A confidentiality preserving system and method for performing a rank-ordered search and retrieval of contents of a data collection. The system includes at least one computer system including a search and retrieval algorithm using term frequency and/or similar features for rank-ordering selective contents of the data collection, and enabling secure retrieval of the selective contents based on the rank-order. The search and retrieval algorithm includes a baseline algorithm, a partially server oriented algorithm, and/or a fully server oriented algorithm. The partially and/or fully server oriented algorithms use homomorphic and/or order preserving encryption for enabling search capability from a user other than an owner of the contents of the data collection. The confidentiality preserving method includes using term frequency for rank-ordering selective contents of the data collection, and retrieving the selective contents based on the rank-order.

A system for conducting a search of dual databases for conducting a dual search, where at least two databases are searched simultaneously by entering a single search term comprising a processor and a memory to store a set of instructions wherein the processor accesses the instructions to receive a search term by a program, dissect the search term into its component parts using a dictionary algorithm, form a first search term having a first format of the first search term, form a second search term with a second format of the second search term, use the first search term to search a first database and saving a first result, use the second search term to search a second database and saving a second result and combine the first and second results in a single display page.

The present specification provides a method and apparatus for determining feature words and a server. The method includes: obtaining text data; extracting a first feature word from the text data; updating a word segmentation library based on the first feature word to obtain an updated word segmentation library, the word segmentation library including a plurality of predetermined feature words for representing predetermined attribute types; and extracting a second feature word from the text data based on the updated word segmentation library and the predetermined feature words.

Techniques are disclosed for using a trained machine learning (ML) pipeline to identify categories associated with target data items even though the identified categories may not already be present in the hierarchy. The ML pipeline may include trained cluster-based and classification-based machine learning models, among others. If the results of the cluster-based and classification-based machine learning models are the same, then the target data items is assigned to a hierarchical classification consistent with the identical results of the machine learning model. An assigned hierarchical classification may be validated by the operation of subsequent trained ML models that determine whether parent and child categories in the identified classification are properly associated with one another.

Provided are systems and methods which can use machine learning to draw additional inferences about transaction records from transaction strings. The inferred data can be used to build a classification model configured to map transaction string to predefined categories. In one example, a method may include receiving a file comprising transaction strings corresponding to a plurality of transaction records, executing the machine learning model on the transaction strings to identify a plurality of categories associated with the transaction strings, generating a classifier model that comprises patterns of keywords from the transactions strings mapped to the plurality of identified categories, respectively, and storing the classifier model in the data store.

Computing systems of a multi-tenant trusted domain collect metadata describing data stored in data sources of a set of tenant trusted domains. The computing systems of the multi-tenant trusted domain use the metadata to process natural language questions based on data stored in data sources of a tenant trusted domain. The computing systems of the multi-tenant trusted domain identify a set of data sources of the tenant trusted domain that are relevant for processing the natural language question and generate an execution plan for answering the natural language question. The computing systems of the multi-tenant trusted domain send the execution plan to one or more computing systems of the tenant trusted domain. The computing systems of the tenant trusted domain execute the execution plan and send the result of executing the execution plan to a client device that sent the natural language question.

The present disclosure relates to a method for communicating between an electronic tag and a computer connected to the internet, wherein the electronic tag: encrypts at least part of the information to be transmitted, using a data format preserving algorithm; generates a URL comprising at least the encrypted part of the information; and transmits the URL to an NFC reader.

System, apparatus and method for managing knowledge generated from technical data are disclosed. The method comprising receiving a user query for technical data stored as a knowledge base (842A) on a knowledge-based system (842); determining, by an inference engine (822), a contextual relevance between the user query and the knowledge base (842A), wherein the knowledge base (842A) comprises a query-able framework of the technical data including processed textual sections and indexed images; identifying textual sections and images of the knowledge base (842A) associated with the user query based on the contextual relevance; determining, by the inference engine (822), relevancy of the identified textual sections and indexed images based on frequency of terms in the query with respect to the identified textual sections and the indexed images; and generating, by the inference engine (822), a response (818A) to the user query including extracted textual sections and indexed images having a relevancy score that exceeds a threshold.

A method for automatically splitting row-based table content into columns is provided, including: receiving first table content sent by a client, the first table content including one or more rows of text data to be split into columns; performing information extraction on the one or more rows of text data in the first table content to obtain an information tag in the one or more rows of text data; performing column splitting on the one or more rows of text data according to the information tag to obtain second table content, the second table content comprising one or more columns of text data after the column splitting; and transmitting the second table content to the client.

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.