A chatbot server that manages context information between a chatbot and a user device includes a receiving unit that receives, from a messenger server, a question message input for a service selected from multiple services by the user device and service account information corresponding to the selected service; a derivation unit that derives an answer to the question message by using the chatbot corresponding to the service account information; and a context information management unit that manages context information of a chat between the user device and the chatbot based on the question message and/or the answer.

A method, computer system, and a computer program product for identifying and storing at least one representation to at least one chemical compound is provided. The present invention may include identifying a chemical compound associated with a source data. The present invention may also include assigning a structure representation to the identified chemical compound associated with the source data. The present invention may further include computing an unformulated representation based on the assigned structure representation. The present invention may then include indexing the computed unformulated representation and the assigned structure representation. The present invention may further include storing the indexed unformulated representation and the indexed structure representation separately as single records in a database.

A query-focused faceted structure generation method, system, and computer program product for generating a query-focused faceted structure from a taxonomy for searching a document corpus, including augmenting taxonomy types with new instances where the instances comprise entities within a proximity of existing instances of taxonomy types in a local embedding of entities parsed from the document corpus, ranking each instance in the augmented taxonomy with respect to its type as a function of both a distance from an instance to a query in a global embedding vector space of the entities trained from the document corpus and a distance of an instance to a type in the local embedding, and ranking the taxonomy types using expanded instances in the document corpus for each type.

Example data processing systems and methods are described. In one implementation, a system accesses a corpus of data and analyzes the data contained in the corpus of data to identify multiple documents. The system generates vector indexes for the multiple documents such that the vector indexes allow a computing system to quickly access the plurality of documents and identify an answer to a question associated with the corpus of data.

In some embodiments, the present disclosure provides for an exemplary computer-implemented system that may include a longitudinal data engine, including: a processor and specialized index generation software to generate: an index data structure for a respective event type associated with each respective subject or object; where each respective index data structure is a respective event type-specific data schema, defining how to store events of a particular event type to form longitudinal data of each respective subject or object; an ontology data structure that is configured to describe one or more properties of a respective event of a respective subject or object; and longitudinal data extraction software to extract a respective longitudinal data for a plurality of index data structures and a plurality of ontology data structures associated with a plurality of subjects or objects.

A method of performing auto-naming of content includes: receiving an auto-naming command for the content; performing auto-naming of the content by using different parameters according to different content types to obtain at least one auto-naming result for the content; and displaying the auto-naming result.

Disclosed herein are systems and methods for implementing a RELATED command with a predictive query interface including means for generating indices from a dataset of columns and rows, the indices representing probabilistic relationships between the rows and the columns of the dataset; storing the indices within a database of a host organization; exposing the database of the host organization via a request interface; receiving, at the request interface, a query for the database specifying a RELATED command term and a specified column as a parameter for the RELATED command term; querying the database using the RELATED command term and passing the specified column to generate a predictive record set; and returning the predictive record set responsive to the query, the predictive record set having a plurality of elements therein, each of the returned elements including a column identifier and a confidence indicator for the specified column passed with the RELATED command term, wherein the confidence indicator indicates whether a latent relationship exists between the specified column passed with the RELATED command and the column identifier returned for the respective element. Other related embodiments are further disclosed.

A scheduler manages execution of a plurality of data-collection jobs, assigns individual jobs to specific forwarders in a set of forwarders, and generates and transmits tokens (e.g., pairs of data-collection tasks and target sources) to assigned forwarders. The forwarder uses the tokens, along with stored information applicable across jobs, to collect data from the target source and forward it onto an indexer for processing. For example, the indexer can then break a data stream into discrete events, extract a timestamp from each event and index (e.g., store) the event based on the timestamp. The scheduler can monitor forwarders' job performance, such that it can use the performance to influence subsequent job assignments. Thus, data-collection jobs can be efficiently assigned to and executed by a group of forwarders, where the group can potentially be diverse and dynamic in size.

In an approach for semantically translating data. Aspects of an embodiment of the present invention include an approach for semantically translating data, wherein the approach includes a processor selecting a first node. A processor identifies a parent node of the first node. A processor determines that a value of the first node is unknown. A processor responsive to determining that the value of the first node is unknown, annotates the first node to indicate that the first node is at least partially unknown. A processor identifies a common table expression of the first node. A processor determines that the common table expression of the first node matches, within a predetermined threshold, a common table expression of the second node. A processor merges information from the common table expression of the second node with the common table expression of the first node.

Optimizations are provided for distinguishing between webpages that are cached and webpages that have been or currently are displayed on a user interface. In some instances, a list of webpages is generated in response to a query entered by a user. Then, a determined number of webpages that were included in the list are cached in memory. These cached webpages each have an associated agent string, and at least some of these agent strings are updated to reflect a cached status. Subsequently, a first webpage is displayed on a user interface. This first webpage was included among those webpages that were cached. Further, the agent string for this webpage is updated to reflect an in-view status. In response to the first webpage being replaced by a second webpage, the agent string for the second webpage is then updated to reflect the in-view status.