Abstract: A generator categorizes documents in one or more transactions into buckets, each identified by a separate category for an expected time window based on a separate relative age of each of the documents evaluated from one or more dates identified in the documents. The generator trains a document classifier with a model of the separate relative age of each of the documents as a temporal characteristic correlated with the respective category of a respective bucket of the buckets. The document classifier executes on a input documents to classify each of the input documents as a particular logical type identified by a particular category from among multiple logical types.

Abstract: Intent-based exploring of self-documenting Application Programming Interface (“API”) documents comprises receiving a document containing a plurality of descriptions of APIs, wherein the document is in a structured format; generating a plurality of entities by identifying a plurality of API definition entries in the document; generating a plurality of intents by iterating through a plurality of path entries in the document; generating a dialog by creating a plurality of phrases by iterating through the plurality of path entries in the document; receiving one or more questions from a user or developer; and in response to the one or more questions, providing one or more conversational responses to the user by a conversational application program incorporating the plurality of entities, plurality of intents, and plurality of phrases.

Abstract: Intent-based exploring of self-documenting Application Programming Interface (“API”) documents comprises receiving a document containing a plurality of descriptions of APIs, wherein the document is in a structured format; generating a plurality of entities by identifying a plurality of API definition entries in the document; generating a plurality of intents by iterating through a plurality of path entries in the document; generating a dialog by creating a plurality of phrases by iterating through the plurality of path entries in the document; receiving one or more questions from a user or developer; and in response to the one or more questions, providing one or more conversational responses to the user by a conversational application program incorporating the plurality of entities, plurality of intents, and plurality of phrases.

Abstract: Mechanisms are provided for processing sources of content in a corpus of information associated with a domain. The mechanisms ingest a plurality of sources of content in the corpus of information associated with the domain and determine at least one temporal category for the domain based on identification of frequently occurring references to at least one of temporal terms or time spans specified in the plurality of sources of content in the corpus of information associated with the domain. The mechanisms store the at least one temporal category for the domain in association with the domain and process queries applied to the plurality of sources of content in the corpus of information associated with the domain based on the stored at least one temporal category for the domain.

Abstract: A method, system, and computer program product provide navigation guidance around various object types in a vicinity of a guidance assistance device for a user by analyzing received data regarding an environment around the guidance assistance device to identify one or more entities Ei and by applying an artificial intelligence machine learning analysis to group the one or more entities Ei into corresponding categories Cj and to determine a minimum spacing distance Dmin for each of the one or more entities Ei, wherein the minimum spacing distance Dmin is a minimum distance between the guidance assistance device and the entity Ei based on the categorization Ci specified by the user, and then providing feedback to the user when any of the one or more entities Ei is within than minimum spacing distance Dmin corresponding to said entity.

Abstract: A classifier receives a digital scan of a document and converts the content of the document in the digital scan from an image into text. The classifier analyzes the text to determine one or more predicted roles of one or more signatories, each predicted role determined based on one or more signature elements in the content of the document executed by the one or more signatories. The classifier evaluates each of the one or more predicted roles in view of a plurality of expected signatory role characteristics of a plurality of categories of documents of a transaction to select a particular category associated with the document from among the plurality of categories. The classifier classifies the document within the transaction as a particular logical type identified by the particular category from among a plurality of logical types for the transaction.

Abstract: A method, computer system, and a computer program product for relevancy-based document quality assessment is provided. The present invention may include computing a document quality score based on at least one container relevancy score determined based on at least one domain link to a domain knowledge base.

Abstract: A classifier receives a document from a multi-document transaction. The classifier analyzes the document to identify one or more embedded dates in the content of the document and context of one or more positions of the one or more embedded dates in the document. The classifier evaluates each of the one or more embedded dates based on the separate context of each of the one or more positions within the document and a relative age of the one or more embedded dates in view of temporal characteristics of multiple categories of documents of a transaction to select a particular category associated with the document from among the multiple categories. The classifier classifies the document within the transaction as a particular logical type identified by the particular category from among multiple logical types.

Abstract: Improved data ingestion techniques are provided. A data set comprising records is received, where each record contains one or more fields. A group of fields is identified, where each of the fields has a common metadata attribute. Metrics are determined for the group based on metadata associated with each field, and weight values are assigned to each of the metrics. A natural language processing (NLP) measure and a discreteness measure are generated for the group of fields based on the metrics and the weight values. A processing workflow is selected to use when ingesting data from the group of fields into a corpus, based on comparing the NLP measure and the discreteness measure to one or more predefined thresholds, and each of the fields in the group of fields are processed using the processing workflow.

Abstract: A classifier receives a document and analyzes the document to determine one or more predicted roles of one or more signatories, each predicted role determined based on one or more signature elements in the content of the document executed by the one or more signatories. The classifier evaluates each of the one or more predicted roles in view of a plurality of expected signatory role characteristics of a plurality of categories of documents of a transaction to select a particular category associated with the document from among the plurality of categories. The classifier classifies the document within the transaction as a particular logical type identified by the particular category from among a plurality of logical types for the transaction.

Abstract: An embodiment of the invention may include a method, computer program product, and system for generating ground truth data for a plurality of cognitive capabilities within an overall cognitive system. The embodiment may include configuring multiple sets of training data. Each set of training data corresponds to a separate cognitive capability. The embodiment may include displaying a set of ground truth curation activities via a user interface. The embodiment may include determining the ground truth curation activities performed for a first type of data for a first duration. The first type of data is selected from the single set of grouped training data. The embodiment may include determining whether the first duration has exceeded a pre-determined threshold. The embodiment may include switching the curation activities to a second type of data. The second type of data is selected from the single set of grouped data.

Abstract: A method, computer system, and a computer program product for detecting one or more semantic headers in one or more tabular structures by utilizing a custom pre-trained embeddings model is provided. The present invention may include receiving the custom pre-trained embeddings model. The present invention may also include computing one or more dot product values associated with the one or more tabular structures from the one or more documents based on the context of each cell associated with the one or more tabular structures in the one or more documents. The present invention may then include generating one or more similarity feature values based on the computed one or more dot product values. The present invention may further include detecting the one or more semantic headers associated with the one or more tabular structures based on the one or more similarity feature values.

Abstract: Based on natural language processing of a passage of a first electronic document, a determination is made that the passage indicates an action to be undertaken. In response to this determination and based on an analysis of a second electronic document, the second electronic document is identified as incorporating content preconfigured to allow completion of the action. In response to this identification, the passage is linked to the second electronic document.

Abstract: Embodiments relate to an intelligent computer platform to support natural language (NL) processing. The request is analyzed and a lexical answer type (LAT) related to the received request is identified. A knowledge graph (KG) related to the LAT is identified and leveraged to extract a first concept related to the LAT and a second concept related to the first concept. First and second clusters are created, with the first cluster having the LAT and first concept as qualifiers, and the second cluster having the first and second concepts as qualifiers. Each of the formed clusters is populated with one or more documents. An inter-cluster assessment is conducted based on the relevancy of the populated document(s) to the received input. In addition, a machine learning model (MLM) corresponding to the KG is identified and utilized to selectively augment the MLM with the LAT, first and second concepts, and a corresponding relationship to the inter-cluster assessment.

Abstract: Embodiments relate to an intelligent computer platform to identify a lexical answer type (LAT), a first concept relevant to the received request and a second concept related to the identified first concept. The LAT, together with the first and second concepts are utilized to create a first and second cluster. Documents are selectively populated into the clusters. The clusters are subject to sorting based on a relevancy protocol.

Abstract: Embodiments relate to an intelligent computer platform to construct ground truth data. A document is subjected to NLP and grammatical elements of the document are identified. The identified grammatical elements are annotated and one or more annotations are assigned to the elements, with each annotation having corresponding meta-data. The assigned annotations are assessed for accuracy and a score associated with the meta-data is adjusted. The annotations are selectively re-assigned with respect to the identified grammatical elements responsive to the adjusted score. Ground truth data is constructed and the selectively re-assigned annotations are assigned to corresponding and identified grammatical elements.

Abstract: Embodiments relate to an intelligent computer platform to construct ground truth data. Annotations are extracted from one or more data elements and characteristic data associated with the extracted annotations is dynamically identified. One or more models are created with the identified characteristic data. Domain activity impact is measured the accuracy of the annotation data is calculated. A policy is generated from the calculated accuracy, wherein the policy includes assigning a score to the annotation data as metadata. Compliance activities are orchestrated based on the generated policy. One or more annotations are selectively re-assigned to the data in compliance with the orchestration, and ground truth data is created from the selective re-assignment.

Abstract: A set of documents is received for processing and extraction. A set of processing engines is received, and each processing engine has an expected benefit when processing a document with associated document metadata. The set of documents is analyzed to determine document metadata to be associated with the document. An expected benefit is determined for each of the documents of the set of document when the respective document is processed by a respective processing engine in the set processing engines. An expected cost for processing is determined for each of the documents in each of the set of processing engines. A processing plan for the set of documents is determined wherein the processing plan identifies a selection of documents to be run in respective processing engines of the set of processing engines based on a cost versus benefit analysis. The processing plan is executed extracting information from the identified selection of documents.

Abstract: Embodiments relate to an intelligent computer platform to receive a request for processing against a corpus. The request is analyzed and a lexical answer type (LAT), a first concept relevant to the received request and a second concept related to the identified first concept, are each identified. The LAT, together with the first and second concepts are utilized to create a first cluster and a second cluster. Documents are selectively populated into the clusters based on the respective LAT and concept qualifiers. The clusters are subject to sorting based on relevancy to the received request.

Abstract: Embodiments include a method, system and computer program product for detecting impersonation attempts in social media messaging. Aspects include receiving, via a social media network, a message from a sender to a recipient and analyzing a content of the message to extract factual statements from the message. Aspects also include analyzing a profile of the recipient to extract facts from the profile and comparing each of the factual statements to the facts from the profile. Based on a determination that one of the factual statements are verifiable by at least one of the facts, aspects include assigning a likelihood score to the factual statements. Aspects further include calculating a legitimacy score for the message based at least in part on the likelihood score of each verified factual statement from the message and transmitting the legitimacy score and the message to the recipient.