Abstract: In an approach to generating and using a sentence model for answer generation, one or more computer processors ingest a first corpus of a plurality of text sentences. One or more computer processors convert the plurality of text sentences into a plurality of sentence vectors. One or more computer processors group the plurality of sentence vectors into a plurality of sentence clusters, wherein a sentence cluster is composed of sentences that are semantically similar. One or more computer processors receive a second corpus. One or more computer processors determine, for each sentence cluster of the plurality of sentence clusters, a frequency each sentence cluster appears in the second corpus. Based on the determined frequency, one or more computer processors calculate a probability of each sentence cluster of the plurality of sentence clusters. Based on the calculated probabilities, one or more computer processors generate a first sentence model.

Abstract: A method, system, and computer-usable medium are disclosed for identifying areas to improve an interactive conversational system, such as a chatbot. A stream of stream of conversational interactions C (C1, C2, . . . , Cn) between users and the interactive conversational system is received. An intent clustering model is periodically applied to the stream to form an incremental clustering based on a set of derived intents to form a mapping from a first set of conversational characteristics to a first set of intents and a second set of conversational characteristics to a first set of unclear intents. Information is provided related to the second set of conversation characteristics.

Abstract: An approach is provided that receives a question at a question-answering (QA) system. A number of passages are identified that are relevant to the received question. A question knowledge graph is generated that corresponds to the question and a set of passage knowledge graphs are also generated with each passage knowledge graph corresponding to one of the identified passages. Each of the passage knowledge graphs are compared to the question knowledge graph with the comparison resulting in a set of knowledge graph candidate answers (kgCAs). A set of candidate answers (CAs) is computed by the QA with at least one of the CAs being based on one of the kgCAs.

Abstract: Embodiments are directed to a system, a computer program product, and a method for identification of linguistically related elements, and more specifically to prediction of a linguistically related element. A linguistic algorithm forms a cluster representation of corpus entries. A linguistic term is identified and applied to the cluster representation to identify proximally related linguistic terms. Associative relationships between the proximally related terms and category metadata are iteratively investigated. One or more linguistic terms related across the two more metadata categories is identified and designated as the linguistically related element.

Abstract: In an approach to generating and using a sentence model for answer generation, one or more computer processors ingest a first corpus of a plurality of text sentences. One or more computer processors convert the plurality of text sentences into a plurality of sentence vectors. One or more computer processors group the plurality of sentence vectors into a plurality of sentence clusters, wherein a sentence cluster is composed of sentences that are semantically similar. One or more computer processors receive a second corpus. One or more computer processors determine, for each sentence cluster of the plurality of sentence clusters, a frequency each sentence cluster appears in the second corpus. Based on the determined frequency, one or more computer processors calculate a probability of each sentence cluster of the plurality of sentence clusters. Based on the calculated probabilities, one or more computer processors generate a first sentence model.

Abstract: A method, system, and computer-usable medium are disclosed for identifying areas to improve an interactive conversational system, such as a chatbot. A stream of stream of conversational interactions C (C1, C2, . . . , Cn) between users and the interactive conversational system is received. An intent clustering model is periodically applied to the stream to form an incremental clustering based on a set of derived intents to form a mapping from a first set of conversational characteristics to a first set of intents and a second set of conversational characteristics to a first set of unclear intents. Information is provided related to the second set of conversation characteristics.

Abstract: A computer-implemented method of executing a virtual agent bot includes receiving, via a computer server, at least one input query from a user, and analyzing the at least one input query to extract at least one input term. The method further comprises determining a semantic skill set of the virtual agent bot among a plurality of different candidate skill sets based at least in part on the at least one input term; and invoking the virtual agent bot to provide a semantic topic response corresponding to the semantic skill set to provide an answer to the at least one input query.

Abstract: A computer-implemented method of executing a virtual agent bot includes receiving, via a computer server, at least one input query from a user, and analyzing the at least one input query to extract at least one input term. The method further comprises determining a semantic skill set of the virtual agent bot among a plurality of different candidate skill sets based at least in part on the at least one input term; and invoking the virtual agent bot to provide a semantic topic response corresponding to the semantic skill set to provide an answer to the at least one input query.

Abstract: An approach is provided that selects an original entity from an original knowledge graph. The approach then accesses a data source that is external to the original knowledge graph, such as an online encyclopedia. An entity in the data source is identified based on the entity matching the original entity. A new relation is then identified in the data source between the identified entity and a new entity with the new entity being absent from the original knowledge graph. An expanded knowledge graph is then generated with the expanded knowledge graph formed by adding the new entity to the original knowledge graph.

Abstract: An approach is provided that receives a question at a question-answering (QA) system. A number of passages are identified that are relevant to the received question. A question knowledge graph is generated that corresponds to the question and a set of passage knowledge graphs are also generated with each passage knowledge graph corresponding to one of the identified passages. Each of the passage knowledge graphs are compared to the question knowledge graph with the comparison resulting in a set of knowledge graph candidate answers (kgCAs). A set of candidate answers (CAs) is computed by the QA with at least one of the CAs being based on one of the kgCAs.

Abstract: Embodiments are directed to a system, computer program product, and method for identification of linguistically related elements, and more specifically to prediction of a linguistically related element. A linguistic algorithm forms a cluster representation of corpus entries. A linguistic term is identified and application to the cluster representation to identified proximally related linguistic terms. Associative relationships between the proximally related terms and category metadata are iteratively investigated. One or more linguistic terms related across the two more metadata categories is identified and designated at the linguistically related element.

Abstract: The present invention provides a computer-implemented method, system and program product for emulating a topology of web services. Specifically, under the present invention a set (e.g., one or more, preferably a plurality) of general purpose web services are deployed. Each general purpose web service includes a central processing unit emulator and a common interface. Moreover, each general purpose web service is capable of executing instructions for retrieving data and interacting with other general purpose web services. The topology of web services is then emulated using the set of general purpose web services by executing the instructions. Once the topology is emulated, it can be tested for accuracy, performance characteristics, etc.

Abstract: The invention provides a method, system, and program product for orchestrating web services. In one embodiment, the method includes accessing a plurality of web services, each web service including an orchestration engine; and employing a program including an instruction for work executed by at least one of the plurality of web services.