Abstract: Disclosed is a system and a method for semantic search in an enterprise. The method comprises defining entities in the enterprise, indexing content from one or more data sources of the enterprise to determine relationships between individual pieces of the content and one or more of the entities, providing an interface to allow a user to enter a search query, parsing the search query, determining relevant pieces of the content based on the parsed search query, identifying the one or more of the entities for each of the relevant pieces of content based on the relationships therebetween and configuring the interface to display each of the relevant pieces of the content under respective one or more of the entities in response to the search query.

Abstract: A method and system of predicting molecular properties is provided herein. The method includes representing a molecule as a graph and a string. The method further includes encoding the graph into a first feature representation and the string into a second feature representation, using a graph neural network and a transformer-based network, respectively. The method further includes concatenating the first feature representation obtained from the graph neural network and the second feature representation obtained from the transformer-based network to create a combined feature representation. The method further includes fusing the combined feature representation using a linear layer to obtain a synergistic combined feature representation for the molecule. The method further includes predicting one or more molecular properties for the molecule using the synergistic combined feature representation and a predictor network.

Abstract: A method and system for optimal scheduling of nursing services in a hospital is provided herein. The method and system comprises forecasting demand for the nursing services for a time interval based on historical data and external data. The method and system further comprises extracting nurses' availability data for the time interval. The method and system comprises aggregating constraints applicable to the nursing services. The method and system also comprises computing a schedule of nurses based on the constraints using at least an optimization model.

Abstract: A method and system for augmenting a neural network is provided herein. The method comprises connecting an input layer to a pre-input layer. The method further comprises joining a hidden layer to the input layer. The method comprises linking an output layer to the hidden layer. The method further comprises connecting a layer for computing physics equations to the output layer. The neural network system further comprising, an input layer, a hidden layer connected to the input layer and an output layer joined to the hidden layer. The system further comprising a layer for computing physics equations connected to the output layer and a pre-input layer attached to the input layer.

Abstract: A method and system for forecasting demand for nursing services in a hospital herein. The method and system comprises accessing external data and historical data of a hospital. The method and system further comprises combining the external data and historical data of a hospital to form a structured data aggregation. Further, the structured data aggregation is processed. The method and system further comprises forecasting for a time interval, the demand for nursing services. In addition, work drivers are an accurate workload indicator for nurse workload planning and takes into consideration patient-dependent diversified needs. Furthermore, the system comprises a productivity index that accounts for nurses down-time and administrative tasks that need to be subtracted from the time spent on clinical tasks. This is used to schedule accurately the nurse rota based on realistic hospital needs and uses historic data to predict the upcoming demand over different ranges of time-horizons.

Abstract: A method and system for generating conversation flows is provided herein. The method and system comprise storing conversations between at least an agent and at least a user in logs. Further, the method and system comprise extracting topics from the logs. Also, the method and system comprise creating clusters from the extracted topics. The method and system further comprise generating the conversation flows from the clusters.

Abstract: A method and system for training a virtual agent is provided herein. The method and system comprises storing conversations between the virtual agent and a user in logs. The method and system further comprises mining the logs to retrieve utterances. The method and system further comprises computing regression for each of the plurality of the charging time segments. The method and system further comprises providing a score to the utterances. Further, the method ranking the utterances based on the score.

Abstract: The disclosure provides a system, a method, and a computer program product for displaying a a Knowledge Graph at a client device. The method comprises receiving a sequence of selection elements in the Knowledge Graph, by a user. The method may include displaying the sequence of selected elements in a last in, first out order on selecting a UI component by the user. Also, the method may enable saving a state of display on closing display of the Knowledge Graph. Further, on reopening the display of the Knowledge Graph the saved state is rendered. The method also provides box select and panning feature for visually manipulating the Knowledge Graph. Further, in order to optimally execute the above features, nodes and edges which are the elements of a Knowledge Graph are stored in array data structures.

Abstract: The disclosure provides a system, a method, and a computer program product for displaying a sentence in a Knowledge Graph at a client device. The method comprises displaying, at the client device, at least an entity in the Knowledge Graph as a node. The method may include displaying, at the client device, at least a relation in the Knowledge Graph as an edge. The method may further include, displaying, at the client device, a sentence on selecting the edge of the Knowledge Graph, wherein the sentence is a natural language expression associated with the relation. The method also allows for visually adjusting the display density of the Knowledge Graph.