Abstract: A computer-implemented method, a computer system and a computer program product enhance user input to a conversational agent. The method includes identifying a word using a natural language processing algorithm. The word is in a query from a user to the conversational agent. The method also includes determining a set of keywords related to each of a plurality of intents associated with the conversational agent within a database. In addition, the method includes calculating a semantic similarity score between each determined set of keywords and the identified word. Lastly, the method includes displaying the identified word on a screen to the user when the identified word is not found in each determined set of keywords and when the semantic similarity score is above a threshold. The display includes a distinct indication of the identified word.

Abstract: An embodiment includes identifying metadata attached to a first node of a graph, where the metadata satisfies an ontological condition. The embodiment also includes transforming the graph such that the transforming results in the graph having a new graph structure, where the transforming of the graph comprises removing the metadata from the first node and adding a second node representative of the metadata removed from the first node to the graph such that the second node is connected to the first node by a first edge.

Abstract: Technology for applying explainable artificial training algorithms (XAI) to training machine learning algorithms for identifying potentially developing computer vision syndrome (CVS), CVS and/or recommended remedial action(s) that a user can perform to counter potentially developing CVS and/or existing CVS. In some embodiments, the XAI includes a Contrastive Explainability model. In some embodiments, the training performed by the XAI includes assigning weight factors respectively to CVS input parameters (for example, blink rate) based upon how strong the respective CVS input factor is correlated with development of CVS in the user.

Abstract: A machine learning model can be depicted as a graph. After identifying a familiarity level of a user, the graph of the machine learning model can be customized. The customization is based on the familiarity level of the user. The customized graph of the machine learning model is displayed to the user.

Abstract: Technology for applying explainable artificial training algorithms (XAI) to training machine learning algorithms for identifying potentially developing computer vision syndrome (CVS), CVS and/or recommended remedial action(s) that a user can perform to counter potentially developing CVS and/or existing CVS. In some embodiments, the XAI includes a Contrastive Explainability model. In some embodiments, the training performed by the XAI includes assigning weight factors respectively to CVS input parameters (for example, blink rate) based upon how strong the respective CVS input factor is correlated with development of CVS in the user.

Abstract: A machine learning model can be depicted as a graph. After identifying a familiarity level of a user, the graph of the machine learning model can be customized. The customization is based on the familiarity level of the user. The customized graph of the machine learning model is displayed to the user.

Abstract: Provided are embodiments for a computer-implemented method for using social media content to influence driving behavior. The computer-implemented method includes determining, by a processing engine, a user driving behavior, analyzing a social media content of the user, and correlating the user driving behavior and the social media content analysis. The computer-implemented method also includes determining a current user state, and generating a recommendation based at least in part on the current user state and the correlation between the user driving behavior and the social media content. Also provided are embodiments for a computer program product and system for using social media content to influence driving behavior.

Abstract: An embodiment includes identifying metadata attached to a first node of a graph, where the metadata satisfies an ontological condition. The embodiment also includes transforming the graph such that the transforming results in the graph having a new graph structure, where the transforming of the graph comprises removing the metadata from the first node and adding a second node representative of the metadata removed from the first node to the graph such that the second node is connected to the first node by a first edge.

Abstract: An approach is provided for granting a security permission to a payment account. A relationship between an owner of an account and a user is determined by using a knowledge graph. A pattern of purchases made by the user is determined by using a machine learning module. Based on (i) the relationship between the owner of the account and the user and (ii) the pattern of the purchases, a security permission is dynamically granted to the user for a usage of the account.