176 results about "Knowledge structure" patented technology

A knowledge-based natural speech dialogue system includes: (i) a knowledge support system, (ii) a flexible dialogue control system, and (iii) a context information system. Flexibilities of the conversation structure, inherent in mixed-initiative mode for dealing with complex user request, are managed because the knowledge structures involved are represented by additional, powerful knowledge representation tools, and because the context information is retained by more specific data structures, which covers larger temporal scopes by the logic of the conversation, rather than by a fixed locality of the grammar flow. This system provides a simple yet reliable method to compensate for these factors to enable more powerful conversation engines with mixed-initiative capabilities.

In the present invention, a textual document is syntactically analyzed and knowledge is constructed from a single word or plural words. The knowledge is then marked, from the broken down knowledge (represented by the underscores in FIG. 1) or from a part-of-speech, as a related object (node) or a relation (edge) (represented by ‘n’ or ‘e’ shown in FIG. 1). In other words, in the present invention a textual document is treated as knowledge constructed from a single word or plural words. The knowledge extracted from the textual document is structured to form a knowledge structure (such as a graph structure constituted from nodes and edges). At least one link can be established between each of the knowledge structure elements and a semantically closest concept entry in a hierarchical concept dictionary.

The present invention is to provide a learning machine capable of expressing/accumulating concept and semantic relation by understanding semantic relation of information as a relation between concepts after characteristics, semantic relation and structure of information and the like have been analyzed. The present invention is intended to realize a thinking machine in which information is inclusively collected and stored as knowledge structured based on the semantic relation of the collected information, information is generated by predetermined inference so as to have a new semantic relation in response to an inquiry or request and which can decide an optimal solution to an inquiry and the like by evaluating/deciding new generated information.

A course guiding system based on non-zero start point and knowledge point in network education belongs to the field of computer technologies. The invention includes an initial knowledge domain construction module, an individualized knowledge domain generation module, an individualized learning route generation module, a history learning record module, a knowledge structure level evaluation module, and an individualized learning resource recommendation module. With knowledge point as the core, the invention constructs courses into the corresponding knowledge point tree and graph among knowledge points; learners are examined on knowledge points at each stage; by the analysis of learners' learning data and test of learning effect, the system obtains the learning status of learners at each knowledge point, their learning prediction result and knowledge point structure, recommends the corresponding learning resources and students to learners, and helps learners form the self-organized learning community to enhance the learning quality.

A campaign is received, at a micro-segmentation system, from an offer provider. The micro-segmentation system is a third-party system. The campaign indicates a set of target attributes and one or more offers corresponding to the set of target attributes. A set of user attributes pertaining to each of the plurality of users is received at the micro-segmentation system. The set of attributes is defined by an attribute knowledge structure. Permission is received at the micro-segmentation system from each of the plurality of users to receive an offer from the micro-segmentation system. Data associated with the micro-segment classification is provided from the micro-segmentation system without user information to the offer provider.

The invention provides a socialized homework evaluation and student learning process information recording system and method. The system comprises a mobile terminal, a computer and an answer sheet marked with specific divided symbols. An evaluation system operates in the computer, the evaluation system comprises an input module, a data processing and storing module, a data analysis and visualization module, an output module and an homework database. The socialized homework evaluation and student learning process information recording system and method are suitable for subject teaching in middle and primary schools, the pressure of homework evaluation of teachers can be lightened through collaborative homework evaluation of socialized members, meanwhile, the formative data in the learning process of students and the knowledge structure of the students can be accumulated, so that homework assigning and course recommendation are conducted more targeted, the quantity of homework is reduced, and homework efficiency is improved.

A system and method for reasoning about concepts, relations and rules having a semantic network comprising at least one node from a predetermined set of node types, at least one link from a predetermined set of link types, and zero or more rules from a predetermined set of rule types, a subset of the rule types being matching rule types, each node and each link being associated with a set of zero or more rules; a network reasoning data structure having a reasoning type database having at least one regular expression, each of the regular expressions being a class of sequences having at least three node types and two link types, wherein the network reasoning data structure further has a context being a set of rules; and a reasoning engine having an activator for activating one or more activated paths in the semantic network, the set of activated paths having a common starting node in the semantic network, wherein the reasoning engine further has a validator for selecting a subset of the activated paths being valid paths, each rule from the set of rule matching types that is associated with one or more path elements on each valid path being matched by one or more rules in the context and wherein the reasoning engine further has a legal inferencer for selecting a subset of the set of valid paths being legal and valid paths, the legal and valid paths matching at least one of the regular expressions.

The invention discloses a cloud-network-based answering learning method and a cloud-network-based answering learning system. The method comprises the following steps that a user transmits a knowledge detection command to a cloud server; the cloud server calculates a corresponding test question set according to the knowledge structure of the user or a set knowledge point range and difficulty level selection, and transmits test questions in the test question set to a user network terminal of the user one by one through a network; the user submits answers to the test questions to the cloud server at the specified time through the user network terminal; the cloud server queries a corresponding recommended course according to the knowledge points and difficulties of the test questions under the condition of determining that the answers submitted by the user are wrong, and transmits answering results and the recommended course to the user network terminal of the user. According to the method and the system, weak related knowledge points can be timely learnt in a reinforcement way, the learning pertinence of the user is improved, and the learning efficiency and learning enthusiasm of a student are greatly improved.

A collaborative, semantic-based knowledge structure creation and management system and method are provided. The system and method may include knowledge structures which include concept based maps which may be linked according to topic, relationship types, conceptual equivalence, and temporally. The knowledge structures may be managed by users, wherein users may perform at least one of adding a new concept, creating a new relationship, confirming an existing relationship, and breaking an existing relationship.

Briefly stated, the invention is directed to retrieving a semantically matched knowledge structure. A question and answer pair is received, wherein the answer is received from a query of a search engine. A question is constraint-matched with the answer based on maximizing a plurality of constraints, wherein at least one of the plurality of the constraints is a similarity score between question and answer, wherein the constraint matching generates a matched sequence. For one or more answer sequences, a subsequence is found that are not parsed as answer slots. Query results are obtained from another search engine based on a combination of the answer or question, and the non-answer subsequence. And a KB based is refined on the query results and the constraint matching and based on a neural network training, for a further subsequent semantic matching, wherein the KB includes a dense semantic vector indication of concepts.

An active semiotic system that is able to create implicit symbols and their alphabets from features, structural combination of features, objects and, in general sense, patterns; create models with explicit structures that are labeled with said implicit symbols, and derive other models in the same format by means of diagrammatic- and graph transformations. The invention treats vision as a part of larger system that converts visual information into special knowledge structures that drive vision process, resolve ambiguity and uncertainty via feedback projections, and provide image understanding that is an interpretation of visual information in terms of such knowledge models. Mechanisms of image understanding, including mid- and high- level vision are presented as methods and algorithms of said active semiotic system, where they are special kinds of diagrammatic and graph transformations. In the invention, the derived structure and not the primary view is a subject for recognition. Such recognition is not affected by local changes and appearances of the object from a set of similar views, and a robot or unmanned vehicle can interpret images and video similar to human beings for better situation awareness and intelligent tactical behavior.

The invention discloses a knowledge reasoning method based on a multi-modal knowledge graph, and aims to enable knowledge reasoning reliability and accuracy to be higher and enable the knowledge reasoning method to have stronger modeling and reasoning capabilities. The method is realized through the following technical scheme: different information is fused based on multi-hop reasoning of a large-scale knowledge base; attribute completion is performed on the attribute missing graph through attribute graph embedding, structured information is extracted from unstructured and semi-structured documents or sentences, and a dynamic heterogeneous graph embedding model is constructed for multi-type characteristics of the multi-modal knowledge graph through heterogeneous graph embedding; feature learning of semi-structured knowledge, structured knowledge and different types of non-structured knowledge is achieved, and multi-modal knowledge graph features are obtained and serve as input for knowledge reasoning based on a graph neural network GNN; an inference path is generated, and a plurality of types of inference paths are constructed; and classification, edge prediction and frequent subgraphs of node types are calculated on the graph, a knowledge reasoning task is generated, and multi-step complex knowledge reasoning is completed.

The invention discloses a self-adaptive network security knowledge evaluation method based on a cognitive diagnosis theory. The self-adaptive network security knowledge evaluation method based on thecognitive diagnosis theory comprises the following steps: S1, an evaluation system generates a network security knowledge graph according to the identity background of a user, and tests the user according to a preset sequence and a knowledge structure; S2, the test system generates a personal basic information database according to the personal identity background information uploaded by the userand a specific format; and S3, the test system traverses according to the structural sequence of the knowledge graph for item-by-item test, and accurate positioning of the knowledge level of the useris realized based on test question extraction of the corresponding difficulty standard. According to the method, a potential knowledge state is obtained through real-time feedback of a user in testing, and a novel cognitive diagnosis model PH-is utilized; And the real knowledge, skill level and corresponding short boards of the user are efficiently reasoned by the DINA, so that efficient and accurate education evaluation is realized, and the learning condition of the user is better reflected.

The invention provides a method for mining social relationship during mobile reading. The social relationship includes the degree of association between users. The method includes computing similarity of knowledge structures formed by knowledge points accessed by the users during book reading at first; and then combining interaction record of the users to obtain the degree of association between the two users according to the computed similarity of the knowledge structures. The method has the advantages that an interaction behavior mode among the knowledge structures and the users when the users read is comprehensively considered, and the interpersonal relationship can be effectively reflected by the obtained degree of association between the users.

Instructional systems are assumed to include one or more learners, human and/or automated, content, means of presenting information to and receiving responses from learners, and an automated tutor capable of deciding what and when information is to be presented to the learner and how to react to feedback from the learner based on configurable options and the current status of the learner model. This invention discloses a method for authoring and delivering highly adaptive instructional systems based on abstract syntax tree representations of the problems to be solved by learners and, of the requisite knowledge structures to be acquired. Authoring includes: a) receiving and/or constructing abstract syntax tree representations of essentially any kind of to-be-acquired knowledge (KR), b) methods for representing problem schemas in an observable medium enabling communication between tutors and learners and c) configuring the learning and tutorial environment to achieve desired learning. Delivery includes general-purpose methods for: d) generating specific problems, updating the learner model and sequencing diagnosis and instruction.

The invention provides a method and a device for establishing an event repository. The method comprises the following steps of recognizing a text to make out an event excavation candidate statement; segmenting the recognized event excavation candidate statement to obtain a syntax fragment; generating event knowledge instances according to the syntax fragment and the pre-defined event knowledge structure, wherein the quantity of the event knowledge instances is identical to that of verb-object fragments and subject-predicate fragments contained in the syntax fragment; obtaining the corresponding event excavation target statement according to the verb-object fragments and the subject-predicate fragments contained in the syntax fragment, classifying the components of the obtained event excavation target statement, and writing the classified component into the event knowledge instance so as to complete the establishment of the repository. The event repository can be established through the pre-defined event knowledge structure, an accurate search result can be provided for the users on the basis of the event repository, and the search experience of the user can be improved.

Preferred embodiments of the invention provide systems and methods for facilitating multiple users or groups in thinking about and working collaboratively on an arbitrary problem, Embodiments of the system includes logic to facilitate multiple user or group specification of a problem specification, a conclusion specification, and a knowledge structure, related to the arbitrary problem. Some embodiments of the system further include logic to subdivide and link at least one of the problem specification, conclusion specification and knowledge specification, as for work by individuals or subteams. Some embodiments include automatic composition logic to combine at least one of the problem specification, knowledge specification and conclusion specification across multiple users or teams and other embodiments provide automatic suggestioning regarding the archetype group problem solving process.

The invention discloses a natural language knowledge exploration system based on formal semantics reasoning and deep learning. The system comprises a machine learning module combining formal semantic reasoning and a machine learning method, and conducting machine learning containing semantics, an intention learning module learning a description intention of a to-be-explored natural language, a text subject extraction module analyzing a text subject content and paragraph content description intention via an LDA model, and a file structure classification model building module conducting model training based on a deep convolution neural network, automatically decorating and improving the neural network during the training, and building an automatic file structure classifier. Based on combination of automatic semantic reasoning, deep learning technology and natural language processing, non-structural data groups with documents and voices as representatives can be processed and reasoned in light of intentions, so real intention of verbal description can be learned; knowledge explored and retrieved can be used for new knowledge structure analysis and construction, so knowledge can be explored and retrieved in a way with high-efficiency, intelligence and learnability and self-evolution.

The invention relates to an intelligent test paper composition and examination method and system. The system comprehensively tracks and manages topic knowledge structures and data extension, thus theintelligent test paper composition function is provided, and the test paper composition quality is improved; and test paper composition persons have complete autonomy on topics and test paper, whereinthe autonomy comprises defining of a subject, knowledge points, a difficulty level, teaching requirements, scores and an applicable scope of each question, and previewing and modifying of the preliminarily generated test papers, and thus personalized test paper is generated more advantageously. The intelligent test paper composition and examination method and system have the advantages of batch import of the test questions, question type setting, intelligent test paper composition, self-defining of examination rules, system scoring/subjective paper marking, management of test points/the testquestions/the test papers, management of question banks, statistical analysis of whole examination/answer situations, examination question doing history, exercise collection, test question notes, testquestion analysis, test question correction, and easy-to-use and efficient examination data analysis and management, and have the advantages that real reasons of wrong questions are troubleshot, various types of past exam paper and exercises are easily solved, operation is easy, the function is excellent, and work such as test paper composition and examination is faster and more standardized.

Enhanced metadata with optimized output. In an embodiment, a content object is received. First metadata to be associated with the content object is determined. At least one metadata field to be acquired is determined based on an association, within a stored knowledge structure, of the at least one metadata field with the first metadata. Second metadata to be associated with the content object is acquired based on the at least one metadata field. A metadata structure and markup format for the content object are determined. The metadata structure incorporates both the first metadata and the second metadata. The content object is output with the metadata structure and in the markup format.

The invention discloses a probability linear speaker-distinguishing identifying method based on priori knowledge structured covariance, which is capable of structuring a covariance hypothesis and iterative process of a probability linear identifying-analyzing model based on random useful information related to training voice; and finally, a probability linear identifying-analyzing model that can be more distinctive and can reflect the real situation can be trained; and at the same time, two structuring coefficients are introduced to make the model adjustable and can be self-adaptive to be optimum aiming to various different structuring information. By adopting the model trained by this method provided herein, compared with the traditional model, the evaluating effect of identifying the speaker on the same dataset is improved significantly; and the equal error rate (EER) and the minimum detect error cost function (norm minDCF) can be lowered 10 percent to 20 percent in the evaluating database of identifying an internationally authoritative speaker.

The present invention relates to a system and method for information process using artificially constructed apparatus. More specially, the present invention provides a system and method that can search for information in a document structure and provide precise results by analyzing the inputs and search results using the executing system and the knowledge structure of the think system. In one preferred embodiment of the present invention, the search terms are divided into subject terms and corresponding feature terms, and document entry files comprising respective subject terms and corresponding feature terms will provide access to documents including subject terms and corresponding feature terms.

Disclosed herein are a real-time semantic annotation system and a method of converting user-entered natural language strings into semantically-readable knowledge structure documents using the system in real time. The real-time semantic annotation system includes a natural language character string input device for enabling a user to enter natural language character strings, a character string pattern triplet-mapping table for storing natural language character string patterns and their corresponding triplets, a triplet extraction device for converting the entered natural language character strings into triplets by analyzing and processing the entered natural language character strings using the pattern-triplet mapping table, an alternative word recommendation device for providing notification that a user should enter an alternative word, and a machine-readable document generation device for generating machine-readable documents from the triplets using a semantically-readable knowledge structure.

The invention discloses a method and a system for predicting ammunition consumption based on interpretative structural modeling knowledge refinement. The system comprises an expert knowledge input module, a prediction structure constructing module, a model base, a data acquisition module, a prediction model matching module, a differentiated structure output module, a model modification module and a consumption computation module. The method corresponds to the system, an ammunition consumption interpretation structure is constructed by an expert, a collected prediction context interpretation structure is matched with an interpretation structure corresponding to a formula, and a model is selected from the model base. A modification rule is constructed according to a differentiated structure so as to modify formula parameters or consumption, and the model is stored in the model base, so that an ammunition consumption structure is adjusted to complete predication of the ammunition consumption. According to the method and the system, an uncertain knowledge structure is enabled to be clear, so that the precision for predicting ammunition consumption is improved.

The invention discloses a smart city traffic signal control recommendation method, system and device. Control scheme data and detector data are learned based on a deep learning algorithm, new knowledge is obtained, the knowledge is updated, a knowledge structure is recombined, signal control recommendation parameter value output is realized, a closed-loop self-learning mechanism is formed, and update and iteration of a model can be continuously carried out according to feedback. A deep learning algorithm model meeting the performance requirement of the model is selected through an algorithm selector; a network structure meeting the searching requirement of the deep learning algorithm is selected through a neural framework generator; data related to a control scheme operator is extracted astraining data of the model, and a signal control scheme of model recommendation is more accurate and more targeted; and a regulation and control triggering mechanism is arranged, and a signal controlrecommendation is timely and effectively given.