Abstract: Methods and apparatus to facilitate annotation projects to extract structured information from free-form text using NLP techniques. Annotators explore text documents via automated preannotation functions, flexibly formulate annotation schemes and guidelines, annotate text, and adjust annotation labels, schemes and guidelines in real-time as a project evolves. NLP models are readily trained on iterative annotations of sample documents by domain experts in an active learning workflow. Trained models are then employed to automatically annotate a larger body of documents in a project dataset. Experts in a variety of domains can readily develop an annotation project for a specific use-case or business question. In one example, documents relating to the health care domain are effectively annotated and employed to train sophisticated NLP models that provide valuable insights regarding many facets of health care.

Abstract: A graph-based data storage and retrieval system in which multiple subgraphs representing respective datasets in different namespaces are interconnected via a linking or “canonical” layer. Datasets represented by subgraphs in different namespaces may pertain to a particular information domain (e.g., the health care domain), and may include heterogeneous datasets. The canonical layer provides for a substantial reduction of graph complexity required to interconnect corresponding nodes in different subgraphs, which in turn offers advantages as the number of subgraphs (and the number of corresponding nodes in different subgraphs) increases for the particular domain(s) of interest.

Abstract: Methods and apparatus to facilitate annotation projects to extract structured information from free-form text using NLP techniques. Annotators explore text documents via automated preannotation functions, flexibly formulate annotation schemes and guidelines, annotate text, and adjust annotation labels, schemes and guidelines in real-time as a project evolves. NLP models are readily trained on iterative annotations of sample documents by domain experts in an active learning workflow. Trained models are then employed to automatically annotate a larger body of documents in a project dataset. Experts in a variety of domains can readily develop an annotation project for a specific use-case or business question. In one example, documents relating to the health care domain are effectively annotated and employed to train sophisticated NLP models that provide valuable insights regarding many facets of health care.

Abstract: In various example embodiments, a system and methods are presented for converting query structures for information retrieval from graph-based data structures. The systems and methods receive a natural language query including a set of terms and generate an intermediate semantic relationship of the set of terms of the natural language query. The systems and methods generate a graph query including graph terms corresponding to the set of terms of the natural language query defined by a graph database. The systems and methods search one or more datasets associated with the graph database using the graph query and return a set of results based on the graph query.

Abstract: Methods and apparatus to facilitate annotation projects to extract structured information from free-form text using NLP techniques. Annotators explore text documents via automated preannotation functions, flexibly formulate annotation schemes and guidelines, annotate text, and adjust annotation labels, schemes and guidelines in real-time as a project evolves. NLP models are readily trained on iterative annotations of sample documents by domain experts in an active learning workflow. Trained models are then employed to automatically annotate a larger body of documents in a project dataset. Experts in a variety of domains can readily develop an annotation project for a specific use-case or business question. In one example, documents relating to the health care domain are effectively annotated and employed to train sophisticated NLP models that provide valuable insights regarding many facets of health care.

Abstract: A graph-based data storage and retrieval system in which multiple subgraphs representing respective datasets in different namespaces are interconnected via a linking or “canonical” layer. Datasets represented by subgraphs in different namespaces may pertain to a particular information domain (e.g., the health care domain), and may include heterogeneous datasets. The canonical layer provides for a substantial reduction of graph complexity required to interconnect corresponding nodes in different subgraphs, which in turn offers advantages as the number of subgraphs (and the number of corresponding nodes in different subgraphs) increases for the particular domain(s) of interest.

Abstract: Methods and apparatus to facilitate annotation projects to extract structured information from free-form text using NLP techniques. Annotators explore text documents via automated preannotation functions, flexibly formulate annotation schemes and guidelines, annotate text, and adjust annotation labels, schemes and guidelines in real-time as a project evolves. NLP models are readily trained on iterative annotations of sample documents by domain experts in an active learning workflow. Trained models are then employed to automatically annotate a larger body of documents in a project dataset. Experts in a variety of domains can readily develop an annotation project for a specific use-case or business question. In one example, documents relating to the health care domain are effectively annotated and employed to train sophisticated NLP models that provide valuable insights regarding many facets of health care.

Abstract: In various example embodiments, a system and methods are presented for converting query structures for information retrieval from graph-based data structures. The systems and methods receive a natural language query including a set of terms and generate an intermediate semantic relationship of the set of terms of the natural language query. The systems and methods generate a graph query including graph terms corresponding to the set of terms of the natural language query defined by a graph database. The systems and methods search one or more datasets associated with the graph database using the graph query and return a set of results based on the graph query.

Abstract: Methods and apparatus to facilitate annotation projects to extract structured information from free-form text using NLP techniques. Annotators explore text documents via automated preannotation functions, flexibly formulate annotation schemes and guidelines, annotate text, and adjust annotation labels, schemes and guidelines in real-time as a project evolves. NLP models are readily trained on iterative annotations of sample documents by domain experts in an active learning workflow. Trained models are then employed to automatically annotate a larger body of documents in a project dataset. Experts in a variety of domains can readily develop an annotation project for a specific use-case or business question. In one example, documents relating to the health care domain are effectively annotated and employed to train sophisticated NLP models that provide valuable insights regarding many facets of health care.

Abstract: Systems, methods, and computer program embodiments are disclosed for detecting software components in a software codebase. In an embodiment, a source file containing source code may be received, and a code signature may be generated for the source file based on a determined structure of the source code. The generated code signature may then be compared to signatures stored in a reference database to identify matching software files. In an embodiment, the reference database may store a plurality of code signatures corresponding to software files. A list of the identified software files may be created and presented to a user.

Abstract: In various example embodiments, a system and methods are presented for converting query structures for information retrieval from graph-based data structures. The systems and methods receive a natural language query including a set of terms and generate an intermediate semantic relationship of the set of terms of the natural language query. The systems and methods generate a graph query including graph terms corresponding to the set of terms of the natural language query defined by a graph database. The systems and methods search one or more datasets associated with the graph database using the graph query and return a set of results based on the graph query.

Abstract: In various example embodiments, a system and methods are presented for converting query structures for information retrieval from graph-based data structures. The systems and methods receive a natural language query including a set of terms and generate an intermediate semantic relationship of the set of terms of the natural language query. The systems and methods generate a graph query including graph terms corresponding to the set of terms of the natural language query defined by a graph database. The systems and methods search one or more datasets associated with the graph database using the graph query and return a set of results based on the graph query.

Abstract: In various example embodiments, a system and methods are presented for generation and manipulation of predictive models within a user interface. The system and methods receive a view query with object data and time data and generate a user interface having a first graphical representation of a set of historical data responsive to the view query. The systems and methods generate a predictive model based on the set of historical data and generate a second graphical representation for the predictive model. The systems and methods generate and monitor a movable pivot element to automatically modify the predictive model and second graphical representation upon a change in position of the pivot element.

Abstract: Systems, methods, and computer program embodiments are disclosed for detecting software components in a software codebase. In an embodiment, a source file containing source code may be received, and a code signature may be generated for the source file based on a determined structure of the source code. The generated code signature may then be compared to signatures stored in a reference database to identify matching software files. In an embodiment, the reference database may store a plurality of code signatures corresponding to software files. A list of the identified software files may be created and presented to a user.

Abstract: Systems, methods, and computer program embodiments are disclosed for detecting third party software components in a software codebase. In an embodiment, a source file containing source code may be received at a server, and a code signature may be generated for the source file based on a determined structure of the source code. The generated code signature may then be compared to signatures stored in a reference database to identify matching third party software files. In an embodiment, the reference database may store a plurality of code signatures corresponding to third party software files. A list of the identified third party software files may be created and presented to a user.