Abstract: A system and a method of generating and using a multimodal knowledge representation within an artificial intelligence computing system may include prompting a selection of nodes in a graphical visualization; defining an adaptation strategy based; integrating an adaptation strategy representation with the selection of nodes to generate a multimodal knowledge representation; traversing the selection of nodes in response to a user query; performing an adaptation on the selection of nodes; and updating the knowledge representation based on user feedback to a result generated in response to the user query.

Abstract: In a first aspect of the invention, there is a computer-implemented method including: generate, by a processor set, an interaction usage graph based on user interaction data on user interactions with an analytical system user interface; generate, by the processor set, an interaction embedding model in a vector space based on the interaction usage graph; determine, by the processor set and based on the interaction embedding model in the vector space, a similarity of a portion of the interaction embedding model that corresponds to a particular analytical task among the user interactions with the analytical system user interface with a particular machine learning model from a set of one or more machine learning models; and output, by the processor set, to the analytical system user interface, an indication of the particular machine learning model.

Abstract: Machine teaching in an embodiment can include receiving a user annotated concept for a given image in given context. The given image can be broken into parts and classified. Relationships can be determined associated with the parts. The created relationships can be stored along with the user annotated concept in a knowledge base. One or more similar images can be annotated using the parts and relationships. A second image associated the given context can be retrieved, decomposed into parts. The parts can be classified. Relationships can be determined associated with the second image's parts. Classifications and relationships associated with the second image's parts can be compared with classifications and relationships associated with the given image's parts. Based on comparing, the second image can be annotated with the user annotated concept for the given image.

Abstract: A computer-implemented method, a computer system and a computer program product coordinates knowledge from visual elements in a collaboration session. The method includes obtaining collaboration session data. The collaboration session data includes a plurality of visual elements and a plurality of participant interactions. The method also includes identifying a subject and a link in the plurality of visual elements using a text recognition algorithm. In addition, the method includes applying a weight to the link based on the plurality of participant interactions and a distinct characteristic in the plurality of visual elements. Lastly, the method includes generating a knowledge graph. The nodes of the graph comprise the subjects and the edges of the graph comprise weighted links.

Abstract: A method, computer program, and computer system are provided for selecting an artificial intelligence algorithm. Data corresponding to a user interaction with an artificial intelligence system is received. A need by the user for an explanation associated with the artificial intelligence system is identified based on the received data corresponding to the user interaction with the artificial intelligence system. An artificial intelligence algorithm is selected from among a plurality of artificial intelligence algorithms. The selected artificial intelligence algorithm includes an explainable artificial intelligence component. A model is created for the the selected artificial intelligence algorithm corresponding to the received data.

Abstract: A hybrid knowledge representation is searched for a machine learning component corresponding to a search query. The hybrid knowledge representation may be structured as nodes representing machine learning workflow components and edges (e.g., links) connecting the nodes based on relationships between the nodes. Responsive to finding the machine learning component in the hybrid knowledge representation, the machine learning component is returned. Responsive to not finding the machine learning component in the hybrid knowledge representation, the hybrid knowledge representation is searched for machine learning model fragments associated with building the machine learning component, generating a new machine learning component by combining the machine learning model fragments and returning the new machine learning component.

Abstract: Embodiments of the invention are directed to a computer-implemented method of generating a pathway recommendation. The computer-implemented method includes using a processor system to generate an intermediate three-dimensional (3D) virtual reality (VR) environment of a target environment. A machine learning algorithm is used to perform a machine learning task on the intermediate 3D VR environment to generate machine learning task results including predicted features of interest (FOI) and FOI annotations for the intermediate 3D VR environment. The processor system is used to generate, based at least in part on the machine learning task results, the pathway recommendation configured to assist a user with navigating and interpreting a 3D VR environment including the intermediate 3D VR environment having the pathway recommendation.

Abstract: Document retrieval through assertion analysis on entities and document fragments is disclosed. A document is received. Logical structures and entities are extracted from the document by parsing the document. For an entity in the extracted entities, an object representing the entity is created, an assertion made in the document associated with the entity is determined, and the assertion is linked to the object representing the entity. A logical structure from the extracted logical structures and content of the logical structure containing the assertion are identified and linked to the object representing the entity.

Abstract: A method and system of creating a knowledge graph includes capturing information of a user interacting with given data, as user interaction data. The user interaction data is structured as a trail of actions over time. An ontology for a domain related to the user interaction data is received. Each action of the trail of actions is matched onto entities of the ontology. The knowledge graph is created based on the ontology having the matched actions.

Abstract: A seismic dataset and a task to be performed with the seismic dataset may be received. A representative seismic line representative of the seismic dataset may be generated. The representative seismic line may include pixel data representative of the seismic dataset. Based on the representative seismic line, the task may be performed. The task may include at least finding an analogous geological region by searching for an analogous seismic dataset existing in a seismic database by comparing the representative seismic line with the analogous seismic dataset's representative seismic line.

Abstract: Storing and retrieving subsurface rock physical property models for well drilling by generating estimated subsurface rock physical property models based on a respective set of seismic data, determining a stratigraphic classification for each model based on seismic interpretation data; storing the models associated classification in a database, determining characteristics of the seismic data used to generate the models, selecting from the database a subset of the models based on a similarity of the stratigraphic classification, determining measures of similarity between the characteristics of the seismic data used to generate each of subset of the models and a new set of seismic data and retrieving an estimated subsurface rock physical property model from the subset models based on the similarity measures.

Abstract: Machine teaching in an embodiment can include receiving a user annotated concept for a given image in given context. The given image can be broken into parts and classified. Relationships can be determined associated with the parts. The created relationships can be stored along with the user annotated concept in a knowledge base. One or more similar images can be annotated using the parts and relationships. A second image associated the given context can be retrieved, decomposed into parts. The parts can be classified. Relationships can be determined associated with the second image's parts. Classifications and relationships associated with the second image's parts can be compared with classifications and relationships associated with the given image's parts. Based on comparing, the second image can be annotated with the user annotated concept for the given image.

Abstract: Method, apparatus, and computer program product are provided for retrieving analogues using topological knowledge representation (TKR). In some embodiments, a TKR input query is built and/or validated using a domain-specific knowledge base (KB). A search database containing candidate analogues and corresponding pre-built TKRs is then searched to retrieve at least one analogue of the TKR input query using statistical analysis. In some embodiments, a system may build the TKR input query based on a seismic dataset. For example, the system may receive a seismic dataset, segment the seismic dataset and classify each region using a computer vision (CV) database and the KB, and build the TKR input query based on the segmented and classified seismic dataset. In some embodiments, the TKR input query may be input and/or edited by a user. For example, the TKR input query may be input and/or edited by the user and validated using the KB.

Abstract: Embodiments of the invention are directed to a computer-implemented method of generating a pathway recommendation. The computer-implemented method includes using a processor system to generate an intermediate three-dimensional (3D) virtual reality (VR) environment of a target environment. A machine learning algorithm is used to perform a machine learning task on the intermediate 3D VR environment to generate machine learning task results including predicted features of interest (FOI) and FOI annotations for the intermediate 3D VR environment. The processor system is used to generate, based at least in part on the machine learning task results, the pathway recommendation configured to assist a user with navigating and interpreting a 3D VR environment including the intermediate 3D VR environment having the pathway recommendation.

Abstract: A system, method and program product for evaluating a prospect. The method includes: receiving prospect information regarding a prospect for natural resource exploration; extracting a set of characteristics based on the prospect information; identifying a set of similar prospects and a set of prospect exploration results, based on the set of characteristics; receiving a success estimate for the prospect; validating the success estimate based on the set of characteristics, the set of similar prospects, and the set of prospect exploration results to determine inconsistencies in the success estimate; and approving the success estimate or receiving an updated success estimate, based on the inconsistencies in the success estimate.

Abstract: A system, product, and method including automatically performing extraction of slides from multimodal content, performing object extraction from each of the slides, allowing object substitution through semantics and concepts of the objects extracted, processing audio synchronized with the slides enriched with cognitive computing, search engine, and knowledge base, to provide annotations of the slides, processing the audio synchronized with the object being presented in each slide to enhance semantics and understanding, and curating for each step with human-machine interaction to provide a learning process by the system.

Abstract: A document may be received. An ontology associated with a domain may be identified or retrieved. A set of concepts in the document may be extracted, the set represented in the ontology. A set of concepts selected from the ontology may be received. A search may be performed based on the set of selected concepts.

Abstract: A system and associated processes may generate new modalities by receiving content associated with an original modality. An appropriateness of an alternative modality may be determined based on at least one of a device available to present the content, a user profile, and a contextual factor regarding an environment of a user intended to receive the content. Source content semantics associated with the content may be automatically determined, and an alternative modality based on the determined source content semantics may be generated and used to present the content to the user.

Abstract: Method and apparatus for monitoring collaboration parameters and maintaining user profiles using cognitive computing techniques are provided. The method includes receiving a domain for a first collaborative session, and retrieving a plurality of trait profiles associated with potential participants. The method further includes determining a subset of trait profiles based in part on comparing each respective trait profile with the rest of the trait profiles, and providing the subset of trait profiles. Additionally, the method includes receiving an indication that the subset of trait profiles is acceptable, and a request to initiate the first collaborative session with participants corresponding to the subset of trait profiles.

Abstract: A system and a method of generating and using a multimodal knowledge representation within an artificial intelligence computing system may include prompting a selection of nodes in a graphical visualization; defining an adaptation strategy based; integrating an adaptation strategy representation with the selection of nodes to generate a multimodal knowledge representation; traversing the selection of nodes in response to a user query; performing an adaptation on the selection of nodes; and updating the knowledge representation based on user feedback to a result generated in response to the user query.