International Business Machines Corporation

Christopher Michael Madison

Joseph N Kozhaya

Corville O Allen

Sridhar Sudarsan

Sai Prathyusha Peddi

Ryan R Anderson

Phaedra Boinodiris

John D Wolpert

Anita Govindjee

Joseph Max Kaufman

Mechanisms are provided for implementing a virtual corpus engine that receives an inquiry to be processed and analyzes the inquiry to extract one or more features of the inquiry. The virtual corpus engine selects a weight matrix associated with a virtual corpus based on the extracted one or more features of the inquiry. The virtual corpus comprises a plurality of actual corpora of information. The weight matrix comprises a separate weight value for each actual corpus in the plurality of actual corpora. The virtual corpus engine processes the inquiry using a set of selected actual corpora selected from the plurality of actual corpora based on the weight values in the weight matrix and receives results of the processing of the inquiry using the set of selected actual corpora. The virtual corpus engine outputs the results of the processing of the inquiry.

Cognitive system virtual corpus training and utilization

A first seed concept term may be identified. The first seed concept term may be to train a cognitive computing system. The cognitive computing system may analyze the first seed concept term to generate a first set one or more concept terms that are candidates for being conceptually related to the first seed concept term. A first plurality of individual characters and the first seed concept term may be provided. A first user of a client computing device may be prompted to generate a second set of one or more concept terms that are conceptually related to the first seed concept term using one or more of the first plurality of individual characters.

Game-based training for cognitive computing systems

Described herein is a system and method for addition of content to a question answer (QA) system. The method may comprise retrieving, from a template store of the QA system, a template comprising one or more data fields and one or more metadata fields. The method may further comprise populating a passage field with passage information, wherein the passage field is one of the one or more data fields. The method may further comprise populating a training field with training information, wherein the training field is one of the one or more metadata fields. The method may further comprise ingesting, into a corpus of the QA system, a completed template comprising a populated passage field and a populated training field into the corpus.

Dynamic templates for incremental addition of specialized content into a cognitive question answer system

A first list that includes a first set of one or more concept terms may be obtained. The first set of one or more concept terms may be candidates for being conceptually related to a seed concept term. Prior to the obtaining, a first client computing device may provide a first plurality of individual characters and the seed concept term to a first user using the first client computing device. The first client computing device may prompt the first user to generate the first set of one or more concept terms that are conceptually related to the seed concept term using one or more of the first plurality of individual characters. In response to the obtaining, one or more particular categories to which each of the first set of one or more concept terms belongs to may be determined.

Categorizing concept terms for game-based training in cognitive computing systems

According to one exemplary embodiment, a method for dynamically defining topics from content objects used to answer a question in a question answering system is provided. The method may include receiving the content objects. The method may include identifying the topics within the received content objects. The method may include generating a graph based on the identified plurality of topics, whereby nodes map to the topics. The method may include calculating edge weights associated with each edge based on semantic relations associated with the topics. The method may include determining if the calculated edge weight associated an edge exceeds a threshold value. The method may include generating a combination topic based on determining that the edge weight associated with the edge exceeds the threshold value.

Defining dynamic topic structures for topic oriented question answer systems

In an approach to generating blockchain smart contracts, one or more computer processors receive a request for a service from a user. The one or more computer processors extract one or more features from the request. The one or more computer processors determine one or more smart contract templates associated with the request based, at least in part, on the extracted one or more features. The one or more computer processors receive one or more responses to the request from one or more service providers. The one or more computer processors generate a draft smart contract based, at least in part on the determined one or more smart contract templates and the one or more received responses.

Cognitive mediator for generating blockchain smart contracts

According to one exemplary embodiment, a method for generating an answer in a question answering system is provided. The method may include receiving a question. The method may also include identifying a candidate answer from a corpus. The method may then include determining a plurality of sentences based on the identified candidate answer. The method may further include calculating a similarity value for each sentence within the plurality of sentences based on comparing the plurality of sentences to the candidate answer and the received question. The method may also include identifying at least one sentence within the plurality of sentences with a calculated similarity value that exceeds a threshold value. The method may then include generating the answer based on the candidate answer and the identified at least one sentence.

Generating answers from concept-based representation of a topic oriented pipeline

According to one exemplary embodiment, a method for generating an answer in a question answering system is provided. The method may include receiving a question. The method may also include identifying a candidate answer from a corpus. The method may then include determining a plurality of sentences based on the identified candidate answer. The method may further include calculating a similarity value for each sentence within the plurality of sentences based on comparing the plurality of sentences to the candidate answer and the received question. The method may also include identifying at least one sentence within the plurality of sentences with a calculated similarity value that exceeds a threshold value. The method may then include presenting the answer, whereby the answer comprises the plurality of sentences, the candidate answer, and metadata.

Presenting answers from concept-based representation of a topic oriented pipeline

A method for controlled sharing, in a cognitive computing system having a memory and a processor, of content from a variety of content providers may include receiving, by the cognitive computing system, an inquiry, wherein the inquiry is directed to at least one content topic. The method may then include identifying, by the cognitive computing system, the at least one content topic, and determining that a cognitive component of the cognitive computing system managed by a content provider has access to a content repository addressing the at least one content topic. The method may further include distributing, by the cognitive computing system, the inquiry to the cognitive component. The method may additionally include receiving a response from the cognitive component.

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Charlotte, NC, United States of America

Christopher Michael Madison