Abstract: A scheduling method using a knowledge graph (KG) performs following steps by a processor: obtaining an initial scheduling solution of the production system; converting the initial scheduling solution into triples to form the KG, each triples includes two entities and a relationship of the production system, the two entities indicate two production resources; embedding triples into a vector space to generate embedded vectors by a KG embedding technique; generating embedded vector combinations according to the embedded vectors and computing a distance of each of embedded vector combinations; and performing a scheduling algorithm to generate a target scheduling solution according to the embedded vector combinations, and providing reference information when the scheduling algorithm generates a schedule of a station, wherein the reference information comprises at least one of the embedded vector combinations associated with the station, and the distance corresponding to said at least one embedded vector combinatio

Abstract: A graph-based natural language optimization method and an electronic apparatus are provided. The method is adapted for an electronic apparatus with a processor. In the method, an input sentence submitted by a user is received, and multiple domain-related entities are extracted from the input sentence. The domain-related entities are input to a graph database to analyze a connection relationship between the domain-related entities, and filling data is obtained from the graph database based on the connection relationship. The input sentence and the filling data are integrated via a natural language processing technology to generate an optimized natural language sentence.

Abstract: An internet question answering method is applied in an internet question answering system. A current-turn user question is received. Based on a discourse labeling model, a discourse of the current-turn user question is labeled as “discourse continue” or “discourse end” online and real-time. Based on the discourse of the current-turn user question, whether to clear at least one discourse of an old boundary is determined. The internet question answering system answers the current-turn user question. A match degree between a next-turn user question and the segment of the dialog log is compared to determine whether the discourse of the current-turn user question is correct, whether to correct the discourse of the current-turn user question, and whether to adjust the discourse labeling model by using the current-turn user question.

Abstract: In one exemplary embodiment, a system for discovering suspicious account groups establishes a language model according to the post contents from each account of a first group of accounts during a first time interval, to describe the speech of the account, and compares the similarity among a plurality of language models of the first group of accounts to cluster the first group of accounts; and for a plurality of newly added data during a second time interval, discovers near-synonyms of at least a monitored vocabulary set, and updates the near-synonyms to a plurality of language models of a second group of accounts. The system further integrates the first and the second groups of accounts, and re-clusters an integrated group of accounts.

Abstract: An internet question answering method is applied in an internet question answering system. A current-turn user question is received. Based on a discourse labeling model, a discourse of the current-turn user question is labeled as “discourse continue” or “discourse end” online and real-time. Based on the discourse of the current-turn user question, whether to clear at least one discourse of an old boundary is determined. The internet question answering system answers the current-turn user question. A match degree between a next-turn user question and the segment of the dialog log is compared to determine whether the discourse of the current-turn user question is correct, whether to correct the discourse of the current-turn user question, and whether to adjust the discourse labeling model by using the current-turn user question.

Abstract: In one exemplary embodiment, a system for discovering suspicious account groups establishes a language model according to the post contents from each account of a first group of accounts during a first time interval, to describe the speech of the account, and compares the similarity among a plurality of language models of the first group of accounts to cluster the first group of accounts; and for a plurality of newly added data during a second time interval, discovers near-synonyms of at least a monitored vocabulary set, and updates the near-synonyms to a plurality of language models of a second group of accounts. The system further integrates the first and the second groups of accounts, and re-clusters an integrated group of accounts.

Abstract: A system for leaving and transmitting speech messages automatically analyzes input speech of at least a reminder, fetches a plurality of tag informations, and transmits speech message to at least a message receiver, according to the transmit criterions of the reminder. A command or message parser parses the tag informations at least including at least a reminder ID, at least a transmitted command and at least a speech message. The tag informations are sent to a message composer for being synthesized into a transmitted message. A transmitting controller controls a device switch according to the reminder ID and the transmitted command, to allow the transmitted message send to the message receiver via a transmitting device.

Abstract: The present invention relates to an antenna having a connecting circuit, which includes a substrate, a grounding metal strip, a first radiating metal strip, a second radiating metal strip and a connecting circuit. The first radiating metal strip is not connected to the grounding metal strip or the second radiating metal strip. The connecting circuit connects different positions on the grounding metal strip and on the second radiating metal strip, so as to form a plurality of resonant paths of different lengths between the grounding metal strip and the second radiating metal strip. Thereby, the frequency of the antenna varies between different values, so that the range of the application and the practicality of the antenna are increased.

Abstract: A recommendation method and a recommender computer system using dynamic language model are provided. The recommender computer system using dynamic language model includes a language model constructing computer module, a language model adapting computer module, a sentence selecting computer module and a sentence recommendation computer module. The language model constructing computer module is used for constructing a language model. The language model adapting computer module is used for dynamically emerging different language models to construct a dynamic language model. The sentence selecting computer module generates a plurality of recommended sentences from a database according to a search keyword. The sentence recommendation computer module analyzes the difference level between the recommended sentences and the dynamic language model and sorts recommended sentences to provide a recommendation list.

Abstract: A system for leaving and transmitting speech messages automatically analyzes input speech of at least a reminder, fetches a plurality of tag informations, and transmits speech message to at least a message receiver, according to the transmit criterions of the reminder. A command or message parser parses the tag informations at least including at least a reminder ID, at least a transmitted command and at least a speech message. The tag informations are sent to a message composer for being synthesized into a transmitted message. A transmitting controller controls a device switch according to the reminder ID and the transmitted command, to allow the transmitted message send to the message receiver via a transmitting device.

Abstract: Disclosed is a system and method for conversation practice in simulated situations. The system comprises situational conversation teaching material, an audio processing module and a conversation processing module. The teaching material consists of multi-flow conversation paths and conversational sentences with a plurality of replaceable vocabulary items. According to different contents of the situational conversation teaching material and biased data of the teaching material, the audio processing module dynamically adjusts speech recognition model and recognizes the inputted audio signal of the learners to determine the information on the recognition results. The conversation processing module determines the information in response to the learners, based on the information on the recognition results, the situational conversation teaching material and biased data of the teaching material.

Abstract: The present invention relates to an example-based concept-orietned data extraction method. In an example labeling phase, the exemplary data string is converted into an exemplary token sequence, in which the target concepts and filler concepts are labeled to be tuples for use as an example, and thus an exemplary concept graph is constructed. In the data extraction phase, the untested data string is converted into an untested token sequence to be processed, and, based on the associated concept recognizers defined by the tuples in the example labeling phase, it is able to detect the concept candidates and establish the composite concepts and aggregate concepts, thereby constructing a hypothetical concept graph. After comparing the exemplary concept graph with the hypothetical concept graph, the optimal hypothetical concept sequence in the hypothetical graph is determined, so as to extract the targeted data from the matched target concepts.

Abstract: The present invention relates to an example-based concept-orietned data extraction method. In an example labeling phase, the exemplary data string is converted into an exemplary token sequence, in which the target concepts and filler concepts are labeled to be tuples for use as an example, and thus an exemplary concept graph is constructed. In the data extraction phase, the untested data string is converted into an untested token sequence to be processed, and, based on the associated concept recognizers defined by the tuples in the example labeling phase, it is able to detect the concept candidates and establish the composite concepts and aggregate concepts, thereby constructing a hypothetical concept graph. After comparing the exemplary concept graph with the hypothetical concept graph, the optimal hypothetical concept sequence in the hypothetical graph is determined, so as to extract the targeted data from the matched target concepts.