154 results about "Knowledge representation and reasoning" 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.

Embodiments of the present invention relate to knowledge representation systems which include a knowledge base in which knowledge is represented in a structured, machine-readable format that encodes meaning. Techniques for extracting structured knowledge from unstructured text and for determining the reliability of such extracted knowledge are also described.

Embodiments of the present invention relate to knowledge representation systems which include a knowledge base in which knowledge is represented in a structured, machine-readable format that encodes meaning.

There is disclosed a system and method for using a knowledge representation to provide relevant information based on environmental inputs. In an embodiment, the system and method considers environmental information from members in a crowd to generate a pool of interests based on the semantic relevance concepts associated with those interests. The most prominent concepts of interest may then be the basis for presenting content to the crowd as a whole. In another embodiment, environmental inputs and other surrounding inputs are considered as a user context. The concepts may be identified as relevant from the environmental context and used to present information relevant to the user given his or her surroundings.

A persistent archive of a collection of data objects comprises a self-describing, infrastructure-independent representation of a logical structure for the collection and a self-describing, infrastructure-independent representation of the data objects. The archive is persistent in that it may be instantiated at an indefinite point in time in the future regardless of the state of technology at that time. A knowledge-based persistent archive of a collection of data objects comprises the foregoing two elements but also a self-describing, infrastructure-independent representation of knowledge relevant to the collection. Another embodiment of a knowledge-based persistent archive comprises at least one representation of a collection or the data objects, at least one self-describing, infrastructure-independent, or executable specification of one or more transformations relevant to the collection, and at least one self-describing, infrastructure-independent, or executable specification of one or more rules relevant to the collection.

According to an embodiment of the present invention, there is provided a method and system for emulating a knowledge representation in a Unified Modeling Language (UML) environment. A Meta-Object Facility metamodel and UML profile are grounded in a foundation ontology. The elements representing the knowledge representation ontology are mapped to elements of UML, based on the grounded Meta-Object Facility metamodel and UML profile, thereby emulating the knowledge representation in a UML environment.

A patient evaluation method is disclosed in which non-standard input data is corrected in a syntax processing block with reference to a healthcare lexicon and a resulting corrected data file is thereafter used to reference an ontology in an ontology processing block to generate a standardized output.

This invention relates generally to classification systems. More particularly this invention relates to a system, method, and computer program to dynamically generate a domain of information synthesized by a classification system or semantic network. The invention discloses a method, system, and computer program providing a means by which an information store comprised of knowledge representations, such as a web site comprised of a plurality of web pages or a database comprised of a plurality of data instances, may be optimally organized and accessed based on relational links between ideas defined by one or more thoughts identified by an agent and one or more ideas embodied by the data instances. Such means is hereinafter referred to as a “thought network”.

Embodiments of the present invention relate to knowledge representation systems which include a knowledge base in which knowledge is represented in a structured, machine-readable format that encodes meaning. Techniques for extracting structured knowledge from unstructured text and for determining the reliability of such extracted knowledge are also described.

A method based on Chinese website punctuation triple recognition and text content filtering. The method based on existing URL, the website information keywords in the method of filtration - filtration rate and the low rate of filtration of the whole problem, Bringing on a method for composite based on the URL and on keywords, as well as text-based knowledge representation method of vector space website text content filtering. Applying to a method Based on black-and-white list of URL filtering and Chinese punctuation statistical characteristics to effectively remove navigation information, relevant linked information, advertising linked information, copyright information and other Web content noise information to extract content of text; adopting vector space model text knowledge representation, By calculating vector text template and unhealthy information in the feature vector cosine angle, and set the threshold, compared to the text of the class. The invention can be widely used in the filtering of undesirable information network and website personalized information services.

The invention provides a method and system for classifying insurance files for identification, sorting and efficient collection of subrogation claims. The invention determines whether an insurance claim has merit to warrant claim recovery efforts utilizing software code for partially describing a set of documents having unstructured and structured file data containing terms and phrases having contextual bases, code for transforming the terms and phrases, code for iterating a classification process to determine rules that best classify the set of documents based upon context, code for incorporating the rules into an induction and knowledge representation, thesauri taxonomies and text summarization to classify subrogation claims; code for calculating a base score and a concept vector to identify the selected claims that demonstrate a given probability of subrogation recovery.

An integrated human and computer interactive data mining method receives an input database. A learning, modeling, and analysis method uses the database to create an initial knowledge model. A query of the initial knowledge model is performed using a query request. The initial knowledge model is processed to create a knowledge presentation output for visualization. It further comprises a feedback and update request step that updates the initial knowledge model.A multiple level integrated human and computer interactive data mining method facilitates overview interactive data mining and dynamic learning and knowledge representation by using the initial knowledge model and the database to create and update a presentable knowledge model. It facilitates zoom and filter interactive data mining and dynamic learning and knowledge representation by using the presentable knowledge model and the database to create and update the presentable knowledge model. It further facilitates details-on-demand interactive data mining and dynamic learning and knowledge representation by using the presentable knowledge model and the database to create and update the presentable knowledge model.The integrated human and computer interactive data mining method allows rule viewing by a parallel coordinate visualization technique that maps a multiple dimensional space onto two display dimensions with data items presented as polygonal lines.

A system and method related to a new approach to speech recognition that reacts to concepts conveyed through speech. In its fullest implementation, the system and method shifts the balance of power in speech recognition from straight sound recognition and statistical models to a more powerful and complete approach determining and addressing conveyed concepts. This is done by using a probabilistically unbiased multi-phoneme recognition process, followed by a phoneme stream analysis process that builds the list of candidate words derived from recognized phonemes, followed by a permutation analysis process that produces sequences of candidate words with high potential of being syntactically valid, and finally, by processing targeted syntactic sequences in a conceptual analysis process to generate the utterance's conceptual representation that can be used to produce an adequate response. The invention can be employed for a myriad of applications, such as improving accuracy or automatically generating punctuation for transcription and dictation, word or concept spotting in audio streams, concept spotting in electronic text, customer support, call routing and other command / response scenarios.

Disclosed is a system and method for performing a semantic operation on a social network. In an embodiment, the method comprises receiving a social network user context associated with a user of the social network; generating, through a semantic operation, an interest network based on the user context information; and filtering, ranking or augmenting, using at least one processor executing stored program instructions, a retrieval of information related to the social network based on the interest network; wherein the interest network comprises concepts represented by a data structure associated with the concepts in the interest network. In another embodiment, the method further comprises representing the interest network as an interest graph. In yet another embodiment, the semantic operation is a synthesis operation or retrieval operation performed on a knowledge representation.

A hierarchical knowledge representation is used in order to more efficiently support the translation and management of multiple localities. Utilizing this type of knowledge representation provides several key advantages. The first is a greater ability to differentiate semantically different symbols within the system. The second, is the ability to reutilize the translation of these symbols once translated a single time; and concurrent locale support.

The invention provides a building method used for a knowledge obtaining model in a knowledge graph. The method comprises the steps of constructing a first training set consisting of multiple text sentences as input data and a relationship between any two entities in each sentence in the knowledge graph, as a classification result, and training a first neural network; constructing a second trainingset consisting of triples in multiple knowledge graphs, and training a second neural network; by taking input data vectors obtained in the second neural network as attention features of the first neural network, building a relationship extraction model; by taking input data vectors obtained in the first neural network as attention features of the second neural network, building a knowledge representation model; and fusing the relationship extraction model and the knowledge representation model to obtain the knowledge obtaining model in the knowledge graph. According to the method provided bythe invention, the two task models of knowledge representation and relationship extraction are integrated at the same time, and the features of the knowledge graph and free texts can be comprehensively extracted, so that the model stability and accuracy are improved.

The invention discloses a keyword based evaluation expert intelligent search and recommendation method. The keyword based evaluation expert intelligent search and recommendation method specifically comprises step 1, segmenting an expert information main text into substring sequences, performing ICTCLAS word segmentation of Chinese academy of sciences and performing stop word filtering on the result of the word segmentation to obtain the word collection; step 2, extracting feature words of the expert information according to fields; step 3, building an expert knowledge representation model based on the fields and the weight of the feature words and establishing an expert information index database; step 4, performing automatic prompting according to a search term thesaurus when a user inputs keywords and meanwhile performing real-time update on the search term thesaurus through a search term counter; step 5, calculating the search relevance between the keywords and the expert information based on the semantic information and the like; step 6, listing relevant experts from high to low according to the matching degree. According to the keyword based evaluation expert intelligent search and recommendation method, the intelligent full-text search and recommendation of the expert information can be achieved through the keyword input and accordingly the experts which are matched with a pended science and technology project can be searched out accurately.

The invention provides an intelligent review expert recommending method for science and technology projects. The method includes the following steps that (1) the science and technology projects to be reviewed and expert information main texts are segmented into substring sequences, ICTCLAS segmentation of Chinese academy of sciences is carried out on the substring sequences, and stop word filtering is carried out on a segmentation result to obtain a term set; (2) a term network of project information is built, feature words are extracted on the basis of statistical characteristics and aggregation characteristics, and if expert information is relatively concise, the term set obtained in the step (1) directly serves as the feature words; (3) a knowledge representation model is built on the basis of fields and weights of the feature words, and a relative information index is built; (4) experts are recommended in groups to carry out feature merging operations between the fields and between the projects on the knowledge representation model; (5) similarity of the experts and the science and technology projects or groups to be viewed is calculated on the basis of semantics, threshold truncation is set, and a final recommended expert list is generated. By means of the method, the problems that recommending workload is large and review decisions lack scientificity are greatly alleviated.

An image reporting method is provided. The image reporting method comprises the steps of retrieving an image representation of a sample structure from an image source; mapping a generic structure to the sample structure, the generic structure being related to the sample structure; determining a region of interest within the sample structure based on content of the image representation of the sample structure; providing a focused set of representations of diagnostic knowledge which is contextually appropriate to the region of interest and prompting the user to select at least one diagnostic finding from the focused set of knowledge representation or by entering free-form text; and generating a diagnostic report based on the selections and free-form text entries.

The invention discloses a power distribution network fault diagnosis system and method based on multi-source information. The system comprises a data collection layer, a knowledge representation layer and a fault diagnosis layer, wherein the data collection layer collects fault warning information in each power distribution substation and provides the collected information for the knowledge representation layer in time sequence; the knowledge representation layer describes and buffers data of all kinds of data sources of the information and provides all kinds of heterogeneous data for the fault diagnosis layer in a unified data view mode; the fault diagnosis layer makes different diagnosis methods according to the different data sources, a fault result is comprehensively judged finally, and the diagnosis result is used for analysis after an accident. The utilization rate of the fault information is improved, the reliability of the fault diagnosis is ensured, the fault reason, the fault influence degree and the like are analyzed preliminarily after a fault happens, the analysis result can assist operating personnel in building a timely and effective power distribution network fault repairing strategy, and the power supply reliability of a power distribution network is improved.

The invention discloses a knowledge map construction method for medical images, belonging to the field of knowledge maps. The construction process includes a step of knowledge representation with a framework theory representation method, a step of knowledge acquisition, wherein the knowledge sources of entity, attribute and attribute value extraction is unstructured data, a step of knowledge fusion with the integration of acquired new knowledge and the elimination of ambiguity, a step of knowledge processing with the knowledge reasoning and quality assessment of the data which is subjected tothe knowledge fusion and the adding of qualified data to a knowledge map, and a step of knowledge update with the update of the knowledge map according to the update and development of medical image knowledge. According to the characteristics of the medical image knowledge, and the knowledge acquisition rate is greatly improved with the unstructured data such as textbooks and academic journals asa source of knowledge.

Embodiments of the present invention relate to knowledge representation systems which include a knowledge base in which knowledge is represented in a structured, machine-readable format that encodes meaning.

The invention provides an information resource query recommendation method and system based on a knowledge graph, and the method comprises the steps: carrying out the preprocessing of the knowledge graph, enabling the knowledge graph to be mapped to a low-dimensional dense vector space through employing a representation learning method, and obtaining the vector representation of an entity; calculating the interest degree of the user in the information resource according to the historical behavior of the user, and constructing a user interest model by combining the vectorized representation ofthe information resource and the interest degree of the user in the information resource; achieving accurate recommendation of the information resources is achieved by calculating the similarity between the resources and the similarity between the users and the resources. According to the invention, the knowledge graph representation learning is combined with the user interest model to provide personalized service for the user; according to the method, the internal relation of knowledge and the interest of the user are considered, and the information resources related to the query content andconforming to the interest of the user are recommended to the user according to the queried resource name input by the user, so that the personalized query recommendation is more professional and targeted.

An onsite information preprocessing method of remote cooperative diagnosis includes an information collection step, a health state prediction step, a deep information processing step and a remote information transmission step. The information collection step collects working state information, control information, fault diagnosis information, working record information and working environment information. The health state prediction step predicates health state of equipment in one further period according to collection information and correspondingly conducting adjustment on an important monitoring point and monitoring frequency of the state monitoring in advance. The deep information processing step excavates fault sign information of relative time periods of the important monitoring point and conducting effective information extraction on common health state information according to certain standard. The remote information transmission step uploads preprocessing results. The onsite information preprocessing method compresses and extracts onsite original data of the equipment, reasonably expresses health state information of the equipment and converts the health state information into the knowledge expression mode required by an expert system or other decision making systems according to actual health state of the equipment.

The invention discloses a knowledge graph-based mechanical fault diagnosis knowledge base construction method, and belongs to the field of mechanical fault diagnosis. A mechanical fault diagnosis knowledge base reflects fault generation essences and domain expert experiences; and through a knowledge processing module, the fault generation essences and the domain expert experiences are stored in the knowledge base, thereby providing support for mechanical fault diagnosis. A conventional knowledge graph is represented in a network form; nodes represent entities; connection lines represent relationships; and for the representation form, a special graph algorithm needs to be designed for storing and utilizing a database, so that the disadvantage of time and labor waste exists. According to a representation learning technology represented by deep learning, a triple object is mapped to a vector space and represented as a dense low-dimensional vector, and efficient calculation and reasoning are realized through vector conversion. The knowledge graph-based mechanical fault diagnosis knowledge base construction method is established; mechanical fault diagnosis knowledge is represented as atriple, and the tripe is represented as the vector by utilizing a TransD model, so that the problems of inaccurate case representation, difficult maintenance and modification, low reasoning and calculation efficiency and the like of a conventional knowledge base can be optimized; and the method has important significance for the field of fault diagnosis.

A hierarchial knowledge representation is used in order to more efficiently support the translation and management of multiple localities. Utilizing this type of knowledge representation provides several key advantages. The first is a greater ability to differentiate semantically different symbols within the system. The second, is the ability to reutilize the translation of these symbols once translated a single time; and concurrent locale support.

A near natural language format is used to codify knowledge (including information, programs, rules, and procedures). The codified knowledge is stored in knowledge snippet files according to a nomenclature in which each file has a name relating to a particular interrogatory and the type of the interrogatory (e.g., having a file extension of: “.what”, “.how”, “.where”, “.which”, “.when”, “.who”, or “.why”). The content of each of the knowledge snippet files comprises an answer to the interrogatory. Language systems and methods, including a run-time environment to process or execute the knowledge, are provided to use the knowledge so represented. The knowledge may also be presented in many different perspectives or views.