International Business Machines Corporation

Rebecca Lynn Dahlman

Mario J Lorenzo

Jennifer Lynn La Rocca

Kristin E McNeil

Debra L Angst

Joshua M Lee

Scott Robert Carrier

Aspects of the present disclosure include determining, by a processor, an ontology, the ontology comprising a plurality of ontological relationships, receiving, by the processor, a plurality of passages, determining, by the processor, a target set of co-occurring entities comprising a first entity and a second entity, determining a first passage in the plurality of passages that includes the first entity and the second entity, determining, from the ontology, a first ontological relationship between the first entity and the second entity, analyzing the first passage to determine a congruency score for the first ontological relationship, and generating a relationship annotation between the first entity and the second entity in the first passages based on the congruency score being within a threshold.

Semantic linkage qualification of ontologically related entities

Techniques for natural language processing based on user context include identifying a context of a user and responsive to receiving a request from the user intended for processing by a natural language processing (NLP) model, accounting for the context of the user in relation to the request. A result from the NLP model having accounted for the context of the user is provided.

Natural language processing based on user context

Embodiments include methods, system and computer program products for extracting attributes from embedded table structures in a document. Aspects include identifying a table in the document and identifying one or more headers of the table by locating co-occurring attributes in the table. Aspects also include identifying a plurality of values in the table and creating an annotation for each of the plurality of values value in the table, wherein each annotation includes text extracted from the one or more headers that correspond to the location of the value in the table.

Extracting attributes from embedded table structures

According to a computer-implemented method, an output is received resulting from natural language processing of unstructured text. In the output different phrases of the unstructured text are categorized into classes. A different visual distinction is generated for at least one class. A generated visual distinction is applied to phrases in the unstructured text based on their respective class. The unstructured text is displayed such that phrases in the unstructured text that correspond to the at least one class appear with the corresponding visual distinction indicative of that class.

Text display with visual distinctions per class

According to a computer-implemented method, a cognitive model container is created. The container includes a set of artifacts. Each artifact includes 1) content used by a cognitive service to convert unstructured text into structured text and 2) metadata. During deployment of a container, for each artifact a set of deployment descriptors are automatically identified. The deployment descriptors identify how the artifact is to be executed in the cognitive service. Also, during deployment of the container, content of an artifact is pushed to a number of cognitive services based on the deployment descriptors. The container is instantiated along with the set of artifacts to the cognitive service. During runtime execution of the container, content of each artifact in a container is obtained and unstructured text is converted into structured text based on the content of the artifacts.

Cognitive service updates via container instantiation

An artifact identification engine identifies artifacts from structured and unstructured data in one or more documents based on pre-defined artifacts, by using cognitive annotations. The identified artifacts are analyzed based at least on received inputs. A cartridge that includes artifacts that are relevant to the structured and unstructured data is generated, based on the analyzing.

Growing community of inventors

Rochester, MN, United States of America

Rebecca Lynn Dahlman