Abstract: A recommendation engine for works of literature uses patterns of flow and element similarities for scoring a first user-rated work of literature against one or more recommendation candidate works of literature. Cluster models are created using meta-data modeling the works of literature, the meta-data having literary element categories and instances within each category. Each instance is described by an index value (position in the literature) and significance value (e.g. weight or significance). Cluster finding process(es) invoked for each instance in each category find Similarity Concept clusters and Consistency Trend clusters, which are recorded into the cluster models representing each work of literature. The cluster model can be printed or displayed so that a user can visually understand the ebb and flow of each literary element in the literature, and may be digitally compared to other cluster models of other works of literature for potential recommendation to a user.

Abstract: Validating belief states of an artificial intelligence system includes providing a question answering service; detecting a negative sentiment of a user to an answer transmitted to a device associated with the user; and responsive to detecting the negative sentiment, detecting that the answer relates to a topic on which there is controversy. Next, a new belief state is added to the question answering service based on the controversy, and an updated answer is transmitted to the device, wherein the updated answer is based on the new belief state.

Abstract: A recommendation engine for works of literature uses patterns of flow and element similarities for scoring a first user-rated work of literature against one or more recommendation candidate works of literature. Cluster models are created using meta-data modeling the works of literature, the meta-data having literary element categories and instances within each category. Each instance is described by an index value (position in the literature) and significance value (e.g. weight or significance). Cluster finding process(es) invoked for each instance in each category find Similarity Concept clusters and Consistency Trend clusters, which are recorded into the cluster models representing each work of literature. The cluster model can be printed or displayed so that a user can visually understand the ebb and flow of each literary element in the literature, and may be digitally compared to other cluster models of other works of literature for potential recommendation to a user.

Abstract: Deep semantic analysis is performed on an electronic literary work in order to detect plot elements and optional other storyline elements such as characters within the work. Multiple levels of abstract are generated into a model representing the literary work, wherein each element in each abstraction level may be independently rated for preference by a user. Through comparison of multiple abstraction models and one or more user rating preferences, one or more alternative literary works may be automatically recommended to the user.

Abstract: A visualization of literary elements of a work of literature, such as a novel or short story, is generated from meta-data records representing a digital work of literature including literary elements (humor, drama, adventure, etc.), characters, and plot devices related to a position within the work of literature where each appears. A significance level is determined for each of the elements, characters and plot devices at each position within the work of literature, and these are plotted into a sequential graph having position (e.g. timeline) axis and a significance level axis. The sequential graph is then output for printing or display. Human-generated and machine-generated meta-data may be ingested equally well by the method. Colors, line thickness, and a broken line patterns may be employed for greater visual meaning. And, the sequential graph may be annotated according to segments (e.g. chapters, sections) and dominant genre within each segment.

Abstract: Methods and apparatus, including computer program products, implementing and using techniques for identifying candidate answer gaps within a corpus of a question and answer system. An original question posed to the question and answer system is analyzed to identify an object and a semantic type for the question. Concepts having a same or similar semantic type are retrieved from an ontology or dictionary. For at least one retrieved concept, one or more altered questions are created by replacing the object of the original question with a preferred term of the retrieved concept. The one or more altered questions are submitted to the question and answer system. The answers to the altered questions are analyzed to identify gaps within the corpus of the question and answer system.

Abstract: Automatic semantic analysis for characterizing and correlating literary elements within a digital work of literature is accomplished by employing natural language processing and deep semantic analysis of text to create annotations for the literary elements found in a segment or in the entirety of the literature, a weight to each literary element and its associated annotations, wherein the weight indicates an importance or relevance of a literary element to at least the segment of the work of literature; correlating and matching the literary elements to each other to establish one or more interrelationships; and producing an overall weight for the correlated matches.

Abstract: The present invention includes a computing device that may receive a business problem in a natural language. The computing device may determine a domain classification from the business problem, where the domain classification is a list of domains determined from an application programming interface (API) catalog. The computing device may generate a problem graph from the business problem, where the problem graph is a parsed tree of natural language elements extracted from the natural language and stored as a database. The computing device may retrieve one or more assets from the plurality of assets based on the domain classification and the problem graph. The computing device may generate a problem-solution graph from the one or more assets and generate a solution API pipeline graph for evaluation by a user and compilation by a pipeline assembler.

Abstract: According to an embodiment of the present invention, a system filters a collection of application programming interfaces based on input data representing information of a document to be processed, and generates a pipeline of filtered application programming interfaces. Each filtered application programming interface is sequentially executed within the pipeline. The document may be processed through the pipeline and visualizations based on the processing of the document may be generated, allowing the pipeline to be refined to produce desired output data selected within the visualizations. Embodiments of the present invention further include a method and computer program product for generating a pipeline of filtered application programming interfaces in substantially the same manner described above.

Abstract: According to an embodiment of the present invention, a system filters a collection of application programming interfaces based on input data representing information of a document to be processed, and generates a pipeline of filtered application programming interfaces. Each filtered application programming interface is sequentially executed within the pipeline. The document may be processed through the pipeline and visualizations based on the processing of the document may be generated, allowing the pipeline to be refined to produce desired output data selected within the visualizations. Embodiments of the present invention further include a method and computer program product for generating a pipeline of filtered application programming interfaces in substantially the same manner described above.

Abstract: Embodiments of the present invention disclose a method, a computer program product, and a computer system for identifying information gaps in corpora. A computer receives a document and extracts keywords from the document while filtering trivial keywords. The computer identifies and extracts top keywords detailed by the document using a topic modelling approach before determining whether the extracted top keywords exceed a threshold use frequency. Based on determining that the top keywords exceed a threshold use frequency, determining whether the top keywords have a relation to other entities within the document and, if so, determining whether the top keywords are defined within the document. Based on determining that the top keywords are not defined in the document, adding the top keywords to a list and defining the top keywords.

Abstract: Methods and apparatus, including computer program products, implementing and using techniques for identifying candidate answer gaps within a corpus of a question and answer system. An original question posed to the question and answer system is analyzed to identify an object and a semantic type for the question. Concepts having a same or similar semantic type are retrieved from an ontology or dictionary. For at least one retrieved concept, one or more altered questions are created by replacing the object of the original question with a preferred term of the retrieved concept. The one or more altered questions are submitted to the question and answer system. The answers to the altered questions are analyzed to identify gaps within the corpus of the question and answer system.

Abstract: Deep semantic analysis is performed on an electronic literary work in order to detect plot elements and optional other storyline elements such as characters within the work. Multiple levels of abstract are generated into a model representing the literary work, wherein each element in each abstraction level may be independently rated for preference by a user. Through comparison of multiple abstraction models and one or more user rating preferences, one or more alternative literary works may be automatically recommended to the user.

Abstract: The present invention includes a computing device that may receive a business problem in a natural language. The computing device may determine a domain classification from the business problem, where the domain classification is a list of domains determined from an application programming interface (API) catalog. The computing device may generate a problem graph from the business problem, where the problem graph is a parsed tree of natural language elements extracted from the natural language and stored as a database. The computing device may retrieve one or more assets from the plurality of assets based on the domain classification and the problem graph. The computing device may generate a problem-solution graph from the one or more assets and generate a solution API pipeline graph for evaluation by a user and compilation by a pipeline assembler.

Abstract: Mechanisms are provided for resolving annotation conflicts in a portion of content. A portion of content is received and annotation operations are performed on it using a plurality of annotators. Each annotator in the plurality of annotators generates a set of annotations for the portion of content, thereby generating a plurality of sets of annotations for the portion of content. The plurality of sets of annotations are analyzed to identify a conflict between a first annotation and a second annotation in the plurality of sets of annotations. The conflict between the first annotation and the second annotation is resolved to generate a resultant set of annotations for the portion of content. The resultant set of annotations is stored in an annotations data structure associated with the portion of content.

Abstract: Mechanisms are provided in which a first knowledge graph, comprising nodes representing entities and edges between nodes indicative of a relationship between the entities, is received. The mechanisms identify a candidate missing edge connecting a node of the first knowledge graph to another node not present in the first knowledge graph and evaluate the candidate missing edge to determine if the candidate missing edge should be added to the first knowledge graph. The mechanisms expand the first knowledge graph to include the candidate missing edge connecting the node to a newly added node that is newly added to the first knowledge graph, to thereby generate an expanded knowledge graph, in response to the evaluation indicating that the candidate missing edge should be added to the first knowledge graph. The mechanisms then perform an operation on the expanded knowledge graph to generate a knowledge output.

Abstract: Deep semantic analysis is performed on an electronic literary work in order to detect plot elements and optional other storyline elements such as characters within the work. Multiple levels of abstract are generated into a model representing the literary work, wherein each element in each abstraction level may be independently rated for preference by a user. Through comparison of multiple abstraction models and one or more user rating preferences, one or more alternative literary works may be automatically recommended to the user.

Abstract: According to an embodiment of the present invention, a system filters a collection of application programming interfaces based on input data representing information of a document to be processed, and generates a pipeline of filtered application programming interfaces. Each filtered application programming interface is sequentially executed within the pipeline. The document may be processed through the pipeline and visualizations based on the processing of the document may be generated, allowing the pipeline to be refined to produce desired output data selected within the visualizations. Embodiments of the present invention further include a method and computer program product for generating a pipeline of filtered application programming interfaces in substantially the same manner described above.

Abstract: Embodiments of the present invention disclose a method, a computer program product, and a computer system for providing heuristic answers to a question that cannot be answered with sufficient confidence. A computer receives a question and the computer identifies one or more answers to the question. In addition, the computer determines that a confidence level corresponding to the one or more answers does not exceed a threshold and, based on determining that the confidence level corresponding to the one or more answers does not exceed the threshold, the computer identifies a primary concept of the question. Moreover, the computer identifies one or more related concepts to the primary concept and reformulates the received question by replacing the primary concept with the one or more related concepts. Lastly, the computer identifies and presents to a user one or more reformulated answers to the reformulated question.

Abstract: Embodiments of the present invention disclose a method, a computer program product, and a computer system for extracting natural language relations between entities. A computer receives a configuration for associating one or more natural language questions with one or more entities and identifies the one or more entities annotated within a document. The computer answers the natural language questions associated with the identified one or more entities based on context surrounding the identified one or more entities. The computer may further transmit the natural language questions associated with the identified one or more entities and the surrounding context to a question and answer service, then receive answers to the natural language questions from the question and answer service. The computer may further determine whether the received answers correctly describe the relation between the identified one or more entities and other entities within the extracted surrounding context.