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 for automatic data preprocessing for a machine learning operation by a processor. For each data instance in a set of data instances, a sequence of actions may be automatically learned in a reinforcement learning environment to be applied for preprocessing each data instance separately. Each of the data instances may be preprocessed for use by one or more machine learning models according to the learned sequence of actions.

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: 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: 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: 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: Techniques facilitating automatic feature extraction from a relational database are provided. In an embodiment, a method can include generating an entity graph based on a relational database, wherein the entity graph comprises a first node associated with a first table in the relational database and a second node associated with a second table in the relational database. In another embodiment, the method can include joining the first table and the second table based on an edge between the first table and the second table defined by the entity graph, wherein a resulting joined table is connected by a column of data. In another embodiment, the method can include extracting a feature from the column of data using a data mining algorithm selected from a set of data mining algorithms based on a type of data in the column of data.

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: Embodiments for automatic visualization and explanation of feature learning output for predictive modeling in a computing environment by a processor. A degree of importance score may be assigned to one or more features from a relational database according to the machine learning model. A visualization graph of one or more join paths and the one or more features with the degree of importance score to predict a target variable may be generated.

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: In an approach to unsupervised feature learning for relational data, a computer trains one or more entity aware autoencoders on one or more tables in a relational database, where each of the one or more entity aware autoencoders corresponds to one of the one or more tables in the relational database, and where each of the one or more entity aware autoencoders are comprised of an encoder and a decoder. A computer transforms each of the one or more tables in the relational database with the encoder of the corresponding trained entity aware autoencoder. A computer joins a first transformed table of the one or more tables in the relational database with each remaining one or more transformed tables in the relational database to form one or more joined tables. A computer aggregates the one or more joined tables. A computer outputs one or more feature representations.

Abstract: Embodiments for intelligent automated feature engineering for relational data in a computing environment by a processor. Indices may be automatically selected and built from one or more columns of one or more tables in a relational database using one or more automated feature engineering models that include a set of queries. One or more features may be determined using a set of queries of an automated feature engineering models to execute for a scoring operation.

Abstract: Embodiments for automatic feature learning for predictive modeling in a computing environment by a processor. A first table and a second table are joined based on an edge between the first table and the second table defined by an entity graph thereby creating a resulting joined table that is connected by a column of data. The resulting joined table is used as an input into one or more neural network operations that transform the resulting joined table to one or more features to predict a target variable.

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.