Abstract: Embodiments are provided for unsupervised learning of domain specific knowledge graph from textual data and language generation from knowledge graph via reinforcement learning in a computing system by a processor. Unstructured data is automatically parsed into one or more knowledge graphs based on the unstructured data and a list of candidate relations using a first machine learning model. Text data is generated from the one or more knowledge graphs using a second machine learning model.

Abstract: Embodiments for providing an enhanced codebase in a computing environment by a processor. One or more container specification files may be automatically generated for a codebase based on one or more extracted attribute names and values.

Abstract: Embodiments are provided for providing predictive computing and data analytics for project management in a computing system by a processor. A lifecycle of each of a plurality of objects may be monitored based on data received from a plurality of data sources. Predictive analytics for project management of each of the plurality of objects may be provide based on monitoring the lifecycle of each of the plurality of objects.

Abstract: Embodiments for providing an enhanced codebase in a computing environment by a processor. One or more container specification files may be automatically updated with one or more changes to a codebase.

Abstract: A computer-implemented method for automatically generating a semantic vector representation of a relation between a specific set of entities in natural language text is provided. The method may include, in response to receiving a text segment comprising a set of entities, automatically parsing the text segment into an abstract meaning representation (AMR) graph comprising nodes representing the set of entities. The method may further include extracting a number of minimum Steiner trees from the AMR graph, and wherein each Steiner tree comprises a minimum amount of edges between the nodes corresponding to a first entity and at least one second entity. The method may further include using a trained graph neural network (GNN) to determine vector embeddings for the minimum Steiner trees. The method may further include aggregating the vector embeddings to generate the semantic vector representation of the relation between the specific set of entities.

Abstract: The present inventive concept provides for a method for automated decision modelling from text including obtaining a text corpus including a policy. Terms and syntax are identified within the text corpus related to the policy. Sentence similarities and co-references based on the terms and syntax are identified. Discourse and sentence level semantic parsing is performed based on the terms and the sentence similarities and the co-references using machine learning. A decision model template is generated based on the discourse and semantic parsing, and the decision model template is transformed into an automated decision model.

Abstract: A method, system, and computer program product are disclosed. The method includes receiving an input text and generating a set of virtual triples, which include pairs of named entities from the input text and relation embedding vectors for each of the pairs, from the input text. The method also includes constructing a virtual knowledge graph (KG) with the set of virtual triples and transforming the virtual KG into a relation-cluster KG. Further, the method includes mining logical rules from the relation-cluster KG.

Abstract: Embodiments for providing an enhanced codebase in a computing environment by a processor. One or more container specification files may be automatically generated for a codebase based on one or more extracted attribute names and values.

Abstract: Software that performs the following operations: (i) receiving a set of graph predictions corresponding to an input text, where graph predictions of the set of graph predictions are generated by different respective machine learning models; (ii) blending the graph predictions of the set of graph predictions to generate a plurality of candidate blended graphs, where nodes and edges of the candidate blended graphs have respective selection metric values, generated using a selection metric function, that meet a minimum threshold; and (iii) selecting as an output blended graph a candidate blended graph of the plurality of candidate blended graphs having a highest total combination of selection metric values among the plurality of candidate blended graphs.

Abstract: Various embodiments are provided for correlating regulatory data in a computing environment by a processor. A rule may be associated with one or more textual paragraphs extracted from a policy document that describes at least a portion of the rule.

Abstract: In an approach for predicting an effectiveness of a given social program for a given patient at one or more points in time and for providing a score indicating the accuracy of the prediction and an explanation of the prediction, a processor receives a request from a user. Responsive to determining a social program database contains historical data on the given social program, a processor analyzes a set of patients associated with the given social program. A processor predicts the effectiveness of the given social program for the given patient at the one or more points in time using a prediction model trained to predict an effectiveness score and a confidence score. A processor outputs a prediction of the effectiveness of the given social program for the given patient at the one or more points in time to the user.

Abstract: Embodiments are provided for generating a reasonable language model learning for text data in a knowledge graph in a computing system by a processor. One or more data sources and one or more triples may be analyzed from a knowledge graph. Training data having one or more candidate labels associated with one or more of the triples may be generated. One or more reasonable language models may be trained based on the training data.

Abstract: In an approach for explaining a theorem proving model, a processor predicts a truth value of a query through a pre-trained theorem proving model, based on the query and one or more facts and rules in a knowledge base. A processor ranks the one or more facts and rules according to contribution, calculated in a pre-defined scoring method, made to the predicted truth value of the query. A processor generates a proof of the predicted truth value, wherein the proof is one or more logical steps that demonstrate the predicted truth value in a natural language. A processor outputs the proof.

Abstract: Methods, computer program products, and/or systems are provided that perform the following operations: obtaining a textual knowledge base; filtering the textual knowledge base to obtain a subset of the textual knowledge base, wherein the filtering is based on textual query data; generating reasoning data based on the subset of the textual knowledge base and the textual query data; generating classification data based on the subset of the textual knowledge base, the textual query data, and the reasoning data; and providing label data as output for the textual query data based on the classification data.

Abstract: A processor may identify a task to be performed by a group of users. The processor may determine one or more requirements for performance of the task. The processor may determine, from one or more categories of users, potential users for the group of users. The processor may analyze one or more metrics of the potential users, where the one or more metrics of the potential users includes a first physical metric. The processor may generate, utilizing an AI model, one or more suggested groups of suggested users based on the one or more metrics of the potential users.

Abstract: Extracting demographic features from audio streams in a crowd environment includes receiving audio stream signals from a predefined geographical area containing a plurality of individuals, recording the received audio stream signals, extracting demographic features from the recorded audio stream signals, aggregating the extracted demographic features, storing the aggregated demographic features in a database and analyzing aggregated demographic features to generate a summary of demographic characteristics of the plurality of individuals in the predefined geographical area. Demographic features may be aggregated at different levels of granularity. The method and system may include extracting spatial information of the recorded audio stream signals within the geographical area, determining spatial distribution of the aggregated demographic features within the geographical area based on the extracted spatial information and including the spatial distribution in the summary of demographic characteristics.

Abstract: Detecting an anomaly in deep learning programming can include receiving a deep learning program with neural network. A pre-trained machine learning model can be run with the deep learning program with neural network as input. The pre-trained machine learning model detects whether the neural network includes a detaching subgraph. Responsive to detecting that the neural network includes a detaching subgraph, a location of the deep learning program causing the detaching subgraph can be output. The neural network of the deep learning program can be run in training mode and weight gradients associated with training of the neural network can be monitored. Based on the monitoring, occurrence of one or more detaching subgraphs can be detected. Responsive to detecting a detaching subgraph, the detaching subgraph can be output. A suggestion to correct the deep learning program can also be output.

Abstract: Process for inducing resistance to diphtheria toxin in a human cell, wherein the process is carried out in vitro and/or in vivo in a rodent xenografted with a human cell, the process comprising: a) providing at least one expression vector containing at least one nucleotide sequence targeting by RNA interference the DPH2 gene transcript of the human cell; c) transducing the at least one vector into the human cell; d) allowing transcription in the human cell, from the at least one expression vector, of the at least one nucleotide sequence targeting by RNA interference the DPH2 gene transcript; e) obtaining silencing of the DPH2 gene in the human cell, whereby the human cell is resistant to diphtheria toxin.