The present disclosure provides method and apparatus for processing a message. A statement sentence message and a message processing parameter associated with a user's session message are obtained. One or more first statement sentence nodes that are semantic-matched with the statement sentence message are determined in the knowledge map. One or more second statement sentence nodes corresponding to the message processing parameters are obtained from the knowledge map, based on the node relationship properties of the first statement sentence nodes. A response is generated based at least in part on statement sentences of the one or more second statement sentence nodes. The generated response is provided to the user.

A system and method for automatically generating organization level ontology for knowledge retrieval, are provided. An input/output unit receives a plurality of documents from document sources and an ontology generation system generates the organization level ontology based on the documents. The ontology generation system extracts one or more nodes and directed relationships from each document and generates an intermediate document ontology for each document. A combination of syntactic, semantic, and pragmatic assessment of intermediate document ontology is performed to assess at least structure and adaptability of the ontology. The ontology generation system further generates a refined document ontology, based on assessment, to satisfy one or more quality metrics. Each of the refined document ontologies is integrated together to generate the organization level ontology. Further, a knowledge retrieval system is operatively coupled to the ontology generation system and processes one or more search queries using the generated organization level ontology.

Automatic expansion of a knowledge information dictionary for speech semantic analysis and generation of responses for a dialogue agent are performed in a favorable manner. Category tags are assigned to each term in input speech for all categories when terms are registered in the knowledge information dictionary. A domain of speech content intended by the input speech is estimated, and terms pertaining to the estimated domain are extracted from the input speech as a phrase of a predetermined entity. A response is generated on the basis of the domain of the speech content intended by the input speech and the phrase of the predetermined entity. When a category tag is not assigned to the phrase of a predetermined entity, the phrase of the predetermined entity is registered for the category corresponding to the predetermined entity in the knowledge information dictionary. The knowledge information dictionary has a hierarchical structure, and the application unit generates the response using the hierarchical structure.

Guided exploration of data is provided. A semantic graph corresponding to a dataset is generated using identified relations among columns of the dataset that are identified based on mapping the columns to main concepts in a generic ontology. A subgraph of the semantic graph is formed based on identification of nodes corresponding to relevant central concepts within the semantic graph using graph centrality metrics. A plurality of paths is identified in the subgraph using a bi-directional multiple hop search from the nodes corresponding to the relevant central concepts in the subgraph. A relevance score is assigned to each path in the plurality of paths using a graph-theoretic metric and the graph centrality metrics. A set of natural language questions based on relevant central concepts and concept relations corresponding to nodes in each respective path with an assigned relevance score greater than a minimum threshold score is output to the user.

Processor-implemented systems and methods are provided for generating clusters of technical documents. A method includes analyzing degrees of similarity among the technical documents using a hierarchical taxonomy code similarity model and a text clustering model. Clusters of the technical documents are generated based upon the analyzed degrees of similarity from the models.

Existing systems for document processing are either based on a supervised approach using annotated tags, and these systems identify section-based data from the unstructured documents without considering the statistical variations in content, which results in highly inaccurate content extraction. The disclosure herein generally relates to document processing, and, more particularly, to method and system for document indexing and retrieval. The system provides a mechanism to correlate unique words in a document with different topics identified in the document, based on a word pattern identified from the document. The correlations are captured in a knowledge graph, and can be further used in applications such as but not limited to document retrieval.

Aspects described herein may relate to methods, systems, and apparatuses that determine one or more categories associated with a dataset, or a portion thereof. The determination may be performed based on one or more tags associated with the dataset and/or a description associated with the dataset. Further, the determination may be performed by searching an ontology based on the one or more tags and/or the description. The determination may be performed by using a machine-learning model based on the one or more tags and/or the description. Once the one or more categories associated with the dataset are determined, the one or more categories may be used as a basis for modifying the dataset and/or validating the dataset.

An ontological modeling method for flower pests and diseases based on knowledge graph, including: extracting multiple property elements of a flower pests and diseases domain from text; constructing an ontology model including a triple unit; tagging a head entity array and a tail entity array of the triple unit; constructing a joint extraction framework model; constructing a knowledge graph-based knowledge extraction framework; and converting a resource description framework (RDF) in the triple unit into a property graph; and storing the property graph in a Neo4J graph database. A system for implementing the ontological modeling method is also provided.

Systems and methods are used for generating and managing digital data files. More specifically, the system and method generate and manage digital contracts that execute the contractual terms of corresponding underlying legal contracts agreed between the parties. The system and method generate, for each digital contract, a corresponding master document data file representing the underlying physical legal contract document containing the contractual terms and conditions agreed between the parties.

Techniques include obtaining a location of a trigger word located in unstructured text of a natural-language-text document; determining, based on the location, a set of words following the trigger word in the unstructured text; conducting a lattice decoding operation for the set of words to determine a clause associated with the trigger word, the operation comprising: determining a clause decoding lattice for the set of words defining one or more paths, between the trigger word and the end of clause token, through the set of words; selecting a path of the clause decoding lattice; and determining, based on the path selected, the clause including one or more words of the set of words that correspond to the path selected; and generating and storing a data model object including the clause associated with the trigger word.