Abstract: Mechanisms for automatically modifying a set of instructions based on an expanded domain specific knowledge base is provided. The mechanisms generate a domain specific knowledge base comprising a set of entities and corresponding domain specific attributes and expand the domain specific knowledge base to include values for the domain specific attributes through an automated bootstrap learning process that performs natural language processing and analysis of natural language content using a set of pre-condition annotated action terms. The mechanisms receive electronic content comprising an initial set of instructions to perform an operation and evaluate the initial set of instructions based on the expanded domain specific knowledge base to identify a missing instruction. The mechanisms modify the initial set of instructions to include an additional instruction based on the missing instruction and thereby generate a modified set of instructions.

Abstract: 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.

Abstract: An object-extraction method includes generating multiple partition objects based on an electronic document, and receiving a first user selection of a data element via a user interface of a compute device. In response to the first user selection, and using a machine learning model, a first subset of partition objects from the multiple partition objects is detected and displayed via the user interface. A user interaction, via the user interface, with one of the partition objects is detected, and in response, a weight of the machine learning model is modified, to produce a modified machine learning model. A second user selection of the data element is received via the user interface, and in response and using the modified machine learning model, a second subset of partition objects from the multiple partition objects is detected and displayed via the user interface, the second subset different from the first subset.

Abstract: An object-extraction method includes generating multiple partition objects based on an electronic document, and receiving a first user selection of a data element via a user interface of a compute device. In response to the first user selection, and using a machine learning model, a first subset of partition objects from the multiple partition objects is detected and displayed via the user interface. A user interaction, via the user interface, with one of the partition objects is detected, and in response, a weight of the machine learning model is modified, to produce a modified machine learning model. A second user selection of the data element is received via the user interface, and in response and using the modified machine learning model, a second subset of partition objects from the multiple partition objects is detected and displayed via the user interface, the second subset different from the first subset.

Abstract: Mechanisms for training a human user to perform an operation and provided. The mechanisms generate a domain specific knowledge base comprising a set of entities and corresponding domain specific attributes and expand the domain specific knowledge base to include values for the domain specific attributes through an automated bootstrap learning process that performs natural language processing and analysis of natural language content using a set of pre-condition annotated action terms, thereby generating an expanded domain specific knowledge base. The mechanisms evaluate an input from another device identifying an action associated with an entity in the set of entities, based on a retrieved domain specific attribute value and the retrieved pre-condition annotation from the expanded domain specific knowledge base. The mechanisms output a notification to a user computing device indicating whether the input is correct or incorrect to thereby train a user associated with the user computing device.

Abstract: An object-extraction method includes generating multiple partition objects based on an electronic document, and receiving a first user selection of a data element via a user interface of a compute device. In response to the first user selection, and using a machine learning model, a first subset of partition objects from the multiple partition objects is detected and displayed via the user interface. A user interaction, via the user interface, with one of the partition objects is detected, and in response, a weight of the machine learning model is modified, to produce a modified machine learning model. A second user selection of the data element is received via the user interface, and in response and using the modified machine learning model, a second subset of partition objects from the multiple partition objects is detected and displayed via the user interface, the second subset different from the first subset.

Abstract: Mechanisms for automatically modifying a set of instructions based on an expanded domain specific knowledge base are provided. The mechanisms generate a domain specific knowledge base comprising a set of entities and corresponding domain specific attributes and expand the domain specific knowledge base to include values for the domain specific attributes through an automated bootstrap learning process that performs natural language processing and analysis of natural language content using a set of pre-condition annotated action terms. The mechanisms receive electronic content comprising an initial set of instructions to perform an operation and evaluate the initial set of instructions based on the expanded domain specific knowledge base to identify a missing instruction. The mechanisms modify the initial set of instructions to include an additional instruction based on the missing instruction and thereby generate a modified set of instructions.

Abstract: Mechanisms for automatically modifying a set of instructions based on an expanded domain specific knowledge base is provided. The mechanisms generate a domain specific knowledge base comprising a set of entities and corresponding domain specific attributes and expand the domain specific knowledge base to include values for the domain specific attributes through an automated bootstrap learning process that performs natural language processing and analysis of natural language content using a set of pre-condition annotated action terms. The mechanisms receive electronic content comprising an initial set of instructions to perform an operation and evaluate the initial set of instructions based on the expanded domain specific knowledge base to identify a missing instruction. The mechanisms modify the initial set of instructions to include an additional instruction based on the missing instruction and thereby generate a modified set of instructions.

Abstract: An object-extraction method includes generating multiple partition objects based on an electronic document, and receiving a first user selection of a data element via a user interface of a compute device. In response to the first user selection, and using a machine learning model, a first subset of partition objects from the multiple partition objects is detected and displayed via the user interface. A user interaction, via the user interface, with one of the partition objects is detected, and in response, a weight of the machine learning model is modified, to produce a modified machine learning model. A second user selection of the data element is received via the user interface, and in response and using the modified machine learning model, a second subset of partition objects from the multiple partition objects is detected and displayed via the user interface, the second subset different from the first subset.

Abstract: Ground truth for a cognitive system is generated from a structured resource such as a table by identifying a subject of the structured resource and field headers. Linguistic analysis is performed on a given header to establish an interrogative context, and a question is generated relating to the subject based on the interrogative context, including an implementation of one or more mathematical operators. The question is generated using a question template, and has a question phrase based on the interrogative context, an operator phrase based on the selected operator, and a keyword phrase based on the subject. An answer to the question is determined by carrying out a computation that applies the selected operator(s) to one or more of the data values, to form a question-and-answer pair that is added to the ground truth. A filtering step is preferably used to ensure that the question-and-answer pair is valid.

Abstract: Mechanisms for training a human user to perform an operation and provided. The mechanisms generate a domain specific knowledge base comprising a set of entities and corresponding domain specific attributes and expand the domain specific knowledge base to include values for the domain specific attributes through an automated bootstrap learning process that performs natural language processing and analysis of natural language content using a set of pre-condition annotated action terms, thereby generating an expanded domain specific knowledge base. The mechanisms evaluate an input from another device identifying an action associated with an entity in the set of entities, based on a retrieved domain specific attribute value and the retrieved pre-condition annotation from the expanded domain specific knowledge base. The mechanisms output a notification to a user computing device indicating whether the input is correct or incorrect to thereby train a user associated with the user computing device.

Abstract: Ground truth for a cognitive system is generated from a structured resource such as a table by identifying a subject of the structured resource and field headers. Linguistic analysis is performed on a given header to establish an interrogative context, and a question is generated relating to the subject based on the interrogative context, including an implementation of one or more mathematical operators. The question is generated using a question template, and has a question phrase based on the interrogative context, an operator phrase based on the selected operator, and a keyword phrase based on the subject. An answer to the question is determined by carrying out a computation that applies the selected operator(s) to one or more of the data values, to form a question-and-answer pair that is added to the ground truth. A filtering step is preferably used to ensure that the question-and-answer pair is valid.

Abstract: 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.

Abstract: Mechanisms for training a human user to perform an operation and provided. The mechanisms generate a domain specific knowledge base comprising a set of entities and corresponding domain specific attributes and expand the domain specific knowledge base to include values for the domain specific attributes through an automated bootstrap learning process that performs natural language processing and analysis of natural language content using a set of pre-condition annotated action terms, thereby generating an expanded domain specific knowledge base. The mechanisms evaluate an input from another device identifying an action associated with an entity in the set of entities, based on a retrieved domain specific attribute value and the retrieved pre-condition annotation from the expanded domain specific knowledge base. The mechanisms output a notification to a user computing device indicating whether the input is correct or incorrect to thereby train a user associated with the user computing device.

Abstract: 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.

Abstract: 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.

Abstract: Mechanisms for training a human user to perform an operation and provided. The mechanisms generate a domain specific knowledge base comprising a set of entities and corresponding domain specific attributes and expand the domain specific knowledge base to include values for the domain specific attributes through an automated bootstrap learning process that performs natural language processing and analysis of natural language content using a set of pre-condition annotated action terms, thereby generating an expanded domain specific knowledge base. The mechanisms evaluate an input from another device identifying an action associated with an entity in the set of entities, based on a retrieved domain specific attribute value and the retrieved pre-condition annotation from the expanded domain specific knowledge base. The mechanisms output a notification to a user computing device indicating whether the input is correct or incorrect to thereby train a user associated with the user computing device.

Abstract: Mechanisms for automatically modifying a set of instructions based on an expanded domain specific knowledge base is provided. The mechanisms generate a domain specific knowledge base comprising a set of entities and corresponding domain specific attributes and expand the domain specific knowledge base to include values for the domain specific attributes through an automated bootstrap learning process that performs natural language processing and analysis of natural language content using a set of pre-condition annotated action terms. The mechanisms receive electronic content comprising an initial set of instructions to perform an operation and evaluate the initial set of instructions based on the expanded domain specific knowledge base to identify a missing instruction. The mechanisms modify the initial set of instructions to include an additional instruction based on the missing instruction and thereby generate a modified set of instructions.

Abstract: Mechanisms for bootstrapping knowledge acquisition from a limited knowledge domain are presented. Natural language content is received and a primary and secondary portion of natural language content are identified within the natural language content. The secondary portion of natural language content is analyzed to identify indications of meaning directed to elements of the primary portion of natural language content. Features related to the secondary portion of the natural language content indicate meaning directed to the primary portion of the natural language content. A collection of domain knowledge is generated from an analysis of the primary and secondary portions of the natural language content and stored to provide meaningful responses to requests.

Abstract: A method for retrieving information spread across a plurality of different ontologies, including: defining a meta-ontology, wherein the meta-ontology includes high-level properties and their mappings to specific properties defined in a plurality of different ontologies; receiving a question, wherein the question is associated with a high-level property; and providing an answer to the question, wherein the answer is determined by using the meta-ontology.