International Business Machines Corporation

Wright State University

Meenakshi Nagarajan

Cartic Ramakrishnan

Daniel Gruhl

Alfredo Alba

Clemens Drews

Neal Ryan Lewis

Pablo N Mendes

Linda Ha Kato

William Scott Spangler

Sheng Hua Bao

Christian Benjamin Kau

Richard L Martin

Olivier Lichtarge

Angela D Wilkins

David Martinez Iraola

Lawrence A Donehower

Benjamin J Bachman

Sam J Regenbogen

Curtis R Pickering

Trudy L Hewitt

Kelley Anders

Rashmi Gangadharaiah

Jacques Labrie

Dan G Tecuci

Stacy M Cannon

Ying Chen

Michael D Pfeifer

Kenneth Lee Clarkson

Ioana Roxana Stanoi

Shenghua Bao

Karthik Rajagopal Gomadam

Ronald Fagin

John Christian Unterholzner

Alix M Lacoste

Pathirage D Perera

Daniel Thornton Pierce

Amit Sheth

Nakul Chakrapani

Anbu Karani Adikesavan

Maria E Terron-Diaz

At least some embodiments are directed to a computer-implemented method that comprises receiving original input text that includes a term, comparing a definition of the term to definitions of multiple candidate replacement terms to generate a set of candidate replacement terms, and substituting each of the candidate replacement terms in the set for the term in the original input text to produce a plurality of modified input texts. The method also comprises determining the grammatical accuracy of each of the plurality of modified input texts, comparing meanings of the modified input texts to a meaning of the original input text, and modifying the set of candidate replacement terms based on the determinations of grammatical accuracy and the comparisons of the meanings. The method still further comprises ranking the modified set of candidate replacement terms using one or more criteria, and displaying the ranking on a display.

Identifying linguistic replacements to improve textual message effectiveness

According to embodiments of the present invention, methods, systems and computer readable media are provided, in a cognitive data processing system, for implementing a predictive analytics system that utilizes entity and non-entity information. A collection of content is processed to extract defined entities pertaining to one or more domains. Semantic relationships are determined between objects within the collection of content, wherein the objects include undefined entities. The defined entities and objects are resolved based on entity definitions and the semantic relationships to determine defined entities and undefined entities for a resulting data set. The resulting data set is processed to identify one or more relationships between a defined entity and an undefined entity.

Combining semantic relationship information with entities and non-entities for predictive analytics in a cognitive system

Utilizing a computing device to assist in repurposing of a pharmaceutical. An identification of a pharmaceutical for repurposing study is received by a computing device. A pharmaceutical expression signature is retrieved based upon the identification of the pharmaceutical, the pharmaceutical expression signature indicating differential expressions of a plurality of biomolecules regulated by the pharmaceutical. A plurality of disease expression signatures are retrieved from a disease omics database, each disease expression signature indicating differential expressions of a plurality of biomolecules affected by a disease. A pharmaceutical vector is generated based upon the pharmaceutical expression signature for the pharmaceutical. A plurality of disease vectors are generated based upon the plurality of disease expression signatures for each disease. N hypotheses correlating the pharmaceutical vector and one or more of the plurality of disease vectors are generated, each hypothesis indicating a potential repurposing for the pharmaceutical to treat the disease.

Accelerated pharmaceutical repurposing by finding anticorrelations and by text mining

A method, computer system, and a computer program product for determining the reliability of a claim is provided. The present invention may include receiving an input data from a user. The present invention may also include analyzing the claim associated with the received input data to determine a reliability score associated with the input data, wherein the claim is semantically similar to the received input data. The present invention may further include generating, from a prediction model, the reliability score for the claim associated with the received input data. The present invention may also include presenting the reliability score for the claim associated with the received input data to the user.

Automatic claim reliability scorer based on extraction and evidence analysis

Disclosed aspects relate to analytics engine selection management. A set of reference data may be ingested by a first analytics engine to compile a first set of characteristic data. The set of reference data may be ingested by a second analytics engine to compile a second set of characteristic data. The first set of characteristic data may be compiled for the first analytics engine. The second set of characteristic data may be compiled for the second analytics engine. A set of distinct attributes related to the first and second analytics engines may be determined based on the first and second sets of characteristic data. An analytics engine selection operation may be executed.

Analytics engine selection management

One embodiment provides generating a similarity matrix corresponding to an input collection including initializing, by a processor, a working set as a collection of a multiple items. Until the similarity matrix converges: receiving a seed for similarity for at least one pair of items of the multiple items, and obtaining a similarity value for all other item pairs using a Naive Triangle Inequality process. The similarity is generated with obtained similarity values.

Efficient semi-supervised concept organization accelerated via an inequality process

Systems and methods for dynamic filtering of content posted to a social network are disclosed. In aspects, a method of the system includes generating, by a computing device, a user profile list for a user of a social network, the user profile list including data regarding the user's preferences with respect to undesirable content; monitoring, by the computing device, a content post on the social network to determine if the content post includes the undesirable content, the content post being displayed to the user through a social network interface; determining, by the computing device, that the content post includes the undesirable content; and effecting a change in the display of the content post in accordance with one or more predetermined display rules based on the determining that the content post includes the undesirable content.

Dynamic filtering of posted content

A method, a computing system and a computer program product are provided. A computing system identifies elements within a collection of medical documents. The elements include patients, adverse events and medical drugs. The medical documents are analyzed by the computer system to determine associations between the identified medical drugs and corresponding identified adverse events. The identified elements and the determined associations may be encoded as features by the computing system. The computing system identifies portions of the medical documents as containing the identified elements and the determined associations. The computing system generates a classification model based at least on the encoded features associated with the identified portions for identifying medical case safety reports within medical documents. The classification model is applied to a new document to determine a classification of the new document with respect to a medical case safety report.

Cognitive framework to identify medical case safety reports in free form text

A method, system and computer program product for disambiguating meaning of terms. A natural language processing system obtains a first corpora of words used in a first sense and a second corpora of the same words that are used in a second sense. Each of these corpora of words may be associated with different linguistic domains. The natural language processing system generates a first and a second set of patterns using both the first and second corpora of words, respectively. A question passage is then received by the natural language processing system. The natural language processing system examines a word of interest in the question passage. The user is then notified that the word of interest is being used in the first sense or the second sense in response to identifying the word of interest in only the first set of patterns or the second set of patterns, respectively.

Disambiguation of the meaning of terms based on context pattern detection

A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement an automated lexicon expansion for an identified corpus. For a selected corpus in a set of corpora, the mechanism determines an estimated number of new terms in the selected corpus that are not in the lexicon based on a frequency count known terms in the selected corpus. Responsive to the estimated number of new terms in the selected corpus being greater than a threshold, the mechanism performs lexicon expansion using the selected corpus to form an expanded lexicon. Responsive to the estimated number of new terms in the selected corpus not being greater than the threshold, the mechanism halts lexicon expansion.

Automatic corpus selection and halting condition detection for semantic asset expansion

Performing an operation comprising extracting, from an input comprising unstructured electronic text, a plurality of feature values for a plurality of features defined in a feature vector, identifying, based on a machine learning (ML) model applied to the plurality of feature values, a portion of the electronic text corresponding to an adverse event, and annotating the portion of the electronic text with an indication of the identified adverse event.

Cognitive framework to detect adverse events in free-form text

Influencers (individuals or groups) over a selected audience (observers or recipients of information, objects and/or events) on a given topic are measured based on influence features, which include a sentiment flipping influence feature indicative of ability of an audience member to influence other audience members to change their sentiment on the selected topic. Other influence features include the ability to influence others: to change followership; to express interest in a topic associated with a hashtag pioneered by the influencer, based on the effectiveness and phrasing of language used. The output of the influence engine can be a score representing the relative influence of audience members over the audience on the topic of interest. Influencers may be ranked according to their total influence score over the audience on the topic.

Measuring the influence of entities over an audience on a topic

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San Jose, CA, United States of America

Meenakshi Nagarajan