence pieces by sentence piece vectors; and subjecting the vectors of the sentence pieces to a neural network to obtain the punctuations for the text. The method may be implemented using a computing device.

Abstract: A system and a method for multimedia data analysis. The system includes a computing device. The computer device includes a processor and a storage device storing computer executable code. The computer executable code, when executed at the processor, is configured to: encode task description and task meta info into concatenated task vectors; encode context text and context meta info into concatenated context text vectors; encode context image and the context meta info into concatenated context image vectors; perform dual coattention on the concatenated task vectors and the concatenated context text and image vectors to obtain attended task vectors and attended context vectors; perform BiLSTM on the attended task vectors and the attended context vectors to obtain task encoding and context encoding; and decode the task encoding and the context encoding to obtain an answer to the task.

Abstract: A method and system for constructing an evolving ontology database. The method includes: receiving a plurality of data entries; calculating semantic similarity scores between any two of the data entries; clustering the data entries into a multiple current themes based on the semantic similarity scores; selecting, new concepts from the current themes by comparing the current themes with a plurality of previous themes prepared using previous data entries; and updating the evolving ontology database using the new concepts. The semantic score between any two of the data entries are calculated by: semantic similarity score=?i=0nsie?j=0kwj×f j, where si is weight of features sources, fj is a feature similarity between the two of the data entries, wj is a weight of fj, and j, k and n are positive integers.

Abstract: A method and system for monitoring an e-commerce platform using artificial intelligence and fixing malfunctions on the e-commerce platform. The method includes: receiving, by a computing device, a feedback submitted by a user through the e-commerce platform; generating a vector based on content of the feedback, context of the feedback and profile of the user using AI processors; and classifying the vector to determine function corresponding to the feedback and status of the function using AI classifiers. The content includes text, voice, image and video; the context includes time, location and submission channel of the feedback; the profile includes attributes, history and preference of the user. Dimensions of the vector respectively corresponding to the text, voice, image, video, time, location, submission channel, attributes, history, and preference of the user.

Abstract: A method and system for constructing an evolving ontology database. The method includes: receiving a plurality of data entries; calculating semantic similarity scores between any two of the data entries; clustering the data entries into a multiple current themes based on the semantic similarity scores; selecting, new concepts from the current themes by comparing the current themes with a plurality of previous themes prepared using previous data entries; and updating the evolving ontology database using the new concepts. The semantic score between any two of the data entries are calculated by: semantic similarity score=?i=0nsie?j=0kwj×f j, where si is weight of features sources, fj is a feature similarity between the two of the data entries, wj is a weight of fj, and j, k and n are positive integers.

Abstract: A method and system for monitoring an e-commerce platform. The method includes: receiving, by a computing device, a feedback submitted by a user through the e-commerce platform; generating a vector based on content of the feedback, context of the feedback and profile of the user using AI processors; and classifying the vector to determine function corresponding to the feedback and status of the function using AI classifiers. The content includes text, voice, image and video; the context includes time, location and submission channel of the feedback; the profile includes attributes, history and preference of the user. Dimensions of the vector respectively corresponding to the text, voice, image, video, time, location, submission channel, attributes, history, and preference of the user.

Abstract: Aspects of the present disclosure may involve a computer implemented method of disambiguating a string from an article involving an electronic device including one or more hardware processing units, and accessing a disambiguation database comprising a plurality of string-entity combinations and associated metrics for each of the plurality of string-entity combinations. The associated metrics may include a metric associated with an arrival probability of linking at a web page for a particular entity after a specified number of links from a starting page. The method may involve generating a clique graph for each candidate entity of the article, and generating a probability that a particular candidate entity matches a particular string associated with the particular candidate entity as a function of score attributes generated from the clique graph and the arrival probability of linking at the page for the particular entity after the specified number of links.

Abstract: Aspects of the present disclosure involve a mobile or computer reader application that obtains articles or other computer files from a central database and displays the articles to a user of the device. The reader application may be customizable around one or more characteristics of the user of the device. In one embodiment, the type and number of articles provided to the device and displayed in the reader application may be based on the determination of a category or type of usage of the application is performed by the user. Further, the determination of the use of the reader application on the device is performed by and contained within the device such that usage information is not shared with overall article providing system. In another embodiment, the article providing system and/or device may determine recommendations to provide to a user of the reading application. These recommendations may be based on one or more selected interests or topics of the user of the reading application.

Abstract: Aspects of the present disclosure involve a mobile or computer reader application that obtains articles or other computer files from a central database and displays the articles to a user of the device. The reader application may be customizable around one or more characteristics of the user of the device. In one embodiment, the type and number of articles provided to the device and displayed in the reader application may be based on the determination of a category or type of usage of the application is performed by the user. Further, the determination of the use of the reader application on the device is performed by and contained within the device such that usage information is not shared with overall article providing system. In another embodiment, the article providing system and/or device may determine recommendations to provide to a user of the reading application. These recommendations may be based on one or more selected interests or topics of the user of the reading application.

Abstract: Aspects of the present disclosure involve a mobile or computer reader application that obtains articles or other computer files from a central database and displays the articles to a user of the device. The reader application may be customizable around one or more characteristics of the user of the device. In one embodiment, the type and number of articles provided to the device and displayed in the reader application may be based on the determination of a category or type of usage of the application is performed by the user. Further, the determination of the use of the reader application on the device is performed by and contained within the device such that usage information is not shared with overall article providing system. In another embodiment, the article providing system and/or device may determine recommendations to provide to a user of the reading application. These recommendations may be based on one or more selected interests or topics of the user of the reading application.

Abstract: A method includes determining a plurality of topics corresponding to descriptive text and to comments concerning the descriptive text, wherein each of a number of sets of the plurality of topics comprise a similar topic and one or more supplemental topics. The method includes determining by a computer system probabilities for words, where the probabilities are that the words belong to individual ones of the topics. The method further includes generating, by the computer system and based on comments and the probabilities, a content summary comprising a plurality of comment snippets having positive and negative sentiments toward corresponding ones of the similar or supplemental topics in the sets of topics. Each topic has corresponding comment snippets having positive and negative sentiments. The method includes outputting by the computer system at least a portion of the plurality of topics and corresponding comment snippets. Apparatus and computer program products are also disclosed.