Abstract: Techniques for the generation of variable natural language descriptions from structured data are disclosed including receiving input data and generating a first plurality of tuples based on the received input data. A first plurality of sentences and a corresponding second plurality of tuples are obtained from a data repository based on the first plurality of tuples. A second plurality of sentences is generated based on the second plurality of tuples, the first plurality of sentences, and the first plurality of tuples. A sentence is selected from the first plurality of sentences and the second plurality of sentences for each of the first plurality of tuples. At least one paragraph variation is generated where each paragraph variation is generated based on two or more of the selected sentences. The at least one paragraph variation is presented as a natural language description of the input data.

Abstract: Systems and methods are provided for generating nested queries from natural language queries. In particular, system and methods are provided to implement natural language interfaces to databases (NLIDB) frameworks which are configured to apply intelligent reasoning over domain semantics to detect and generate nested queries across different domains without the need for domain specific training or utilizing domain-specific semantic templates for mapping a natural language query to a structured query.

Abstract: Methods, systems, and computer program products for unsupervised tunable stylized text transformations are provided herein. A computer-implemented method includes identifying stylistically amendable portions of input text by applying at least one neural network to the input text; determining stylistic text modifications to the amendable portions of the input text, the text modifications encompassing a set of stylistic parameters, wherein the determining comprises applying at least one neural network to the set of stylistic parameters; generating a stylized output set of text by transforming the input text, wherein the transforming comprises modifying at least one of the stylistically amendable portions of the input text via at least one of the stylistic text modifications encompassed by the set of stylistic parameters; and outputting the stylized output set of text to a user.

Abstract: Methods, systems, and computer program products for user-initiated dynamic data application programming interface creation are provided herein. A computer-implemented method includes automatically transforming a user-provided query into one or more parameterized queries, generating a data application programming interface based on at least one of the parameterized queries, outputting the generated data application programming interface to the user providing the user-provided query, and storing the generated data application programming interface in a database accessible to one or more additional users.

Abstract: Disclosed embodiments relate to a prefetcher for delinquent irregular loads. In one example, a processor includes a cache memory, fetch and decode circuitry to fetch and decode instructions from a memory; and execution circuitry including a binary translator (BT) to respond to the decoded instructions by storing a plurality of decoded instructions in a BT cache, identifying a delinquent irregular load (DIRRL) among the plurality of decoded instructions, determining whether the DIRRL is prefetchable, and, if so, generating a custom prefetcher to cause the processor to prefetch a region of instructions leading up to the prefetchable DIRRL.

Abstract: One embodiment provides a method, including: deploying a machine learning model, wherein the deployed machine learning model is used in responding to queries from users; receiving, at the deployed machine learning model, input from a user; identifying a type of machine learning model attack corresponding to the received input; computing, responsive to receiving the input, a resiliency score of the machine learning model, wherein the resiliency score indicates resistance of the machine learning model against the identified type of attack; and performing an action responsive to the computed resiliency score.

Abstract: One embodiment provides a method, including: receiving a natural language query from a user; identifying a plurality of interpretations for interpreting the natural language query, wherein the plurality of interpretations are based upon at least one ambiguity in the received natural language query; generating, for each of the plurality of interpretations, a plurality of example queries; generating, for each of the interpretations, both (i) an answer to the received natural language query and (ii) an answer to each of the generated plurality of example queries; and providing, to the user, (i) the generated answer for each interpretation of the natural language query and (ii) a plurality of question/answer pairs for each of the identified plurality of interpretations that assists in disambiguating the ambiguity, wherein each question/answer pair comprises at least one of the generated plurality of example queries and the corresponding generated answer to the example query.

Abstract: One embodiment provides a method, including: receiving, from a user, a natural language query; generating an ontology subgraph by mapping the natural language query to a domain ontology and filtering the domain ontology based upon entities within the natural language query; producing a knowledge graph from (i) the generated ontology subgraph and (ii) a set of documents related to the natural language query, wherein the producing comprises extracting triples from the set of documents and aggregating the extracted triples to form the knowledge graph; and providing, to the user, a response to the received natural language query, wherein the returning the response comprises querying the produced knowledge graph using the natural language query.

Abstract: One embodiment provides a method, including: assigning a machine learning model signature to a machine learning model, wherein the machine learning model signature is generated using (i) data points and (ii) corresponding data labels from training data; receiving input comprising identification of a target machine learning model; acquiring a target signature for the target machine learning model by generating a signature for the target machine learning model using (i) data points from the assigned machine learning model signature and (ii) labels assigned to those data points by the target machine learning model; determining a stolen score by comparing the target signature to the machine learning model signature and identifying the number of data labels that match between the target signature and the machine learning model signature; and classifying the target machine learning model as stolen based upon the stolen score reaching a predetermined threshold.

Abstract: Aspects of the present disclosure relate to automated ontology refinement based on query inputs and provided feedback. A query input is received for an ontology. Features of the query input are analyzed, wherein analyzation includes determining syntactical and semantic characteristics of the features of the query input. Based on the determined syntactical and semantic characteristics, ontological elements are classified for each feature of the query input. The ontological element for each feature of the query input is then compared to a set of ontological elements of the ontology. Based on the comparison, a response to the query input is received, along with a request for feedback regarding the response. Feedback is then received regarding the response. Based on the feedback, the ontology is analyzed to determine at least one deficiency of the ontology. The ontology is then refined to correct the at least one deficiency.

Abstract: A method, computer system, and computer program product for a conversational system driven by a semantic network with a library of templated query operators are provided. The embodiment may include loading one or more operators for the conversational system to the library of templated query operators. The embodiment may also include receiving a query statement from a user. The embodiment may further include identifying an operator from the library to process the received query. The embodiment may also include identifying one or more input terms for the identified operator within the received query. The embodiment may further include generating one or more output terms based on processing the one or more identified input terms using the identified operator. The embodiment may also include generating a natural language response to the received query based on the one or more generated output terms.

Abstract: Methods, systems, and computer program products for user-initiated dynamic data application programming interface creation are provided herein. A computer-implemented method includes automatically transforming a user-provided query into one or more parameterized queries, generating a data application programming interface based on at least one of the parameterized queries, outputting the generated data application programming interface to the user providing the user-provided query, and storing the generated data application programming interface in a database accessible to one or more additional users.

Abstract: The present invention may include a computing device receives a query and determines a plurality of implementations based on the query and searching the ontology driven system. The computing device may display the determined plurality of implementations and monitors the eye tracking datum for a fixation. The computing device may determine the corresponding plurality of implementations associated with a region where the fixation is directed based on determining the fixation. The computing device may determine a plurality of candidate elements of the plurality of implementations in the region based on previous determination of the corresponding plurality of implementations. The computing device may generate a plurality of preferred interpretations based on monitoring the eye tracking datum, the corresponding plurality of implementations associated with the region of the fixation, and the plurality of candidate elements.

Abstract: Methods, systems, and computer program products for translating structured languages to natural language using domain-specific ontology are provided herein. A computer-implemented method includes determining similarities among multiple natural language query interpretations derived from an input query, determining differences among the multiple natural language query interpretations, and generating natural language descriptions of each of the multiple natural language query interpretations based on analysis of the determined similarities, the determined differences, and the input query. The method also includes producing, for each of the natural language query interpretations, a natural language string that represents one or more unambiguous interpretations of the input query, wherein the producing comprises consolidating the generated natural language descriptions. Further, the method includes outputting each of the produced natural language strings to a user.

Abstract: Methods, systems, and computer program products for carrying out interactive dialog in natural language using an ontology are provided herein. A computer-implemented method includes generating one or more domain-driven interpretations of a natural language dialogue query provided by a user via utilization of a web ontology language; determining multiple structured base queries, from among a stored collection of structured queries, that correspond to the natural language dialogue query, in view of the one or more generated domain-driven interpretations; selecting one of the multiple determined structured base queries, based on one or more items of context information pertaining to the dialogue; automatically generating a response to the selected structured base query; and outputting the generated response to the user.

Abstract: In one embodiment, a processor comprises a processor optimization unit. The processor optimization unit is to collect runtime information associated with a computing device, wherein the runtime information comprises information indicating a performance of the computing device during program execution. The processor optimization unit is further to receive runtime optimization information for the computing device, wherein the runtime optimization information comprises information associated with one or more runtime optimizations for the computing device, and wherein the runtime optimization information is determined based on an analysis of the collected runtime information. The processor optimization unit is further to perform the one or more runtime optimizations for the computing device based on the runtime optimization information.

Abstract: Methods, systems, and computer program products for translating structured languages to natural language using domain-specific ontology are provided herein. A computer-implemented method includes determining similarities among multiple natural language query interpretations derived from an input query, determining differences among the multiple natural language query interpretations, and generating natural language descriptions of each of the multiple natural language query interpretations based on analysis of the determined similarities, the determined differences, and the input query. The method also includes producing, for each of the natural language query interpretations, a natural language string that represents one or more unambiguous interpretations of the input query, wherein the producing comprises consolidating the generated natural language descriptions. Further, the method includes outputting each of the produced natural language strings to a user.

Abstract: Methods, systems, and computer program products for repeat execution of RCA logic through run-time discovered topology pattern maps are provided herein. A computer-implemented method includes determining one or more topology paths of configuration items from a collection of multiple views of a target system; identifying one or more patterns from the one or more topology paths of configuration items; comparing the one or more identified patterns against a collection of data comprising mappings between (i) multiple root cause analysis logical expressions and (ii) multiple system architecture patterns, thereby identifying one or more of the multiple root cause analysis logical expressions to be executed on the one or more topology paths of configuration items; and generating a user-accessible link via a uniform resource locator for direct invocation of the one or more identified multiple root cause analysis logical expressions executed on the one or more topology paths of configuration items.

Abstract: One embodiment provides a method, including: utilizing at least one processor to execute computer code that performs the steps of: receiving a partially completed query request from a user, wherein the partially completed query request comprises at least one text string provided in a query field; mapping the at least one text string to at least one ontology element contained within at least one of a plurality of partitions in an ontology graph, the graph comprising (i) ontology elements represented as nodes and (ii) connections between ontology elements represented as edges, wherein each of the plurality of partitions corresponds to a subject area of a field and comprises ontology elements corresponding to concepts related to the subject area of the field; and generating, in view of the plurality of partitions, at least one suggestion for completing the query request to the user, wherein the at least one suggestion comprises a semantically relevant suggestion corresponding to the at least one text string prov

Abstract: Methods, systems, and computer program products for repeat execution of RCA logic through run-time discovered topology pattern maps are provided herein. A computer-implemented method includes determining one or more topology paths of configuration items from a collection of multiple views of a target system; identifying one or more patterns from the one or more topology paths of configuration items; comparing the one or more identified patterns against a collection of data comprising mappings between (i) multiple root cause analysis logical expressions and (ii) multiple system architecture patterns, thereby identifying one or more of the multiple root cause analysis logical expressions to be executed on the one or more topology paths of configuration items; and generating a user-accessible link via a uniform resource locator for direct invocation of the one or more identified multiple root cause analysis logical expressions executed on the one or more topology paths of configuration items.