Abstract: One embodiment provides a method that includes determining candidate ontologies for alignment from multiple available knowledge bases. An initial target ontology is selected from the candidate ontologies and correcting the initial selected ontology with received refinement input. Concepts in the selected initial ontology are aligned with concepts of the target ontology using a deep learning hierarchical classification with received review input. A user is assisted to build, change and grow the selected initial ontology exploiting both the target ontology and new facts extracted from unstructured data.

Abstract: Embodiments relate to a system, program product, and method for use with a physical computing device to process a data access request. The requested data is encrypted with two keys, including a physical device authentication key and a transient key. Access to the data requires authentication on both the device level and situational level. Device situational data is monitored, which includes selectively enabling access to the requested data and de-activation of the transient key in response to a change in the monitored situational data. The transient key de-activation removes access to the requested data.

Abstract: One embodiment provides a method that includes obtaining a default language corpus. A second language corpus is obtained based on a second language preference. A first transcription of an utterance is received using the default language corpus and natural language processing (NLP). At least one problem word in the first transcription is determined based on an associated grammatical relevance to neighboring words in the first transcription. Upon determining that a first probability score is below a first threshold, an acoustic lookup is performed for an audible match for the problem word in the first transcription based on an associated acoustical relevance. Upon determining that a second probability score is below a second threshold, it is determined whether a match for the problem word exists in the secondary language corpus. Upon determining that the match exists in the secondary language corpus, a second transcription for the utterance is provided.

Abstract: Embodiments relate to a system, program product, and method for information extraction and annotation of a data set. Neural models are utilized to automatically attach machine annotations to data elements within an unlabeled data set. The attached machine annotations are evaluated and a score is attached to the annotations. The score reflects a confidence of correctness of the annotations. A labeled data set is iteratively expanded with selectively evaluated annotations based on the attached score. The labeled data set is applied to an unexplored corpus to identify matching corpus data to populated instances of the labeled data set.

Abstract: One embodiment of the present invention provides a method comprising running a monitoring agent on a connected electronic device, and determining, via the monitoring agent, a level of hearing impairment of an individual user associated with the connected electronic device. The method further comprises, in response to determining the individual user has some level of hearing impairment, selecting a notification mechanism suitable for notifying the individual user based on the level of hearing impairment, and invoking the monitoring agent to notify the individual user of an event in accordance with the notification mechanism selected.

Abstract: Embodiments relate to a system, program product, and method for dictionary membership management directed at identifying ambiguity in semantic resources. A dictionary of seed terms is applied to a text corpus and matching items in the corpus are identified. The linguistic properties for each matching item are characterized and a context pattern of each matching item is constructed. Each context pattern is applied to the dictionary and matching content between the seed terms and the context pattern is identified and quantified. Lexicon items from the dictionary that have anomalous behavior reflected in the quantification are identified. One or more seed words identified as having anomalous behavior are selectively removed from the dictionary.

Abstract: An example operation may include one or more of receiving a transaction request for a first transaction into a blockchain network, determining one or more potentially linked transactions subsequent to the first transaction, determining one or more nodes required for the first transaction and the one or more potentially linked transactions, determining an availability for the one or more nodes, for example, by analyzing social network usage at the respective nodes, and determining, from the availability of the one or more nodes, a preferred time to initiate the first transaction.

Abstract: One embodiment provides a method including monitoring social media application usage for particular users over a time period for media feeds and postings of content. Based on the monitoring, the method determines specific times to render content position, dimension sizes and flow rate. Connection speeds are distinguished for the particular users within the social media application. New social media feeds and new postings of content are dynamically reorganized and prioritized based on the connection speeds for the particular users.

Abstract: One embodiment provides a method including identifying all computing nodes and connections associated with the computing nodes in a data center based on running processes in the data center that communicate with one another. For each computing node, running processes are identified using natural language processing (NLP) by: iteratively refining a rule set that enables processing of surveillance information from the data center into an initial map of systems and applications in the data center, and extracting known process entities according to predetermined rules from the rule set. A visual dependency representation of the computing nodes and the processes running on the computing nodes is generated.

Abstract: One embodiment provides a method for relevant language-independent terminology extraction from content, the method including extracting lexicon items from the content based on context extraction patterns using statistical processing. Feedback on the extracted lexicon items is received to automatically tune scores and thresholds for the context extraction patterns. Available Linked Data is leveraged for a bootstrap source. The relevant language-independent terminology extraction is bootstrapped using the bootstrap source.

Abstract: One embodiment provides a method for on-demand relation extraction from unstructured text that includes obtaining a text corpus of domain related unstructured text. Representations of the unstructured text that capture entity-specific syntactic knowledge are created. Initial user seeds of informative examples containing relations are received. Extraction models in a neural network are trained using the initial user seeds. Performance information and a confidence score are provided for each prediction for each extraction model. A next batch of informative examples are identified for annotation from the text corpus based on training a neural network classifier on a pool of labeled informative examples. Stopping criteria is determined based on differences of the performance information and the confidence score in relation to parameters for each extraction model. Based on the stopping criteria, it is determined whether to retrain a particular extraction model after the informative examples have been labeled.

Abstract: Embodiments relate to a system, program product, and method for use with a physical computing device to process a data access request. The requested data is encrypted with two keys, including a physical device authentication key and a transient key. Access to the data requires authentication on both the device level and situational level. Device situational data is monitored, which includes selectively enabling access to the requested data and de-activation of the transient key in response to a change in the monitored situational data. The transient key de-activation removes access to the requested data.

Abstract: One embodiment provides a method that includes obtaining a default language corpus. A second language corpus is obtained based on a second language preference. A first transcription of an utterance is received using the default language corpus and natural language processing (NLP). At least one problem word in the first transcription is determined based on an associated grammatical relevance to neighboring words in the first transcription. Upon determining that a first probability score is below a first threshold, an acoustic lookup is performed for an audible match for the problem word in the first transcription based on an associated acoustical relevance. Upon determining that a second probability score is below a second threshold, it is determined whether a match for the problem word exists in the secondary language corpus. Upon determining that the match exists in the secondary language corpus, a second transcription for the utterance is provided.

Abstract: One embodiment provides a method that includes obtaining a default language corpus. A second language corpus is obtained based on a second language preference. A first transcription of an utterance is received using the default language corpus and natural language processing (NLP). At least one problem word in the first transcription is determined based on an associated grammatical relevance to neighboring words in the first transcription. Upon determining that a first probability score is below a first threshold, an acoustic lookup is performed for an audible match for the problem word in the first transcription based on an associated acoustical relevance. Upon determining that a second probability score is below a second threshold, it is determined whether a match for the problem word exists in the secondary language corpus. Upon determining that the match exists in the secondary language corpus, a second transcription for the utterance is provided.

Abstract: One embodiment provides a method comprising determining a recurring event involving a first vehicle based on location information for the first vehicle over a pre-determined period of time, and determining one or more recurring vehicles for the recurring event. Each recurring vehicle is a different vehicle, and a number of times the recurring vehicle is within proximity of the first vehicle over the pre-determined period of time satisfies a pre-determined threshold. The method further comprises establishing a vehicle social network including the first vehicle and the one or more recurring vehicles. The method further comprises generating a shared pool of resources and caching power by pooling together resources and caching power of each vehicle included in the vehicle social network, and utilizing the shared pool of resources and caching power to facilitate collaborative caching between vehicles in the vehicle social network.

Abstract: Embodiments relate to a system, program product, and method for use with a physical computing device to process a data access request. The associated data is encrypted with a key pair that includes both a persistent key and a transient key. Both keys require authentication to access the requested data. The transient key is subject to real-time monitoring, with changes in situational data selectively affecting the validity of the transient key, and selectively changing the physical state of the physical computing device.

Abstract: One embodiment provides a method including identifying all computing nodes and connections associated with the computing nodes in a data center based on running processes in the data center that communicate with one another. For each computing node, running processes are identified using natural language processing (NLP) by: iteratively refining a rule set that enables processing of surveillance information from the data center into an initial map of systems and applications in the data center, and extracting known process entities according to predetermined rules from the rule set. A visual dependency representation of the computing nodes and the processes running on the computing nodes is generated.

Abstract: Embodiments relate to a system, program product, and method for iterative expansion and application of a domain-specific dictionary. One or more dictionary instances are applied against a text corpus. The dictionary is iteratively expanded and selectively populated with one or more additional dictionary instances, including semantically similar instances to the applied dictionary instances and extension instances contextually related to the applied dictionary instances. The iteratively expanded dictionary is applied to an unexplored corpus to identify matching corpus data to populated instances of the dictionary.

Abstract: One embodiment provides a method including identifying all computing nodes and connections associated with the computing nodes in a data center. For each computing node, running processes are identified using natural language processing (NLP) by: extracting known process entities according to predetermined rules; extracting unknown process entities by: grouping process logs that share process entities and identifying hints in parameters and directory paths; receiving annotations to the hints to identify an application a process is running; and creating a new rule based on the annotations and propagating the new rule to other process logs. A visual representation of the computing nodes and the processes running on the computing nodes is generated.

Abstract: Embodiments relate to a system, program product, and method for knowledge resource management. A first document is subjected to a first semantic annotation and one or more entities, relations, and textual annotations of interest are identified. A neural model is built with the first document and trained with the first document and one or more of the first semantic annotations. An un-annotated document is applied to the neural model, and one or more second semantic annotations are produced. The un-annotated document is enriched with the produced second semantic annotation(s) and is subjected to adjudication. The neural model is selectively amended responsive to the adjudication.